Title: Parallel and sequential job scheduling in heterogeneous clusters : a simulation study using software in the loop
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Title: Parallel and sequential job scheduling in heterogeneous clusters : a simulation study using software in the loop
Physical Description: Book
Language: English
Creator: George, Alan D.
Collins, Dave E.
Affiliation: University of Florida
FAMU-FSU -- College of Engineering
Publisher: High-performance Computing and Simulation Research Laboratory, Department of Electrical and Computer Engineering, University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2001
Copyright Date: 2001
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Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
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2001, HCS Research Lab All Rights Reserved

Parallel and Sequential Job Scheduling in Heterogeneous Clusters:
A Simulation Study using Software in the Loop

Dave E. Collins* and Alan D. George**

High-performance C- ',,ii-,,,i and Simulation (HCS) Research Laboratory
*ECE Department, FAMU-FSU College of Engineering, Tallahassee, FL
**ECE Department, University of Florida, Gainesville, FL

The task of designing and 'pmi-i_,i job scheduling li 1i...' im, for heterogeneous c. ,ni,~j-r,, environments
requires the ability to predict scheduling performance. The complexity of heterogeneous scheduling issues requires
that the experiments devised to test a scheduling paradigm be both flexible and extensive. Experimentally
determined models for the prediction of job execution times on both sequential and parallel c. mij 'ii, resources
are combined with the implementation of a novel scheduling ,il .... iln and a software-in-the-loop (SWIL)
simulation. The result is a potent design and analysis approach for job scheduling u i~ ., a/i,, and implementations
intended for heterogeneous environments. This paper develops the concepts, mechanisms, and results of a SWIL
design and analysis approach. The merits of this approach are shown in four case studies, which determine
overhead, scheduling performance, and the impact of preemption and priority policies. These case studies illustrate
the contributions of this research in the form of a new parallel job scheduling ~lim... lm for heterogeneous
c.-oi 'i-'rni and in the novel application ofSWIL simulation to the analysis ofjob scheduling systems.

Keywords: Resource management systems, simulation, job scheduling, preemptive scheduling, priority
scheduling, software in the loop, heterogeneous computing, cluster computing

1. Introduction
Commercial off-the-shelf (COTS) workstation clusters are probably the most powerful yet poorly employed
computing resource available to most organizations today. Furthermore, many of these clusters are heterogeneous in
architecture, operating system, or network. Often, this heterogeneity is not even planned, but arises simply due to
the march of technology over time and the whim of the computer market. Heterogeneous computing research has
shown that with careful job scheduling, heterogeneous collections of computing resources can usually outperform
comparable homogeneous resource sets when the application set places varied demands on the computing nodes and
the interconnection networks [11]. Considering the fact that workstation clusters have excellent price to
performance ratios, and given that COTS heterogeneous clusters can realize a very high level of aggregate
performance, why is it that heterogeneous clusters are rarely effectively applied outside of research efforts?
Part of the answer lies in inertia, since large organizations are often slow to adapt new technologies and
computing paradigms. However, a more significant factor is that heterogeneous clusters are difficult to use
effectively. The programmer and user generally need to be painfully aware of the fact that the underlying resources
and networks are complex and varied, and must condition their actions based on this heterogeneity of resources to
realize high performance in their applications. One possible solution is to develop a Single System Image (SSI) to
hide the complexity of the heterogeneous resource set from at least the programmer and end user, while easing the
burden on the system and network administrator. Older implementations of an SSI include IBM's Parallel Sysplex
and the DEC/Compaq VAXcluster environments. More recent developments in the SSI area include the IBM
Phoenix, Microsoft Cluster Service ("H'TlfacV k"), Berkeley's GL Unix, and Sun's Solaris MC [25].

