Title: incCoda
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00100848/00001
 Material Information
Title: incCoda incremental hoarding and reintegration in mobile environments
Physical Description: Book
Language: English
Creator: Khushraj, Abhinav, 1977-
Publisher: University of Florida
Place of Publication: Gainesville Fla
Gainesville, Fla
Publication Date: 2001
Copyright Date: 2001
Subject: Mobile communication systems   ( lcsh )
Computer and Information Science and Engineering thesis, M.S   ( lcsh )
Dissertations, Academic -- Computer and Information Science and Engineering -- UF   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
Summary: ABSTRACT: Disconnection is one of the popular techniques for operating in mobile environments and is here to stay for sometime until long-range wireless connectivity becomes a reality. However, disconnection requires periodic hoarding and reintegration of data, which raises performance issues especially during weak connection. A common hoarding and reintegration mechanism involves complete transfer of contents. In order to hoard and reintegrate efficiently, an incremental approach is being introduced to do data transfers based on the delta between changes. Data objects are differentially transferred in either direction -- to hoard from the server to the client, and to reintegrate changes made while disconnected from the client to the server -- and are patched on the receiving side to generate full copies. QuickConnect and MoneyConnect are two important and useful features that are also being introduced to allow mobile users to control the amount of connection time and money spent during hoarding and reintegration. Performance evaluation of this system proves that it does efficient incremental hoarding and is beneficial on any type of connection. Analysis also shows that incremental reintegration is particularly beneficial in the weakly connected mode of operation. The incremental hoarding and reintegration setup is built within the Coda File System of the Carnegie Mellon University to replace the full file transfer mechanism with the incremental approach, based on the Revision Control System (RCS).
Summary: KEYWORDS: disconnection, CODA, hoarding, reintegration, weak connectivity
Thesis: Thesis (M.S.)--University of Florida, 2001.
Bibliography: Includes bibliographical references (p. 49-50).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
Statement of Responsibility: by Abhinav Khushraj.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains viii, 51 p.; also contains graphics.
General Note: Vita.
 Record Information
Bibliographic ID: UF00100848
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 49052910
alephbibnum - 002766265
notis - ANP4304


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Copyright 2001


Abhinav Khushraj

To my Mom.


I express my sincere gratitude to Dr. Helal for all the encouragement, guidance

and above all the motivation that he has constantly provided. He has been of real help

providing me with the right direction for this thesis work and writing. Without him the

thesis would not have been possible.

I also thank Dr. Hammer and Dr. Su for being on my thesis committee. I would

also like to thank the people at Harris Lab for their guidance and support during this


I specially thank Jinsuo for helping me out at many points during this thesis

especially with incremental hoarding and its evaluation.

I especially thank my friend Sovrin for the fruitful discussions that we had on this

topic and for the constant support he provided. I thank Amar, Raja and Subodh for their

great company.



A C K N O W L E D G M E N T S .................................................................................................. iii

LIST OF FIGURES .................................................. .......................vi

A B STR A CT................ ...... ........... .. ........................... ............. vii


1 INTRODUCTION .................... .................... ....... .............

1.1 Significance of the P problem ......................................... ....................................... 1
1.2 Goal and Approach ......................................... ................ .... ............ 2
1.3 O organization of the Thesis ...................................................................... .............. 4

2 REVIEW OF RELATED WORK .............................................................................6

2.1 Update Notification/Propagation and Reconciliation in Ficus ............................... 6
2.2 Disconnected Operation in IntelliMirror ........................................................... 7
2 .3 T he InterM ezzo F ile Sy stem .......................................................................... .... 8
2.4 SEER : Predictive File H oarding ........................................ ........................... 9
2.5 Support for Hoarding with Association Rules ............................................. .. 10
2.6 Operation-based Update Propagation ....................................... ............. 11

3 CODA AND RCS ....................................................... .......... .......... .... 15

3.1 Types of C onnectivity ................................................. .. .. .. .. .......... .. 15
3 .2 C o d a ....................................................................... 1 7
3.2 .1 C lient-Serv er M odel............................................................................................ 17
3.2.2 Structure of C oda Client .............. ......................................................... 19
3.2.3 V enus States ...................... .. ......... ..... ................................. 20
3.2.4 Handling W eak-Connectivity in Coda.................... ............................ .... 22
3.2.5 Implementation of Specific Tools for Coda............................................... 23
3.3 R vision C control System ...................... .. .. ......... ....................... .............. 24

4 INCREMENTAL HOARDING AND REINTEGRATION ............... ..................... 26

4.1 Motivation for the Incremental Approach...... ................................ 26
4 .2 Increm mental H boarding ...................................... ................................................. 2 8

4.3 Incremental Reintegration............. ....... ................. ..... .............. 32
4.4 Reintegration Control with Time and Money................................... .......... 35
4.4.1 Reintegration Controlled with Time ......................................................... 35
4.4.2 Reintegration Controlled with Money .......................................................... 36

5 PERFORMANCE EVALUATION OF INCCODA..............................................38

5.1 Perform ance of Increm mental H oarding................................................ ................. 38
5.2 Performance of Incremental Reintegration........................................... ......... 40
5.3 Heuristics for Reintegrating PINE Email Folders ............................................... 42
5.4 Storage Overhead ..................... ... ........................ ...... . ............ .. 44

6 CONCLUSION AND FUTURE W ORK ........................................... .....................46

6.1 Achievem ents of this Thesis ........... ............. ............... ... .............. .. 46
6.2 Future Work ............................................ 47

L IST O F R E FE R E N C E S ......................................................................... ....................49

B IO G R A PH IC A L SK E T C H ..................................................................... ..................51


Figure Page

2.1: An Overview of Operation Shipping ............................................................................. 12

3.1: Structure of a C oda C lient ........................................................................ ........ ........... 19

3.2 : V enus States and T transitions ..................................................................... ..................20

3.3: CM L During Trickle Reintegration............................. ......................... ............... 23

4.1: Regular Full-file Transfer between Coda Server and Client .............. ........ ............... 29

4.2: Modified Vice Supporting Incremental Approach..................... .........................29

4.3: Modified Venus Supporting Incremental Approach................................. ...... ...............29

5.1: Payload Comparison Between Incremental Hoarding and Original Hoarding ......................39

5.2: Network traffic for Linux Source and PINE Email Folders ...............................................42

5.3: Heuristics for Reintegrating Pine Folders ................................................. . ............. 44

5.4: Storage Overhead due to RCS V version Files ........................................ .....................45

Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science



Abhinav Khushraj

August 2001

Chairman: Dr. Abdelsalam Helal
Major Department: Computer and Information Science and Engineering

Disconnection is one of the popular techniques for operating in mobile

environments and is here to stay for sometime until long-range wireless connectivity

becomes a reality. However, disconnection requires periodic hoarding and reintegration

of data, which raises performance issues especially during weak connection. A common

hoarding and reintegration mechanism involves complete transfer of contents. In order to

hoard and reintegrate efficiently, an incremental approach is being introduced to do data

transfers based on the delta between changes. Data objects are differentially transferred in

either direction to hoard from the server to the client, and to reintegrate changes made

while disconnected from the client to the server and are patched on the receiving side to

generate full copies.

QuickConnect and MoneyConnect are two important and useful features that are

also being introduced to allow mobile users to control the amount of connection time and

money spent during hoarding and reintegration.

Performance evaluation of this system proves that it does efficient incremental

hoarding and is beneficial on any type of connection. Analysis also shows that

incremental reintegration is particularly beneficial in the weakly connected mode of


The incremental hoarding and reintegration setup is built within the Coda File

System of the Carnegie Mellon University to replace the full file transfer mechanism with

the incremental approach, based on the Revision Control System (RCS).


