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Title: A survey of market-based approaches to distributed computing
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Title: A survey of market-based approaches to distributed computing
Series Title: Department of Computer and Information Science and Engineering Technical Report ; 03-013
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Language: English
Creator: Shetty, Shashank
Padala, Pradeep
Frank, Michael P.
Publisher: Department of Computer and Information Science and Engineering, University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: August, 2003
Copyright Date: 2003
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CISE Technical Report TR03-013, Aug 2003

A Survey of Market-Based Approaches to

Distributed Computing

Shashank Shetty, Pradeep Padala and Michael P Frank
Computer & Information Science & Engineering
University of Florida
Gainesville, Florida 32611-6120
Email: {sks,ppadala,mpf}

Abstract-Distributed and grid computing has become
the choice of computing in high performance applications.
Various projects have developed software infrastructure
to harness the enormous power of distributed resources.
Applying market approaches to computing, though not
new, is an active field of research. Various key issues like
QoS of matching, protocols for negotiation and service level
agreements are not fully understood. Academic projects
like Nimrod/G and commerical projects like GridMP have
made good contributions in answering these key questions.
In this survey, we explore the distributed computing
paradigm and issues. We present an overview of meth-
ods used by various projects and how they do resource
management. We conclude with a list of open research
problems in this field.

Distributed computing strives to harness the power of
resources distributed in a network. There are millions
of computers connected to Internet that are usually idle
and can be part of collective computing. In today's
world, distributed computing has attained new heights
with complex projects like SETI@home[1]. But, there
are various challenges for achieving the noble goal of a
global computer. Recently, there has been many advances
in solving these problems using the grid. Grid[2], as it
is known, enables the sharing, selection, and aggregation
of a wide variety of geographically distributed resources
including supercomputers, storage systems, data sources
and specialized devices owned by different organizations
administered with different policies.
Grid computing has become the choice of distributed
computing especially in academic disciplines. To harness
the power of the grid, there are some key issues to
be solved in resource management. In the last few
years, a number of exciting projects like Globus[3],
Legion[4] and UNICORE[5], developed the software
infrastructure and protocols needed for grid computing.
Various distributed computing issues have been solved

using these tools and libraries. But, we do not yet
have a scalable, high-performance, self-serving resource
management architecture.
Surprisingly, we can apply the resource management
strategies used in economic markets in distributed com-
puting. In this paper, we provide an overview of market
approaches and how they are applied to distributed
computing. First, we provide a background of market
approaches including review of some existing distributed
computing projects. Then, we explain various issues
involved in resource management. Next, we present a
detailed survey of various projects using market based
approaches for resource management. We conclude by
summarizing our findings and open research issues in
this active field.

Economic systems in human society can be broadly
classified into two models: Central Planning Model and
Free Market Model. In Central Planning Model, a single
institution has total authority and decides what to pro-
duce, how to produce and to whom. In a free market
model, producers and consumers make these decisions
suiting their benefits. Central Planning Model is aimed at
the overall welfare of all individuals at the cost of some
individuals' freedom. Such an economy makes free trade
tougher. Thus, the free market model wins in this case
even though it has its share of disadvantages.
Auctioning plays a major role in a free market model.
Auctioning has history dating back to 500B.C., when
men used to bid for women, whom they wanted to
marry on the streets of Babylon. Auctioning has evolved
constantly over years. Christie's and Sotheby's are few
of the popular auction houses currently, where people
trade things. Change in technology also brings change
in our economic environment. Advent of computers and
Internet brings new dimension to auctioning. Online
auctioning services like e-bay and Yahoo Auctions have
major impact on how we buy various commodities.