An effective Resource Management System (RMS) is an important component of an SSI intended to improve the
efficiency with which tasks are scheduled to computing resources. Condor, Codine, NQS, NQE, Spawn, SmartNet,
Loadleveler, and DQS are a few examples of the currently available RMS implementations. With the exception of
SmartNet, the tools described above all operate under the principle of OLB (Opportunistic Load Balancing) [12].
OLB essentially assigns one task to each machine, and gives each machine another task whenever one has been
completed. However, SmartNet is significantly different, and uses the concept of an experiential database (i.e. a
database holding information regarding the execution time distribution of the job's previous executions used to
predict execution time) and a number of scheduling algorithms. An overview of SmartNet and job scheduling will
be forthcoming, as the case studies analyze and evaluate a parallel job scheduling approach that is a novel evolution
of the SmartNet concept.
The remainder of the paper is structured as follows. In the next section, an overview of related work in the area
of job scheduling is presented and the novel parallel job scheduling system is placed into the context of the existing
literature. In Section 3, an overview of the novel Parallel Job Scheduler (PJS) algorithm and its associated site
database is given. In Section 4, the method is discussed for the employment of software-in-the-loop (SWIL)
simulation around the existing PJS software. Section 5 includes the description, results, and analysis of four case
studies conducted using this approach as well as the motivation in each case for choosing a SWIL approach. These
case studies demonstrate how SWIL simulation can be used to investigate questions related to job scheduling
algorithms, implementations, and policies. These results show that the PJS is capable of achieving up to twice the
level of performance of its sequential ancestor and that it is particularly well suited for priority and preemptive
priority scheduling. In addition, the results also determine the overhead of the PJS within the context of batchjob
scheduling. Finally, conclusions about the uses and contribution of the SWIL approach as applied to the analysis
and design of job scheduling environments and RMS policies as well as future directions for research are discussed
in Section 6.

2. Related Work
The following section describes related work in the literature. SWIL literature is first treated briefly and a
review of literature related to job scheduling in heterogeneous computing environments is presented.

2.1. SWIL Methodology and Application
The ability to execute real software on simulated hardware has long been a desire for researchers and designers
in the field of computing. In recent years, the means to achieve this goal have emerged. By running sequential
program code on VHDL representations of processors, the co-simulation movement (also called co-design or co-
verification) took the lead in attempts to combine application software and simulated hardware. Co-simulation is the
process by which real application code, written in a high-level programming language such as C, is fed to a
processor model, written in a low-level hardware description language such as VHDL or Verilog [1]. Contemporary
to the co-simulation work, methods were developed to run applications on physical prototypes using reconfigurable
FPGA hardware. In recent years, a new emphasis has emerged for parallel systems and their simulation. Several
research groups have created methods for running parallel or multithreaded programs over simulated shared-

memory multiprocessors. None have as of yet proposed an approach for the use of cosimulation methodology for
the development, evaluation, and refinement of job scheduling algorithms for heterogeneous environments.
Software-in-the-Loop (SWIL) simulation is an effective methodology for analyzing the functionality and
performance of a software implementation. It differs from Hardware-in-the-Loop (HWIL) simulation in that the
software used is the actual implementation and the hardware and testing environment is partly or wholly simulated,
whereas in HWIL simulation the hardware is entirely real and the other elements may be thus simulated. SWIL
simulation is a common capability in hardware/software codesign packages such as Ptolemy, developed at the
University of California at Berkeley [6]. Such codesign environments require the ability to use a simulation of the
hardware to test the actual software and vice-versa to realize their full potential in accelerating the design cycle.
ISE, developed at the University of Florida, also provides SWIL capabilities for software testing, refinement, and
performance evaluation in a parallel computing environment [13]. Another area where SWIL methodology is
sometimes used is in the development of control software. A TTSIM is one example of a tool for SWIL simulation in
this area, intended for the design and verification of flight control concepts, architectures, and algorithms [18].
This research describes the use and proves the utility of SWIL simulation techniques for the evaluation and
analysis of implementations of job scheduling algorithms for heterogeneous computing environments. This
approach represents a novel application of the SWIL simulation paradigm.

2.2. Job Scheduling in a Heterogeneous Comluting Environment
Ideally, heterogeneous applications should be scheduled in an intelligent fashion to make best use of the
available resources. Best use of the available resources, in this case, refers to a metric of performance (e.g. total
completion time, average response time, or average throughput) chosen and weighted to reflect the values and
priorities of the users [5]. One goal of this research is to develop a unified approach to parallel and sequential job
scheduling for heterogeneous clusters. This approach is demonstrated by the development of the PJS, a novel
scheduling algorithm. The discussion that follows places the PJS within the context of the existing literature.
There exists a number of job schedulers that attempt to efficiently matchjobs to resources in a networked
environment. Some examples include the DQS, NQE, Condor, LoadLeveler, and SmartNet RMS tools. There also
exists a number of distributed operating systems upon which the parallelization of jobs is presumed to be the default,
such asAmoeba, Chorus, Mach, Sumo, and Spring [17]. Such distributed operating systems necessarily perform
scheduling on incoming jobs, as they aim to present an SSI to the user. There also exist parallel coordination
languages, such as Piranha Linda, which make some provision to the scheduling of resources and tasks [27]. The
PJS described later in this research is best described as a scheduling algorithm well suited to a RMS.
OLB is by far the most prevalent algorithm for scheduling jobs to machines with a conventional RMS. In simple
terms, OLB involves scheduling one task to each resource unit (e.g. a processor, cluster, or parallel machine). If
there are jobs remaining, the machines first completing their jobs will be assigned another task until all jobs are
complete. If there are fewer jobs than resource units, the resources first responding will receive jobs.
Best Assignment (BA) is also employed on occasion in an RMS. With best assignment, each task has a
designated type of resource that it is best suited to using. The scheduler then attempts to assign that job to a resource
unit matching that description. Sometimes this approach is combined with OLB in scheduling. This hybrid