1.1 Significance of the Problem

In the present computing age users demand constant availability of data and

information which is typically stored on their workstations, corporate file servers, and

other external sources such as the WWW. With the increasing popularity and prevalence

of mobile computing, mobile users are demanding the same when only limited network

bandwidth is available, or even when network access is not available. Moreover, given

the growing popularity of portables, laptops and personal digital assistants (PDA), mobile

users are requiring access to the data regardless of the form-factor or rendering

capabilities of the mobile device they choose to use.

In this space, three broad challenges are imposed by mobility [HEL01]:

1. Any time, anywhere access to data, regardless of whether the user is
connected, weakly connected via a high latency, low bandwidth network,
or completely disconnected

2. Device-independent access to data, where the user is allowed to use and
switch among different portables and PDAs

3. Support for mobile access to heterogeneous data sources such as files
belonging to different file systems and/or resource managers.

This work tries to solve the first challenge access to data anywhere, anytime,

which is a step towards ubiquitous computing and facilitates management of data in

different connectivity modes strong, weak and no connection.

1.2 Goal and Approach

In today's computing world, networks provide very high performance and

bandwidth and are highly transparent. The network bandwidth is no longer the rate-

determining step for applications and systems. However, this argument holds true only

for the fixed and wired networks. To reach the same level of network performance and

efficiency over mobile networks is still a far-fetched reality and so we have to explore

ways to counter the challenges posed by mobility.

Mobile devices are in general resource poor and computationally starved when

compared to desktops and their counterparts. They have smaller memory and persistent

storage space. Their weight, power and size are much smaller and lesser [SAT93]. The

biggest challenge is, however, the network. Mobile elements have to operate under a very

broad range of network conditions. The connection may vary from full range 10mbps on

802.1 lb wireless LANs to 56kpbs modem connection from home to Obps in disconnected

mode while traveling. The connection may also be intermittent and users may want to

disconnect to save connection cost and power consumption.

To operate disconnected, two important mechanisms are required hoarding and

reintegration. Hoarding is the process of caching important and relevant user data onto

mobile devices for use while operating disconnected. While disconnected, the cache in

the mobile device might be updated due to changes made by the user and applications

running on the mobile device. Reintegration is the mechanism by which all these updates

are synchronized with the fixed network upon reconnection.

Users invariably operate under weak connection (56kbps ranges), for example -

At home when users want to work over a weekend they have to rely on a
slow modem connection that ranges from 28kbps to 56 kbps.

When high-flying executives want to collaborate from the airplane with
peers on the fixed network, they have to use the expensive phone connection
that provides very little bandwidth.

Mobile users on the 802.1 lb wireless LAN have very weak connection
when they are in the periphery of the coverage area. Moreover, there is
intermittence owing to their movement into and out of range of the LAN
access points.

Under weak connection, hoarding and reintegration is time consuming owing to

the minimal bandwidth available. Especially, hoarding and reintegrating large files over

slow networks may not be desirable in many situations. Another important aspect is that

users make very little changes while mobile. If there is a text file or a program file they

are working on, they update only few lines while being mobile.

The main goal of this work is to do incremental hoarding and reintegration by

exploiting the fact that users make minimal changes while they are mobile. In this

approach, we do differential transfer of data objects (files, databases, etc.) instead of

hoarding and reintegrating full data objects. To this end, we use a version control system

to compute and maintain object differentials.

We also provide two useful tools QuickConnect and MoneyConnect that will

aid the mobile user to make conscious decisions regarding the time and money spent for

reintegration. QuickConnect is a tool that gives the mobile user the ability to specify the

connection time for reintegration. MoneyConnect, in the same lines, lets the user specify

the amount of money he is willing to spend for reintegration. This is especially useful

when the cost model is based on packets transferred (e.g., iDen packet data or GPRS).

The entire work that has been done in this thesis is based on the Coda File System

of the School of Computer Science at the Carnegie Mellon University. Coda is a

distributed file system that has a user level cache on the client, called Venus that is used

to support disconnected operation. This client cache services all the file system calls from

the users and applications on the Coda namespace. Before disconnection, the cache files

are hoarded from the Coda server, Vice into the user cache. The operations that are done

on the cache during the disconnected period are logged persistently and are reintegrated

into the Vice upon reconnection. During hoarding and reintegration, files are transferred

in full from the server to the client and vice versa. When the network connection is

strong, this is not a particular problem. However, if these transfers were to happen under

weak connection, it would impose unnecessary load on the network, because we only

need to send those parts of the files that were changed while disconnected and do not

have to send complete files. This is the basis of incremental hoarding and reintegration

To be able to do incremental hoarding and reintegration we need to keep track of

the different versions of the files on both Vice and Venus. Revision Control System

(RCS) [TIC85] is a software tool originally developed by Walter. F. Tichy of Purdue

University that assists with this task of version control by automating the storing,

retrieval, logging and identification of revisions. We use RCS functionalities to maintain

the various file versions and to compute the file differences between changes.

1.3 Organization of the Thesis

Chapter 2 of the thesis discusses related work on how to operate disconnected and

how to optimize the use of weak connectivity. In Chapters 3, we set up the base for the

incremental concept by discussing differing connectivity levels, Coda's states and model

and Revision Control System (RCS). In Chapter 4 we will discuss in detail the how the

Coda File System has been redesigned to support incremental hoarding and reintegration.


We evaluate performance results of the incremental approach in Chapter 5. Chapter 6

concludes the thesis and suggests future work.


Significant work has been done in the area of disconnected operation and

managing connectivity. Researchers have long back identified the usefulness of the

disconnected model and have produced many interesting results and came up with

various prototypes such as Ficus and Coda. Hoarding and reintegration are two areas that

have aroused considerable interest in the research community. In the industry,

IntelliMirrorTM, built into Microsoft Windows 2000 operating system, supports

disconnected operation. In this chapter, we will discuss some interesting research and

industry efforts that have been carried out in disconnected operation, hoarding techniques

and in managing varying connectivity during hoarding and reintegration.

2.1 Update Notification/Propagation and Reconciliation in Ficus

The Ficus project at UCLA is a distributed file system that provides replication

facility with optimistic concurrency control. Ficus is a peer-to-peer system that allows

updates under network partition. The updates are done in a non-serializable fashion called

one-copy availability that allows updates for any copy of the data, not requiring a

minimum number of available copies [GUY90]. This replication technique is used to

provide high availability in an environment that has frequent communication

interruptions. Any file or directory in the file system may be replicated at any set in of

Ficus hosts.

Every replica in the file system has afile-identifier number identifying a logical

file represented by a physical set, and a replica-identifier that identifies that replica. Also

associated with each replica is a version vector that holds the updates history of the


Updates are applied first to a single physical replica. The update makes an entry

in the version cache for the current update. An update propagation daemon checks this

cache and periodically propagates updates. For regular files, the updates are done

atomically by creating a shadow copy first to allow the complete file to transfer. The

shadow copy then replaces the original version. Update propagation for directories is a

little more complicated and Ficus uses a directory reconciliation algorithm. A

reconciliation algorithm gets the new operations performed at a remote host and inserts or

deletes accordingly on the local copy.

As can be noted, the updates are using full file transfer causing unnecessary

burden on the network, especially when Ficus aims at servicing users over large and

wide-area networks.

2.2 Disconnected Operation in IntelliMirrorTM

IntelliMirrorTM [MIC99] is a powerful tool built in Microsoft Windows 2000

that provides change and configuration management. The three main features that it

provides are

User data management

Software installation and maintenance

User settings management

It provides a mechanism by which user data automatically follows the user

whether he is online, connected to the network or offline. IntelliMirror stores the selected

data in specified network locations that makes it appear local to users.