CISE Technical Report TR03-013, Aug 2003

Now, researchers are trying to apply the same tech-
niques to distributed computing. It all started back in
1968 when Sutherland demonstrated how auction meth-
ods were used to allocate time to users of the PDP-1
Computer in Aiken Computation Laboratory at Harvard
University[7]. In this system, hours of the day were
divided into time slots. Users were assigned different
amounts of currency based on the importance of their
projects. Users bid for a particular time slot and a
user who submits the highest bid gets the privilege of
using the computer for that time-slot. This is one of the
first applications of economics to problems of computer
resource allocation.
But, Sutherland's work failed to address the problem
in distributed system. Drexler and Miller approached this
problem in paper[8], which describes auction mecha-
nisms for allocating distributed resources. This paper
made two important contributions to computational mar-
ket. First, market based mechanisms to allocate dis-
tributed resources is provided and second, the problem of
stability of pricing in computational market is addressed.
The advent of personal computers and Internet tech-
nology caused computational economy to attain new
dimensions. Increase in high performance computations
that require huge amount of resources like processing
power, memory etc. makes it impossible for an average
person to get such computations done on a normal com-
puter. The user may want to get the computations done
on other idle machines that are connected to Internet.
Usually, a personal computer sits idle for most of the
time, other than doing simple tasks like browsing the
net or editing a letter which only consumes a fraction of
total CPU power. This has led people to think about
harnessing the computing power of millions of idle
computers around the world. A big step towards this
direction was SETI@home project which uses comput-
ing power spread across the world to analyze radio waves
from outer space in search of extraterrestrial intelligence.
Anyone interested in participating in SETI@home has to
download a SETI screen-saver that takes care of using
the idle cpu cycles of the machine. It is amazing that
the creators of the Toy Story used 117 multiprocessor
Sun Sparc workstations working around the clock for
more than nine months to generate the films' images.
SETI@home could have accomplished this task in less
than seven hours[9].
SETI@home is not a market-based approach, rather
it is a simple cycle stealing technique where a user lets
the SETI@home project to use the computing power of
his machine free of cost. But SETI@home opened doors
to the development of new market based approaches in
this area by different players. With lot of research going

on in this area, there are lot of private companies and
academic institutions working on this. People have tried
to apply different economic models existing in the human
economic society. Some of the most common ones are
auction model, commodity market model, contract-net
model, bargaining model and bartering model[6].
Few of the popular projects that use market-based
approach are Nimrod/G[12], United Devices' Grid MP
Platform[14], Frontier (Parabon Computation)[16], Com-
pute Power Market[15], Mojo Nation[17], Entropia's
DCGrid[18] projects. We will give an insight into re-
source management in the next section before going
through some of the projects in detail.

Grid Systems allow applications to assemble and use
collections of resources on an as-needed basis, without
regard to its physical location. Grid middleware and
other software architecture that manage resources have
to locate and allocate resources according to application
requirements. They also have to manage other activities
like authentication and process creation that are required
to prepare a resource to use.
Some of the challenges involved in this task include
site autonomy, heterogeneous substrate, policy extensi-
bility, co-allocation and online control[10].
Site autonomy means resources are owned and oper-
ated by different organizations which may have differ-
ent policies, security mechanisms etc. Owner of these
resources has total control over the usage of resources.
Different sites may use different local resource
management systems like Condor, CODINE, and
LoadLeveler etc, which leads to heterogeneous substrate
problem. Even if two different sites use the same local
resource management system they might differ in con-
figuration leading to difference in functionality.
Resource Management should support frequent
changes of domain specific management structures with-
out any requirement to change the code thus supporting
policy extensibility. Resource management should adapt
itself to new user requirements and should be capable of
evolving itself to meet future demands.
Lots of applications need resources that can be sat-
isfied only by allocation of resources simultaneously at
several different sites, leading to co-allocation problem.
Resource requirements and characteristics may change
during execution, thus making it necessary for having
an online control because substantial negotiation may be
required to adapt to application requirements.
Grid Resource Allocation Manager (GRAM)[10] API
developed in the context of Globus project addresses

CISE Technical Report TR03-013, Aug 2003

all the five issues listed above. GRAM allows pro-
grams to be executed on remote resources regardless
of any heterogeneity. It provides a standard interface
for executing jobs using remote resources. Requirements
are communicated using resource specification language
Legion[l1], another grid middleware developed at
University of Virginia, has similar resource manage-
ment architecture. Features of the architecture include
global, shared namespaces, wide area data sharing, het-
erogeneity, security, efficient scheduling and resource
management. Legion has an object-based model where
each of its components is an object. These objects are
independent of each other and they communicate using
remote method invocation.
There are other projects that employ market based
appraoches for resource management. These include
Nimrod/G, OCEAN, Compute Power Market etc. For
example Nimrod/G supports Commodity market and
Contract-net models.

Finding optimal mechanisms for distributed comput-
ing using market-based approaches is an active research
problem. Various academic and commercial projects
like Nimrod/G, United Devices' Grid MP Platform,
Parabon Computation's Frontier, Compute Power Mar-
ket, OCEAN, Mojo Nation, and Entropia's DCGrid are
the result of research in this area. Some of these projects
are explained in detail with a brief overview on the rest.

A. Criterion for choosing the projects
We have selected few projects based on the following
criteria. First, it should be distributed computing project,
second it should have mechanisms to solve various
issues in resource management and third, market-based
approach should be used directly or indirectly.