Data are placed in specific network locations. When users want to disconnect they

specify the option to make these folders available offline. A local copy of these files is

made and then the user can move with his mobile device continuing to work on the

offline folders.

When the user comes back to the network, IntelliMirror identifies if any changes

have been made and if so prompts the user asking him permission to synchronize. All the

folders on the network location are updated upon confirmation from the user. If a

particular file has been changed both on the network and the users local computer then

the user is prompted to resolve conflicts and is given the option to save the files as he


The "Make Available Offline" can be compared to hoarding and 'Synchronize' to

reintegration in our context. However, the point to be noted here is that issues about

network connection have not been addressed. IntelliMirror assumes that network

connection is never a problem.

2.3 The InterMezzo File System

InterMezzo is a distributed file system that is capable ofjoumaling updates and

versions made at the client while connected and disconnected. A client module in the

kernel intercepts the updates and the associated details and writes journal records about


When the client loses network connection either due to disconnection or a

network or server failure, InterMezzo client starts operating disconnected and makes

journal records for any updates that are done. Upon getting back connection all the

updates are forwarded from the server into the client for those changes that have been

done on the server while the client is disconnected. Following this, the client then

reintegrates all of the changes made while disconnected into the server [BRAa].

This system does not provide any feature to hoard before disconnection and so if

users happen to disconnect involuntarily there may be some portions of the client files

that may not be available while he is operating disconnected.

2.4 SEER: Predictive File Hoarding

To operate disconnected a good quality cache has to be hoarded into the client

that results in least number of cache-misses. Hand specification of the files to be hoarded

is not practically viable as it would require computer expertise and understanding of

which files have to be hoarded and so this mechanism would fail to reach out to common

users who want transparent mechanisms. An LRU scheme, though it is very appealing, is

not very effective as the cache miss penalty is heavy and very often causes all the on

going work to come to a stall if an important cache file is missing.

SEER is a system that predicts and hoards user cache based on semantic

information that it gathers from user file access pattern. It identifies files that are

naturally related such as a set of C program files, and incidentally related files like the

editor and the C compiler. It uses these relations to create semantic distances that are

used to ensure that the necessary files will be hoarded into the cache.

Semantic distance is a measure that quantifies the relationship between any two

files. The closer the relation between two files is, the smaller the semantic distance will

be. The semantic distance is based on measurements of individual file references. A file

reference is considered to be a high level operation, such as an open or status inquiry on a

particular file. In the SEER system semantic distance is computed on the basis of the

sequence of file accesses. It uses a simple heuristic that operates in constant time and

linear space, and still discovers useful reference relationships.

The SEER system is implemented with a small modification to the UNIX kernel

and a bunch of user-level processes. The kernel module logs system calls that are placed

in a trace buffer that are read by the observer process. It then passes the log information

to a correlator process that computes the various semantic distances and builds a

database of these relationships to be used by a cache manager. The cache manager runs

periodically and makes file clusters based on these semantic relationships. It then chooses

appropriate file clusters that are most likely to be needed on the mobile device and hoards

them into its cache [KUE94].

2.5 Support for Hoarding with Association Rules

This method uses data mining rules to identify the set of data items that are to be

hoarded into portable computers prior to disconnection. Data mining techniques can be

used to create associative rules based on the users access history. These rules would then

represent the users access pattern on the cache and can be used to determine what

contents need to be hoarded.

Client request history can be divided into sessions. In each session there is a

pattern of client's requests. Data mining techniques are used to find the patterns and

produce rules to build a rule base of associations.

Partitioning of the history into sessions is done in two ways. Theflat approach

extracts data irrespective of who requested particular data. The user-based approach

separates the client request history on the basis of the specific users who requested the

objects. A window-based approach called the gap is used to separate session boundaries.

When more than a threshold amount of time separates two consecutive requests they are

supposed to be part of different windows. In the flat-approach, however, windows are

separated by a fixed time period but in the user-based approach the gap-based approach is

used to separate client requests. With these sessions and times1 standard data mining

algorithms (like Apriori) are used to find the association rules.

Candidate sets and hoarding set are then obtained using the associative rules just

obtained. Candidate sets are those that are eligible for being hoarded into the client for

the next disconnected session and are identified using an inferencing mechanism. These

are distinguished by priorities assigned to them at the time of creating associative rules.

The hoarding set has those entire candidate sets identified on the basis of the priority that

can fit into the client's cache [SAYOO].

2.6 Operation-based Update Propagation

Operation-based update propagation as shown in Figure 2.1 is especially designed

for dealing with weak and flaky network connections. It efficiently transfers changes

made to large files over a weak connection. In this technique, file system changes are

1 Windows are mapped to sessions and data requests are mapped to items

collected above the file-system level and during weak connection are passed on to a

surrogate client that is strongly connected to the server. The surrogate then re-executes

the changes and then transfers over the changes to the server. Changes are sent directly to

the server from the client if the connection is strong or if the re-execution fails on the


This approach takes place in four distinct steps [LEE99]:

1. Logging of user operations: The user operations that are logged are high

level commands issued by users that can be intercepted, logged and

replayed later. In application-transparent logging, in which the

applications are non-interactive like compilers and linkers, the logging of

operations can be done without modifying the applications. The shell

executing the applications is modified such that it logs the user operations

by issuing two newly implemented ioctl commands VIOCBEGINOP

and VIOCENDOP at the beginning and end of an operation.

Application-aware logging requires modifications to the application and

has not been addressed in this work.

1. Logging of user operations 3. Re-execution of user operations
2. Ship operation log'
Client / Surrogate
compact ,..
Large files operation La(ge files 4. Validation of
re-execLtion anc
updated log re-generate shippinglofre-
generatdd files

weak network
strong network f Oerat

Figure 2.1: An Overview of Operation Shipping

2. .\l/nppigY of operation log: The reintegrator that is responsible for the

normal reintegration process is modified to support this system. It does a

UserOpPropagate RPC to transfer the packed operation log before trying

to reintegrate directly to the server using value-shipping. If the RPC fails

then the client continues to reintegrate in the regular fashion over the slow


3. Re-execution of operations: The surrogate client upon receiving the user

operations puts the corresponding volumes2 in the write-disconnected3

state and then it re-executes the operations by pawning a new user thread

called the re-executor. The operations are reintegrated at the surrogate

after they are validated. If at all any of the volumes fail the reintegration

process at any stage the updates are discarded and the failure of the RPC is

intimated to the requesting client. Those operations that execute on the

surrogate exactly as they would on the requesting client, the operation is

said to be repeating re-execution. The system tries to facilitate repeating

re-executions by trying to give every operation the same execution

environment (shell, environment-variable, files, etc.), but it does not

guarantee it.

4. Validation of re-execution: After re-executing the operation log, the re-

executor creates a re-execution log that captures all the mutations made by

2 In Coda, a volume is a collection of files forming a partial subtree of the Coda name

3 In write-disconnected state the files can be retrieved from the servers but any changes
made locally cannot be written to the servers.

it. To validate the re-execution, the original execution log of the client is

compared with the re-execution log. It assigns fingerprints to each log

entry and validates it if the fingerprints match in the two logs.

The contribution of operation-based propagation is noteworthy, as it particularly

addresses the problem of reintegration during weak connectivity that is at the heart of this

work. This system's performance tests prove that it efficiently uses the network traffic

and reduces the elapsed time. However, it is not transparent and requires user

intervention in many specific scenarios and would not be a feasible solution if the user

does not have enough expertise.


This chapter discusses how varying connectivity affects portable devices, what

Coda is and how it works, and describes the use of RCS in the context of this work.

Special emphasis is laid on the design of Coda with respect to disconnected operation,

hoarding and reintegration in weakly connected environments. This chapter concludes

with the discussion of RCS that will be used for computing and maintaining file

differences and versions respectively.

3.1 Types of Connectivity

In the foreseeable future, mobile clients will encounter a wide range of network

characteristics in the course of their joumeys. Cheap, reliable, high-performance

connectivity via wired or wireless media will be limited to a few oases in a vast desert of

poor connectivity. Mobile clients must therefore be able to use networks with rather

unpleasant characteristics: intermittence, low bandwidth, high latency, or high expense


Networks that are not limited by such shortcomings are generally called strongly

connected. We mean that the network is not responsible in any way for any performance

degradation in the systems and applications that are using it. With the current technology,

such high-quality networks with unlimited bandwidths are usually found only in Local

Area Networks (LAN).

Mobile networks are inherently weak and are challenged by all the above factors.

Strong connectivity for wireless networks is a distant reality and weak connection may

always be a characteristic of mobile computing.

To counter the challenges posed by lack of connectivity, researchers have come

up with interesting mobile computing models. One of the earliest and most impressive

models is the disconnected operation model that came into importance due to constant

network and server failures. Its implications were soon identified for supporting mobility

by Dr. M. Satyanarayanan who pioneered the research of this model in the classic work

called Coda a distributed file system that supports disconnected operation for mobile


Disconnected operation is a mode of operation in which the client continues to

operate by accessing data that is locally stored during temporary failure or absence of

shared data repository. It is based on caching wherein important and useful data is cached

locally thereby increasing availability and performance [KIS92].

Disconnections can be of two types: involuntary disconnections that are caused by

network and server failures, out of range of connectivity and line-of sight constraints and

voluntary disconnections that are caused when users choose to not have network access

for their portable computers. Disconnections are caused by handoff blank out (> 1ms for

most cellulars, drained battery disconnection, battery recharge down time, voluntarily

disconnected to preserve battery power, theft and damage. The ways these are handled

are almost the same except that user expectation and co-operation are likely to be


However, disconnected operation has its own limitations and drawbacks


1. Updates are not visible to other clients

2. Cache misses may impede progress

3. Updates are at risk due to theft, loss or damage

4. Update conflicts become more likely

5. Exhaustion of cache space is a concern

Coda exploits weak connectivity to offset these problems. In the next sub-section

we will discuss about Coda and how it manages the different types of connectivity.

3.2 Coda

Coda is a distributed file system that has many features that are highly desirable

by network file systems. One of the most important and valuable contribution of Coda is

support for disconnected operation. In the following few sub sections we will discuss the

client-server model of Coda, its different operating states and how it adapts to varying


3.2.1 Client- Server Model

Coda is based on the classic client-server computing model. It is designed for a

large collection of untrusted clients and a much smaller number of trusted servers

[KIS92, SAT90]. It provides high availability by server replication and localfile caching.

Each Coda client is called Venus and has a local cache on which it relies for all its file

accesses. All files are grouped into volumes, each forming a partial subtree of the entire

namespace and typically containing the files of a single user or a group. The client uses

RPC2, a variant of RPC implemented at CMU, to communicate with the servers while it

is connected. However, it may be not be able to access the server in the event of network

or server failure or if the mobile client has been detached from the network.

Coda consists of a group of replicated servers each called a Vice. The entire group

of servers is collectively called the volume storage group (VSG). The subset of the VSG

that is available to a particular client at a moment is called the accessible volume storage

group for that client (AVSG). The client is disconnected when its AVSG becomes zero.

When the AVSG becomes zero for a client, all the file system operations are

served from the its persistent cache. The Client Modification Log (CML) records those

operations made while Venus is disconnected and replays this log on the Vice upon

reconnection. In the event that Venus is unable to serve a particular file request made by

an application or a user it reports a file miss. The ongoing work might be impeded and in

such an event the extent of damage caused depends on how critical that file is for a

particular task.

The reason why Coda has server replication (first-class replication) in spite of the

presence of local file caching (second-class replication) is that first-class replicas are of

much higher quality, are more persistent and reside on robust servers. Second-class

replicas are inferior to the servers in all these aspects and are subject to loss. It is

therefore required that the reliable and robust first-class replicas be maintained.

To support second-class replicas Coda uses the optimistic replication strategy in

which the writes can be partitioned allowing reads and writes everywhere. However, for

it to be viable Coda provides conflict detection and a resolution mechanism for the

conflicting writes that will be caused by this strategy. The rationale behind using an

optimistic strategy is that conflicting write operations constitute a very small proportion

of the total file accesses and because Coda is primarily targeted towards academic

environments wherein each user has his own private space in which all files are kept.

3.2.2 Structure of Coda Client

Venus is a user-level client cache manager that services all the file requests made

on the Coda namespace from its persistent cache. Venus has a tiny kernel module, the

Coda FS driver that redirects all the file system requests from the kernel to the user-space

and vice versa. The Coda client cache is stored generally on the local file system (e.g.,

Ext2 FS) in the configuration directory /usr/coda/venus cache.


User kwel meiurr k
system ca W McNO


Figure 3.1: Structure of a Coda Client

Figure 3.1 shows the structure of a Coda client and how a file system call is

serviced that is initiated from a user application program [BRAb]. A file system call

(read, write, open, close and others in the Unix context) that is made by the client

program is trapped by the operating system. Generally, the Virtual File System (VFS)

layer of the operating system intercepts such a file system call in the kernel. The VFS

redirects the system call to the Coda FS driver. The driver then communicates with

Venus by passing messages. Coda uses a character device /dev/cfsO for Venus to read

from the driver and vice-versa. The kernel driver makes an upcall using a message

structure passing the current file system call to Venus. Venus now searches in its Coda

cache and tries to service the system call from its cache contents. If it fails to find the file

in the cache or if the cache contents are stale, Venus makes an RPC call to the AVSG to

fetch the latest version of the file from one of the available Vices. If the contents are

found in the cache or when the RPC returns, Venus makes a downcall to the Coda FS

driver and replies to it about the system call that it is currently serving. The Coda FS

driver then responds to the VFS layer that in turn returns the file request successfully to

the user application that had initiated the file system call. However, in the event that the

client is disconnected, Venus will report a cache miss to the user if an RPC was required

to service the system call.

3.2.3 Venus States


Connection / Write-
Emulating Disconnection disconnected

Figure 3.2: Venus States and Transitions

Figure 3.2 shows the different states in which Venus exists and how the various

state transitions occur when the mobile client operates disconnected [MUM95]. Venus is

normally in the hoarding state trying to preserve its cache coherence. It is responsible for

caching all important and useful data for operating with minimal cache misses during

disconnection. Some of the hoarding techniques have been discussed earlier in Chapter 2.

In Coda, hoarding is done by prioritized cache management and by hoard walking. Hoard

priorities are assigned to file objects based on the user's activity. There is a spy utility

that logs the recent activity of the user on the file system and assists users to specify the

list of files that are to be hoarded in the per-client hoard database. Venus periodically

does a hoard walk of the cache to ensure that high priority objects are in the cache and

that the cache objects are consistent with the servers.

When Venus disconnects from the network its cache begins to emulate the server

and services all file requests. Any file request that cannot be serviced from the cache is

reported to the user as a cache-miss. All of the file operations that are made while Venus

is disconnected are logged in the Client Modification Log (CML).

Upon reconnection, Venus starts its process of reintegration. The CML is packed

together and sent to the Vice via an RPC and the file operations made while disconnected

are replayed on the server side. For any sto r e operations that are replayed, the Vice

does a callback fetch on the corresponding Venus and fetches the actual file contents

associated with that store operation. While disconnected, however, Venus optimizes its

CML by canceling corresponding s to re and un1 ink operations. This way it can

reduce the number of operations that have to be replayed and the callback fetches that

will be made by the Vice. This is the basic reintegration mechanism in Coda and occurs

during the write-disconnected state. In the write-disconnected state, files can be fetched

from Vice to Venus but the updates from Venus cannot be propagated to Vice

immediately. This state was introduced later in Coda with the aim to support weak-

connectivity that is the topic of discussion in the next sub-section.