B. Nimord/G
Nimrod/G[12], a grid enabled resource management
and scheduling system that uses Globus middleware
services for resource discovery and dispatching jobs over
grid. Nimrod/G is modular, extensible, portable, provides
interoperability of independently developed components
and provides a simple declarative parametric modeling
language for expressing a parametric experiment.
1) System Architecture: Nimrod/G has 5 key compo-
nents as shown in the figure 1.
SClient or User System: It provides a user-interface
that allows user to vary parameters and monitor

the status of the submitted jobs. It allows running
multiple instances of the same client at different
Parametric Engine: Heart of the system that man-
ages the whole experiment. As a persistent job
control agent, it is responsible for creation of jobs,
maintenance of job status, interacting with clients
and scheduling dispatcher. Declarative parametric
modeling language is used to give input to the
experiment. Experiment is recorded in a persistent
storage, so that the experiment can be restarted in
case of failure.
Scheduler: It is responsible for resource discovery,
resource selection and job assignment. Resource
discovery algorithm interacts with grid-information
service discovery (MDS) [13]and identifies the list
of authorized systems. Resource selection algorithm
selects those resources that meet the deadline and
minimize the cost of computation.
Dispatcher: It initiates execution of a task on the
selected resource. This is done by starting a remote
component named job wrapper.
Job-Wrapper: It is responsible for starting execution
of a task on the selected resource and sending the
results back to parametric engine.
2) Selection of computational resources: Selection of
computational resources is handled in two ways as shown
The work is completed within a given cost and
User is allowed to negotiate for resource in the
grid. The system can employ resource reservation or
trading technique to identify suitable resources. The
user is then allowed to renegotiate with a different
deadline or cost.

C. United Devices Grid MP Pli,,f,'i
Grid MP platform [14] .i_ ic lc computer resources
across clusters, servers, workstations and PCs to enhance
the performance of compute-intensive applications. Main
features are
supports wide variety of applications from message
passing interface jobs to data parallel jobs that run
on clusters, servers and desktops.
enables multi-step parallel or pipelined applications
to be executed as single job on the grid.
includes Linux Binary Emulator that executes Linux
applications on Windows platform without any
1) Grid MP I" /,f, i It includes tools and inter-
faces to create and manage various functions in the grid.

CISE Technical Report TR03-013, Aug 2003


Fig. 1. Nimrod/G architecture

Fig. 2. Grid MP 4.0 architecture

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CISE Technical Report TR03-013, Aug 2003

MP Web Console: User interface for administrators
to manage users, devices and jobs. It also allows
users to monitor the progress of their jobs.
MP Grid Service Interface (MGSI): It is a
XML/SOAP based web services interface that en-
ables applications to be grid enabled.
MPSUB: It provides a command line interface to
submit applications to resources.
MPIRUN: It enables parallel jobs to run on the
resources that are capable of running them.
MP S, 'l ,iciw Development Kit (SDK): Development
kit for programmers and users to develop custom
grid applications.
2) Grid MP System Components: It is a collection of
components that perform specific functions to run and
manage grid applications.
3) Grid MP Agent: It is the software that runs on
all the participating machines. It is responsible for iden-
tifying and authenticating itself to the MP Services,
downloading jobs and returning results after executing
the jobs. It takes care of securing the job application
from other applications executing on the same machine.
It also executes Linux libraries on Windows without any
modifications to the code.
4) Grid MP Services: It includes the following core
services that operate on the grid:
Realm Service: Registers, authenticates and licenses
devices on the grid. MP Agents authenticate them-
selves with Realm Services before starting execu-
Monitoring Service: Uses polling to periodically
process state information of all active devices on
the grid.
File Service: It is responsible for sending and re-
ceiving files from MP Agent. It uses an underlying
file system like NFS or AFS. It also provides
garbage collection for those files that are no longer
Dispatch Service: As the name says, it dispatches
jobs to those devices that are idle connect to it. It
uses a centralized scheduling engine that selects a
job based on device's capability, job priority, device
availability, usage preferences and other defined
constraints. It also reschedules failed jobs.
Metadata Repository: It is a database that stores
information on devices, device configurations, user
accounts, user roles, applications, jobs and data.
Currently, it supports DB2 and Oracle.
5) Grid MP Management Functions: Grid MP incor-
porates a set of management functions for administrators
and users to apply on the grid.