3.2.4 Handling Weak-Connectivity in Coda

Coda uses two main techniques to handle weak connectivity. They are rapid

cache validation and trickle reintegration. The write-disconnected state discussed above

is essentially designed to manage weak connectivity in Coda.

Coda's cache coherence is based on the concept of callbacks, which is a promise

by the server to the client to notify it when the cache object in the client becomes stale.

The message sent for invalidation is called a callback break. However, during

disconnection there are no callbacks. So when a client reconnects after a period of

disconnection it has to validate each of the objects it has in its cache. During weak

connection this imposes unnecessary load on the network, wastes lot of connection time

and increases latency. To counter this, Coda raises the granularity at which it performs

validation. Instead of performing validation for each and every object, it validates entire

volumes. This removes the need to validate every object in a volume if the entire volume

has been identified to be valid. This method is called the rapid cache validation.

However, the primary mechanism by which Coda handles weak connectivity is by

trickle reintegration. Trickle reintegration is a mechanism that propagates updates to

servers asynchronously, while minimally impacting foreground activity [MUM95]. In the

write-disconnected state, the behavior of Venus is a combination of its connected and

disconnected behaviors. While file operations are logged into the CML, updates are also

propagated to the Vice. During weak connectivity reintegration is a constant background

activity and so it is termed trickle reintegration.

In trickle reintegration, instead of packing the entire set of updates in the CML

and sending it to the Vice for reintegration, it now packs only those operations in the log

that have aged. Aging is a mechanism that ensures that records have spent a sufficient

amount of time in the log and have been candidates for log optimizations. By separating

the foreground activity from the slow propagation of updates, trickle reintegration

improves the system for operation in weak connectivity. Figure 3.3 shows the CML while

weakly connected [MUM95]. A is the aging window. The records that are in the shaded

are being reintegrated while those outside have to spend time equivalent to the aging

window before they can be reintegrated. The reintegration barrier separates records that

are eligible for reintegration from those that are not.

Older than A

Log Log tail

T im e ............................................
Reintegration barrier
Figure 3.3: CML During Trickle Reintegration

3.2.5 Implementation of Specific Tools for Coda

We will discuss now some of the tools and mechanisms that were built to support

Coda and its disconnected operation.

1. RPC2 is a Carnegie Mellon implementation of Remote Procedure Call. All

communication between servers and between server and client is done

using RPC calls.

2. Transaction Mechanism: Coda is a strict, stateful file system. Unlike NFS,

a stateless file system, Coda operates strictly based on transactions. This

guarantees atomicity to file operations.

3. Recoverable Virtual Machine (RVM) is a Coda file system mechanism to

preserve the state of the file system. RVM strictly traces the state of the

file system hierarchy. All the Coda data structures are maintained by the

RVM. The RVM is stored persistently so that Coda can be brought back

into the same state in which it had left upon restarting.

4. SmartFTP (SFTP) is a file transfer protocol and is a side effect of the

RPC2. It is a variant of FTP and it is used for all file transfers within

Coda. We have used this for all the file transfer in our incremental


3.3 Revision Control System

To support the incremental approach we have to maintain all the file versions that

are created at the server. Saving each and every file would be inefficient and managing

each version would not be easy. Instead, a good version control system is used in which

files can be checked in and checked out for specific versions. Version control systems

maintain a modification log relative to one initial version. Therefore versioning

effectively stores only one physical version that is highly efficient and especially useful

in the incremental approach where we want to maintain revisions for each file. Some of

the popular version control systems on the Unix side are SCCS, RCS and CVS. After

careful consideration we chose RCS because of its simplicity and ease of use.

RCS assists with the task of keeping software system consisting of many versions

and configurations well organized [TIC85]. A detailed description of the commands that

RCS provides can be looked up in the manual page of RCS. Some of the common

commands and those that have been used in this work are ci, co, rlog, res, and rcsdiff

Apart from the RCS commands we also use two other standard Unix utilities -

diff and patch. diff is a utility that is used to compare the difference between two files and


generate a difference file specifying the line numbers where new lines have been added

or removed, patch takes a patch file containing a difference listing produced by the diff

program and applies those differences to one or more original files, producing patched

versions. Normally the patched versions are put in place of the originals.


This chapter presents the motivation and basis for using the incremental approach

and describes our design and implementation that has been adopted for hoarding the files

into the client and propagating updates back to the server.

4.1 Motivation for the Incremental Approach

Disconnected operation is based on the assumption that users operate without

connectivity for short periods of time using their locally stored copies for lack of network

connection or due to high connection costs. A businessman may work disconnected on

some incomplete document while away from office, a developer may be writing

programs from home on a weekend using his locally cached copies. While disconnected,

users have some common characteristics -

They frequently work on the same set of files that they had created when they

were connected;

They generally make very little changes to their files during the short periods

of disconnection;

In order to save connection cost users have the general tendency to work on

their files or write emails without being connected. They then connect for a

small period of time and synchronize their mobile device with the outside


This gives us the opportunity to exploit the common user tendency to make

minimal changes while they are disconnected.

In Coda, in spite of remaining in the write-disconnected state and decoupling the

foreground activity from the slow and continuous propagations of updates by doing

trickle reintegration, it still suffers from a limitation: the updated files are propagated in

their entirety [LEE99]. Though the response time for the user decreases the actual

propagation of the changes generates heavy traffic. If a hoarded file in Venus becomes

stale, then Venus simply discards it and fetches the whole up-to-date version of this file

from Vice. In a high-speed LAN this does not have much effect but in a mobile

environment the client is usually a laptop or a PDA connected through a slow modem

connection that affects the time it takes to hoard and reintegrate complete files.

In the incremental approach we compute the changes made while disconnected.

Upon regaining network connection, instead of sending the entire files on which changes

have been made, we only send the changes that were computed. On the receiving side,

instead of dumping the older versions, we apply to it the changes that we just received

and generate the new file. Since the changes made are generally small, avoiding

resending the complete files and sending only the changes saves the network bandwidth.

The network savings may not necessarily be useful on a high-speed LAN network but

network traffic is considerably reduced on a slow connection resulting in quicker

synchronization and lower connection costs.

The incremental approach implemented by us is for transfer of file changes in

either direction from server to client and vice versa.

4.2 Incremental Hoarding

Hoarding that is done based on the incremental approach is called incremental

hoarding. When Venus caches a file by hoarding, it gets a callback promise from the

Vice that it will intimate the client if the cache version becomes stale. If two Venii are

holding copies of the same file and if one of them updates that file, Vice makes a callback

on the unchanged Venus. This Venus does a callback break by dropping the version it has

and fetching the latest version from Vice. In order to reintegrate incrementally, this client

will have to fetch the difference instead of the complete up-to-date version.

To support incremental hoarding, both Venus and Vice had to be modified. Our

basic approach is to identify the Coda file transfer mechanism and modify it so that it

transfers the difference files. We identified the RPC calls in Coda that are invoked for

performing file transfers during hoarding. While hoarding, we use the same RPCs that are

being used by Coda. However, we replace the full file that was being transferred with the

difference file before making the RPC call as will be explained below.

For incremental hoarding we have to maintain every version of a particular file at

Vice. We could store every version of the file but that would be a storage overhead.

Instead a good version control system is being used. RCS that was introduced in Chapter

3 is used to maintain the versions of a file. The basic mechanism is to check-in the latest

version of a particular file into the RCS server and later use the RCS server to compute

file differences between file versions.

Whenever a new file is created at Vice, the first version of the corresponding RCS

file is also created. This is done by the RCS check-in command ci. When newer versions

of this file are created, they are simply checked-in into the corresponding RCS server.

More on this will be discussed in incremental reintegration.

The thick arrows in figure 4.1 indicate the heavy network load between the client

and server when it does not use the incremental approach. In figure 4.2 and 4.3, we

indicate how the client and the server have been modified in order to support incremental

hoarding and reintegration. The thin lines are an indication of the efficient network usage.

Venus Cache I Coda server

Figure 4.1: Regular Full-file Transfer between Coda Server and Client

patch before ci

diff rcsdiff upon the latest
Figure 4.2: Modified Vice Supporting Incremental Approach


o >

< (I
(D 0


Figure 4.3: Modified Venus Supporting Incremental Approach

An archive.info file is created in /usr / coda/ archive if it does not exist

already which holds the version information of the files along with the complete path

name in the Coda namespace. When a particular Venus file gets a callback break from the

server, it makes a vi c e Fetch RPC call from the Fe t ch function in Venus to the Vice

requesting the newer version of the file. Before it makes the RPC call Venus checks in its

archive.info if it has an older version of the file that is about to be fetched. If so it

prepares to fetch only the difference file instead of the complete file. To do so it reads the

version number from archive.info and sets it in the dat ae r s i o n vector of the

ViceStatus object. It also changes the name of the file1 to be fetched in the

SFTP Descriptor. The RPC call is made now to fetch the difference by passing

the connection id, file id, version number and other parameters as described in the

function signature below.

ViceFetch (IN ViceFid Fid,
IN ViceVersionVector VV,
IN RPC2 Unsigned InconOK,
OUT ViceStatus Status,
IN RPC2 Unsigned PrimaryHost,
IN RPC2 Unsigned Offset,
IN RPC2 CountedBS PiggyCOP2,
IN OUT SE Descriptor BD);

At the Vice, the procedure that handles this RPC call is FS Vice Fetch. The

steps carried out by the RPC for doing the fetch are

1. Validate Parameters
2. Get Objects
3. Check Semantics (Concurrency Control, Integrity Constraints, Permissions)

1The difference files are given temporary names that is the name of the original file
suffixed with. diff

2 SFTP Descriptor is a data structure that holds the parameters like name, size, file
descriptor etc of the file that has to be transferred.

4. Perform Operation (Bulk Transfer, Update Objects, Set Out Parameters)
5. Put Objects

In step 4, the parameters of the bulk transfer function FetchBul kTrans fer

are modified so as to pass the version number along with it. Inside this function, before

doing the actual file transfer using SFTP, it computes the difference file if the version

number received is greater than 0. r c s di f f computes the difference file by using the

version number against the latest version in the RCS server for that file object.

The SE Descriptor is modified so that the file to be transferred is set to the

difference file instead of the latest file available at the server. The difference file is now

transferred to Venus by passing the modified SED e scr ip tor to the SFTP RPC called

RPC2 CheckSideEf fect that does the file transfer.

At Venus, after the RPC returns, it retrieves the file it has in the archive

corresponding to the version number in the archive.info. The difference file obtained

from the RPC call is patched to this archive version and the latest file is generated that

matches the latest version that is at the server. This file is now made available in the

Venus cache and is accessible from the Coda namespace.

For a remove operation the particular file object that is being deleted is archived.

The file is removed from the cache and the relevant RPCs are called to delete the file and

its RCS server from Vice.

Similarly, if a file is renamed it is handled gracefully by changing the name of the

archive file at the client and by changing the name of the RCS repository at the server.

4.3 Incremental Reintegration

Incremental reintegration is used to transfer changes from the clients to the

servers efficiently based on the incremental approach. In a weakly connected mobile

environment reintegrating by transferring complete files lays a heavy burden on the

network. In Coda, all S TORE operations have to be reintegrated to the server so that the

server maintains cache coherence across the distributed file system. STORE operations

occur at the client while the user is connected or when he is disconnected.

To implement incremental reintegration within Coda we used a similar approach

as we did for hoarding incrementally. We identified those RPCs that are responsible for

performing the STORE operations on the client.

There are two types of store operations in Venus. The S TORE operations

performed while the client is connected are categorized as connected stores and are

handled by the ConnectedStore function. The STORE operations performed when

the client is disconnected are called disconnected stores and are handled by the

DisconnectedStore function. The RPCs called in each of these are different.

In Co nne c t edS tore the Vi c e S tore RPC originally transferred the newly

created files in its entirety. However, to support incremental transfer of the files we create

the difference file between the new version just created at the client and the old version

that existed before the change was made.

To be able to create the difference file, Venus needs to maintain an archive that

has the one of the older versions of this file object. If the current S TORE operation is the

first S TORE on this file object then it has no old versions in its archive and the file has to

be transferred in its entirety. This archive is generally stored in

/usr/coda/archive/. The entire Coda namespace from /coda is archived in this

directory at /us r/coda/ archive /coda. This is also the archive that is used by the

Fetch function for incremental hoarding as explained earlier.

The ConnectedStore function uses the diff utility of Linux that is execed

from within Venus to calculate the temporary difference file. It also calculates its new

length and changes the file name in the SFTP Descriptor that carries information

about the file that will be transferred by the RPC.

The RPC called for the file transfer is ViceS tore. This RPC has been

modified so that it checks out (using co of RCS) the appropriate version from the RCS

server of that file and applies the difference file that it obtains by calling

S t o reBul kT rans fe r. This function is responsible for doing the actual file transfers

between the server and the client brings the difference file to the server. This difference

file is patched to the checked-out version to generate the new version. The new version

hence generated is checked in (using ci of RCS) back into the RCS server. Following is

the signature of the ViceStore RPC.

ViceStore (IN ViceFid Fid,
IN OUT ViceStatus Status,
IN RPC2 Integer Length,
IN RPC2 Unsigned PrimaryHost,
IN ViceStoreId Storeld,
IN RPC2 CountedBS OldVS,
OUT RPC2 Integer NewVS,
OUT CallBackStatus VCBStatus,
IN RPC2 CountedBS PiggyCOP2,
IN OUT SE Descriptor BD);

The basic steps carried out by Vi ceS tore at Vice are the same as described

earlier with Vi ce Fe t ch .

Di s connect edS tore is responsible for logging all the file operations that are

performed during disconnection. This is called the Client Modification Log (CML) and it

is also stored persistently. The ST ORE operations are logged in the CML along with their

length. We have modified this length that is logged in the CML to the length of the

difference file that will be generated when connection is restored and reintegration

occurs. We calculate the length of the difference by measuring the length of the

difference between the cache file and the archive file. If another S TORE happens on the

same file while continuing to be disconnected, the new ST ORE will replace the old

S TORE also setting the appropriate length.

When connection is restored, Venus makes a transition from the Emulating state

to the Write-disconnected state and starts reintegration of all the changes made while

disconnected. The CML is packed and sent to the server to be replayed there. However,

Vice does a CallBackFetch for all the STORE operations that it replays. The

CallBackFetch RPC does a transfer of those files that correspond to a S TORE in the

CML. We modified the Cal B ac k Fetch RPC at Venus so that it transferred just the

difference file that is computed as described in ConnectedS tore. On the receiving

side in Vice the new file is generated in the same way as it is for vi ce S t o r e the

difference file is patched to the corresponding RCS server version and the new file is

generated and checked-in into the RCS server. The signature of the CallBackFetch RPC

is as follows:

CallBackFetch (IN ViceFid Fid,
IN OUT SE Descriptor BD);

File removal and file rename operations are handled in the same way as described


4.4 Reintegration Control with Time and Money

In Coda, reintegration is an automatic process that initiates automatically and goes

on until all the changes made while disconnected have been reintegrated. Reintegration

after disconnection does not take a lot of time on a high-speed network and therefore

doing things automatically without user intervention is desirable. However, if a mobile

client is reintegrating on a weak connection, it may take a long time for the updates to be

propagated to the servers. In such situations the user has to be given the choice to control

reintegration. In QuickConnect and MoneyConnect we have identified two types of

common necessities of users to control the time spent during reintegration and the

amount of money spent to propagate updates respectively.