Application Management: It enables administrators
to register applications and manage them.
Device Management: It enables administrators to
separate devices based on their capability or geo-
graphic location.
Data Management: It enables the sharing and man-
aging application data across the system.
User Management: It allows administrators to man-
age users on the system.
Workload Management: It allows both users and
administrators to manage the jobs running on the
Grid MP has been most successful among organizations
that want to harvest the spare cycles of their own

D. Compute Power Market
Compute Power Market (CPM)[15] is a market based
resource management and job scheduling system for grid
computing. CPM is comprised of Markets, Resource
Consumers and Resource Providers. It supports three
major economy models Commodity market, Contract-
net/tendering and Auction models.
1) System Architecture: Three major components of
CPM are Market, Market Resource Agent, and Market
Resource Broker as shown in the figure 3.
Market is the point of interaction for both consumers
and producers. Consumers and producers have to register
with a market to buy or sell computational power. Market
provides the following services.
Repository of information on providers
Agents for consumers and providers
Mechanisms for updating the information
Interaction with other markets
Market Resource Index is a database that has infor-
mation on providers and consumers within that markets'
domain. It also stores information on other markets like
addresses of other markets, capabilities etc.
Market Control Unit is the Central processing unit
of a market. It is responsible for channelizing and
regulating requests, monitoring the market behavior and
synchronizing with other markets.
Provider domain is associated with resource providers
in the CPM. It is made up of Market Resource Download
Unit and Update Unit. Market Resource Download Unit
keeps track of download of market resource agents by
providers. On completion of download, an entry is made
in Market Resource Index for that provider. Update unit
is responsible for updating the provider information,
whenever the provider specification changes. Consumer

CISE Technical Report TR03-013, Aug 2003

Fig. 3. Compute Power Market

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CISE Technical Report TR03-013, Aug 2003

domain is similar to provider domain but deals with
resource consumers.
Resource providers download Market Resource
Agent(MRA) from the market. MRA is responsible
for updating the market with the latest information
on providers' resources and .cccpling. deploying and
launching the job. MRA works on a push-pull mech-
anism with the pull unit extracting information like
available memory, number of processes etc. whereas
the push unit pushes this data into the market through
communication unit.
Resource Consumers download Market Resource Bro-
ker (MRB), which is responsible for locating the appro-
priate provider based on the information furnished by
the market. It acts as a middleman between user ap-
plications and CPM resources. It includes the following
Job Control Agent
Market Explorer
Resource Trader
Deployment Agent
Job Control agent is responsible for shepherding a job
through the system by interacting with other components
of the broker. Economy computations in the CPM grid
is managed by schedule advisor, trade manger and trade
server. The schedule advisor uses grid explorer for
resource discovery, trade server for negotiating costs and
scheduling algorithms for identifying mappings between
jobs and resources that meet deadline and minimize the
cost of computation.

E. Frontier: The Premier Internet Computing Plalfa1
Parabon Computation is the first company to release
a commercial distributed computing platform Frontier
[16]. Frontier delivers the spare processing power of
millions of computer connected to Internet. It defines
three categories Clients (one who needs processing
power), Providers (one who provides unutilized power
of his/her computer) and Developers (one who writes
application on Frontier).
Frontier is comprised of three components:
SPioneer Compute Engine: It is a desktop applica-
tion that manages the unused processing power of
provider's computer. It is responsible for download-
ing tasks on the provider's computer from frontier
Server and uploading the results of the tasks back to
the Frontier server. Pioneer starts processing when
the provider's computer becomes idle and uploads
the results into the Frontier server when the provider
connects to the Internet.

Frontier Server: It is a centralized server responsible
for scheduling, dispatching and executing jobs. It
monitors all the tasks that are running and also takes
care of switching jobs to another machine in case
of system failure.
Frontier S,,'fin lic Development Kit: Software tool
that enables clients to write, monitor and control
the execution of their jobs.

F Miscellaneous Projects
Mojo Nation is a peer-to-peer file sharing system like
Napster, but the users cannot simply share files free of
cost instead users should pay the providers for whatever
they download. It employs bartering model and builds a
content sharing community within a network.
Entropia's DCGrid is aimed at organizations that have
computational bottlenecks in critical path processing.
DCGrid aims to utilize untapped CPU cycles of the Win-
dows based Desktops in the existing enterprise network.
DCGrid is comprised of two key modules DCGrid
Scheduler and DCGrid Manager. DC Grid Scheduler
schedules, deploys and manages execution of jobs on
the network, whereas DCGrid Manager provides a web
based, centralized management console for managing
the resources. It takes care of splilling. submitting and
gathering of job results.

G. Conclusion
We have surveyed various projects supporting market-
oriented distributed computing. There has been signifi-
cant work in various issues in market approaches. But,
there is a lot of potential for more research. Finding
optimal protocols for resource matching and benchmark-
ing various protocols is still an open task. Research
needs to be done for evolution protocols of matching
networks. How much overhead do these protocols im-
pose on networks? How many messages are distributed
in the network? How to reduce the overhead? all these
questions are not fully answered by any of the projects.

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Grid 1


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