4.4.1 Reintegration Controlled with Time

During weak connection the time taken for update propagation increases as the

average number of packets transferred per second decreases. In a typical scenario a

mobile user makes updates to the cache contents while disconnected for a period of time

and then reintegrates over a weak connection from a modem. Even though doing

incremental reintegration will reduce the time taken for reintegration the user may still

want to specify a period after which he wants to stop reintegration. Also if the user is

reintegrating from an airplane over an expensive phone connection, he would like to limit

the connection time and hence the reintegration time.

We have provided a useful feature that does just this. QuickConnect is a feature

that allows user to specify the time for which reintegration has to be carried out. We have

used the Venus utility c f s to provide QuickConnect. The user can specify the time for

which reintegration has to be carried out in the following way.

%cfs quickconnect

To implement this we modified the cf s utility and added the qu ic kconne ct

command. The c f s command makes a p i o c t 1 that in turn makes the file system

i o ct 1. When making the p i o ct 1 call the time specified for reintegration is passed on

to i o ct 1. Venus services this io c t1 system call. Venus handles this by doing

reintegration of one volume at a time and also notes the time spent. It reduces the

remaining time for reintegration by the amount spent reintegrating the last volume. When

no more time is remaining Venus stops reintegration and remains in the write-

disconnected state until another reintegration is requested.

4.4.2 Reintegration Controlled with Money

With the recent advent of pricing models based on the number of packets

transmitted we identified the need to give user control over reintegration so that he is able

to specify the amount to be spent for reintegration. Common scenario these days is that

mobile users work disconnected for short periods of time and then they connect to the

Internet using their cellular phones. Some cellular services nowadays have a cost model

based on the number of packets that come in and go out of the phone. In this case it does

not help much by just being able to specify the time for reintegration. Users want to

control the amount of money spent in this pricing model. MoneyConnect is a tool that

allows the users to specify the amount of money that can be spent for reintegration.

Like QuickConnect, MoneyConnect is also implemented by modifying the c f s

utility. To command used to do reintegration by MoneyConnect is

%cfs moneyconnect

MoneyConnect is also initiated as a p i oct call. The amount to be spent is

converted into the max number of bytes that can be transmitted with that money. This


parameter is passed along with pioct 1 to the ioct system call. Venus services this

io ct 1 call by calling FailReconnect. A global variable is maintained to keep track

of the number of remaining bytes that can be reintegrated. This variable is reduced each

time after a Venus Callback RPC by the number of bytes that have been transferred by

the last call back fetch. When the number of bytes remaining reaches zero Venus calls

FailDisconnect and stops reintegration.


The goal of the experiments for incCoda was to demonstrate quantitatively how

the incremental approach is better than the full file transfer approach. We will first

discuss performance of incremental hoarding followed by reintegration.

5.1 Performance of Incremental Hoarding

Our experience with hoarding has been positive. The network performance has

improved owing to the smaller deltas that are being transferred in place of the complete

files. We did the following experiment to evaluate incremental hoarding against full file


A random sample of 100 files was chosen from the source code of the Linux

kernel. One set of 100 files was chosen out of the version 2.2.5 and another set from

version 2.2.12. We chose Linux source files since they are a good example of a developer

making changes from one version to another. The developer while disconnected may

have upgraded from one kernel version to another. As we know that Linux kernel

changes little from one version to another and so are good candidates to demonstrate

incremental hoarding.

Five groups of 20 files each are made for conducting the experiment. The scenario

of updating is as follows: Initially we have two disconnected clients both working on the

older version of the source code. While one of them is disconnected he upgrades his files

to the higher kernel version. When he reintegrates to the network all the updates are

propagated to the server. Later when the other client reconnects, his cache contents are

invalidated and the newer version of the source files are hoarded into his cache.

The payloads of the files were compared and the results obtained are show in

Figure 5.1. The results are expressed as a percentage of the transferred payload of the

incremental approach out of the total payload using the full file approach. The results

indicate that incremental hoarding is always beneficial. In the best case the incremental

payload is less than 1/20th of the payload otherwise.

Incremental Hoarding vs Original Hoarding

5 35 -
S25 -
"S 15
c 10

1 2 3 4 5
Linux source file set

Figure 5.1: Payload Comparison Between Incremental Hoarding and Original Hoarding

From the calculations above we would like to present the benefits in a real world

scenario. Lets say an IT company that has a lot of mobile users within the company

roaming with their mobile devices from place to place within the premises of the

organization. These users rely on Coda for their server needs and operate disconnected

while mobile. In this scenario lots of mobile users are constantly connecting and

disconnecting from the network and hoarding and reintegrating changes as they move

from one place to another. If the average number of updates made by a mobile user is

5MB and the actual size of the contents is about four times the size of the updates (the

average value from Figure 5.1) then the average bandwidth consumption per user is

reduced to one fourth. The implication of this is that since the average network usage is

reduced the network can support up to four times the number of users with the same

bandwidth. Also in another sense it will provide these mobile users faster hoarding

thereby saving the user and the organization lot of useful time.

5.2 Performance of Incremental Reintegration

Using incremental reintegration considerably reduces the network traffic caused

due to reintegration. The performance in our experiments improved by one order as

opposed to using the full file reintegration.

The subject files that we chose were Linux kernel source from above and also

email files. We used one single set of 100 Linux source files in this experiment. The other

set of subject files that we used were day-to-day email files. We used the email files in

PINE of a typical user on his disconnected mobile device. While disconnected we moved

some emails from one folder to another causing moderate changes to the email files. We

used about ten email folders and the total size of all the email files was about 6MB.

The idea behind using email files is that users often write emails while

disconnected and actually send them upon reconnecting. Also users want to maintain the

same email folders seamlessly on both their mobile devices and the fixed network. So if

the user is managing his emails while disconnected moving some of them from Inbox to

another folder, the corresponding email files are updated. When he reconnects he would

like to synchronize the email folders with that on the fixed network thereby requiring

reintegration of the email files. We believe that incremental reintegration will be

particularly useful for email files since the updates are a small percentage of the actual

file size. The results of our experiments prove the truth of this statement.

The experiment done to measure the network traffic for Linux source files is as

follows. A mobile user while connected has one of the older versions of the kernel source

on his mobile computer. He disconnects and while away he installs a newer version of the

kernel source files in the same directory location. When he reconnects the files are

reintegrated and the network traffic generated is measured in each case incremental and

full file transfer.

Table 5.1: The network traffic generated (in bytes) without and with the incremental
approach for the Linux sources and the PINE email folders.
Linux kernel source files PINE Email folders
Without Without
S With incremental i t With incremental
incremental incremental
(by) (bytes) (bytes)(bytes)

4369370 570134 12604004 615136

4356584 628010 12635300 554648

4403780 553954 12837716 577352

4322542 582818 12768556 674152

4313856 556624 12774556 539468

To measure the network traffic we use rpc2 t cpdump. This is a utility similar to

tcpdump and is provided by the Coda developers at CMU. rpc2 t cpdump dumps the

entire network traffic of RPC2 communication between the Coda client and the server.

The port on which the network traffic is generated for reintegration can be specified and


the network traffic is dumped for all the client-server communication that takes place

during reintegration. Care has been taken that Venus or Vice was not involved with some

other network activity on those ports at the time of reintegration.

We repeated the experiment five times for each of the subject files using both

reintegrating methods and obtained very encouraging values. The network traffic

recorded is shown in table 5.2. The reduction in network traffic is by one order of

magnitude. Figure 5.2 is a plot of the values in Table 5.1.

Network Traffic for Linux Source Network Traffic for PINE Folders

5000 14000
4500 -- 12000
E 3500 g10000
S3000 8000
S2500 I-
S 2000 6000
0 1500 0 4000
S 500 2000
0 0
1 2 3 4 5 1 2 3 4 5
Experiment number Experiment Number
-*- Wthout Incremental With Incremental - Withoutlncremental With Incremental

Figure 5.2: Network traffic for Linux Source and PINE Email Folders

5.3 Heuristics for Reintegrating PINE Email Folders

To impress upon the benefits of using the incremental approach for reintegration

we are presenting a heuristic analysis using PINE email folders of a common email user.

We chose email because it is a good example of making updates while disconnected and

reintegrating over a weak connection. Email is a day-to-day application with which users

spend on an average about 2.5 hours a day sending and receiving about 30 emails. For

executives these figures are much higher.

Table 5.2: Heuristics for Reintegrating PINE Email Folders
Count ofevents Reintegration Data Size
Email Processing (bytes)
Time (min)
ime (m ) E2 E3 E4 E5 E6 E7 Incremental Traditional

5 3 1 0 0 1 0 0 9334 1481253

10 7 3 0 4 3 0 0 29704 2896004

20 14 5 0 7 5 0 0 56677 2893005

30 20 10 5 13 5 0 0 214873 2900727

45 36 12 15 20 7 1 1 321067 2905069

We identified common events that users do to manage their emails in PINE. Some

of the common events are removing emails from the Inbox, moving emails to another

folder, etc. We identified 7 such events as shown in Table 5.3. Also the time spent by

users checking and managing emails varies. Some of the factors it depends on are the

number of emails the user gets, time of the day at which emails are being checked. Less

time is spent if he is rushing for a meeting and more time would be spent if the user were

managing long accumulated emails. The five processing times that we chose are shown in

Table 5.2.

Table 5.3: Event description for PINE folders
Event Description
El Messages removed from Inbox
E2 Postponed/send later messages
E3 Messages removed from folders other
than Inbox
E4 Messages added to folders other than
E5 Take address from email
E6 Create new folder
E7 Edit .pinerc

Based on the survey conducted within Harris Networking and Communication

Laboratory at the University of Florida, we gathered average pattern for our events.

While disconnected we performed these events on a user's email folders that are plain

files in the Coda namespace. Upon reconnection the email folders are reintegrated to the

server. We conducted the experiment using the traditional method and the incremental

approach and noted the bytes transferred in each case for reintegrating the changes made

to the email folders. The results we came up with are shown in Table 5.2. Figure 5.2

shows the huge difference between using the incremental approach and the traditional


Heuristics for Reintegrating PINE Email Folders

- 2500
" 2000 ---- Traditional
c 1500 Incremental
a 1000
> 500
5 10 20 30 45
Email Processing time (min)

Figure 5.3: Heuristics for Reintegrating Pine Folders

5.4 Storage Overhead

The only cost that has to be paid for using the incremental approach is the storage

space tradeoff because we maintain RCS server on the Vice for every file. However, RCS


stores the versions in a very efficient manner and the space overhead is overshadowed by

the performance gains obtained from using the incremental approach. Figure 5.2 shows

our evaluation of the extra space required on the Vice using the incremental approach as

compared to the regular full file transfer using the Linux source files experiment.

Storage Overhead using the
Incremental Approach

o 14
ii 10

2- -

0 0-----------
1 2 3 4 5
Linux source file set

Figure 5.4: Storage Overhead due to RCS Version Files

Another storage overhead is on the client side to maintain the archives that hold

the last version that the client holds before any updates are done. The extra storage

required by this archive is as much as the space required by the Venus cache. However,

since the Venus cache is usually small, the extra space is not really a burden since the

performance gains far out weighs the space loss.


We would finally like to present the conclusions that were drawn from this work,

what are the benefits, limitations and drawbacks of using the incremental approach. We

would also like to suggest future developments to make more out of the incremental


6.1 Achievements of this Thesis

In this thesis we experimented a new way to do hoarding and reintegration. After

reviewing the existing hoarding and reintegrating mechanisms against the incremental

approach we conclude that this approach is very beneficial and promising. The benefits of

the incremental approach are magnified in mobile computing environments when the

connection is weak because the bandwidth and connection are scarce in such networks.

The network traffic caused by the transfer of files is considerably reduced owing to the

differential transfer of contents. This is especially beneficial since it saves users

connection time and money.

The improvements on the time spent and the reduction on the network traffic vary

based on the usage pattern. There is an improvement of one order magnitude when few

changes are made on cache contents while disconnected. However, the gains are only

reasonable when many changes are made. As mentioned earlier, our premise has been

that users are disconnected only for short periods of time, and few changes are expected

from the users during these short periods of disconnection. Based on this, the incremental

approach has highly positive results in realistic scenarios. Also this approach is

transparent and does not require user intervention or administration unlike the operation-

based approach discussed in Chapter 2.

However there are certain limitations and drawbacks to this approach. The main

drawback of this approach is that it largely depends on how similar the two versions of a

particular file are. In the worst case one might make global replacements in a text file to a

particular string resulting in a differential file that is as large as the older version resulting

in little gain from this approach.

Transfer of binary files with the incremental approach is not as attractive as for

text files. However, this is attributed to the diff algorithm.

Another limitation within this implementation is that there is high dependability

on the Unix rcsdiff and diff utilities that are not very sophisticated.

6.2 Future Work

This thesis is a step in the direction of using the incremental approach. There are

useful improvements that can be made to this method of hoarding and reintegration.

A well-designed diff utility specifically tailored for the incremental approach can

be developed that creates good quality and small differential files. The utility may also

handle specific situations like the global replacement case described above.

For transferring binary files there are tools like rsync that can be used to transfer

contents based on the difference on the blocks of data inside a file. The blocks can also be

matched given the offset in the files and not just multiple of block sizes.

In this implementation we can improve version control by replacing RCS with a

more sophisticated version control system that provides API's for doing the basic

operations like ci, co etc. Revision Control Engine (RCE) is a tool that is available that

provides API's.

Another interesting possibility is to use the incremental approach in conjunction

with operation-based update propagation. Based on the situation either differential or

operation-based shipping may be used. If the differential file that is generated is small

enough then we can use the incremental approach otherwise resort to the operation-based


Based on our experiments and evaluations we conclude that the incremental

approach is highly beneficial and useful to hoard and reintegrate after short periods of



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Abhinav Khushraj was born on October 17th, 1977, in Chennai, India. He received

his undergraduate degree Master of Science (Technology) in Information Systems from

Birla Institute of Technology and Science, Pilani, India, in June 1999.

He joined the University of Florida in 1999 to pursue a master's degree in

computer and information science and engineering. He has worked as a research assistant

with Dr. Abdelsalam Helal in the Harris Communications and Networks Laboratory. His

main research interests lie in mobile computing and mobile data management.

He has worked in the industry for about a year altogether. At Software

Technology Parks of India, Bangalore, he worked on two-tier client server architecture

for databases. He has also worked as an intern at Citrix Technologies, Ft. Lauderdale, in

the summer of 2000 and has experience in software localization and memory leak


After his graduation he will continue at Citrix as a full time employee working on

thin-client server technologies.

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