approach uses job requests that specify on which type of machines the user would prefer the job be executed, and the
other, less preferred, machines that the job is capable of being executed on if necessary. This scheduling technique
is often used with the PVM (Parallel Virtual Machine) coordination language [28].
Hagerup presents the Bold scheduling algorithm, which assumes that the mean and variance of execution times
are known a priori [14]. It also assumes a fixed delay whenjobs are dispatched. Bold attempts to minimize the
makespan (i.e., the time necessary until the last task has completed). Bold adaptively decreases the batch size of
jobs assigned as time progresses in the attempt to ensure that each batch assigned will not be the last to complete.
Bold is not, however, designed to exploit the heterogeneous environment of SmartNet or the PJS.
With the exception of SmartNet, all RMS implementations at this point in time employ relatively simple
scheduling algorithms (i.e., OLB and BA) to make their scheduling decisions. SmartNet thus marked a step forward
in the evolution of RMS tools, as it supports the use of a large number of scheduling algorithms. There is one key
assumption in SmartNet's design: that past executions of a job type contribute useful information in the prediction
of future job execution times. The reader is referred to [21] for a more complete treatment of this assumption.
Techniques for the prediction of parallel application performance yielding relatively modest worst-case errors are
also explored in [24]. It is also implicit in the design of SmartNet that its users deal with a tractable number of job
types with relatively predictable execution characteristics. This knowledge is necessary to improve scheduling
performance over OLB, and the accuracy of this data greatly influences that performance [12].
It should also be mentioned that there is a good deal of research into the area of task scheduling. Generally, this
scheduling research is concerned with the execution of a single job, which is broken into a number of tasks that may
have precedence constraints and/or communication requirements. In such research, the focus is usually on reducing
the makespan. Directed acyclic graphs are used to depict the subtasks of the job, where a circle is used for each
subtask and an edge with a weight indicates the necessity and magnitude of any communication. Task clustering,
where several subtasks are assigned to a single machine, is one method used to minimize the makespan, as the
communication cost within a single machine is presumed to be negligible. In cases where the communication to
computation ratio is large, it is sometimes profitable to use task duplication (i.e., where the identical task is
performed locally on several machines to avoid the communication overhead needed to transmit its data). The
reader is referred to [23] and [34] for a more extensive discussion of this topic. Other research into approaches for
task scheduling can be found in [4,9,15-16,19,29-30].
These algorithms differ from the PJS algorithm developed in this research because they assume complete a priori
knowledge of execution times rather than statistical knowledge (i.e., mean and variance) of execution times. They
also differ from this research in that they generally deal with tasks that collectively form some largerjob. This focus
explains why such algorithms generally use the makespan as their figure of merit as the completion of the
component tasks only becomes meaningful once the entire set is finished. The PJS focuses on the job, which may
itself be a collection of tasks. For example, ajob scheduler might assign ajob that consists of a group of tasks to a
cluster. That collection of tasks could then use a task-based scheduling algorithm to minimize its makespan given
the resources allocated to it. Thus, there is a natural nexus between these two scheduling approaches.

An approach closely related to task scheduling, referred to as application-level scheduling, similarly attempts to
optimize the performance of a single job, usually composed of multiple tasks. One implementation of application-
level scheduling is the AppLeS (Application Level Scheduler), which is capable of using the related tool NWS
(Network Weather Service) to obtain information about resource availability although, in contrast to task scheduling
approaches, it makes no assumption of a priori knowledge of execution times [31-32].

3. PJS Algorithm and Database
Job scheduling is a key issue and a nontrivial problem when considering single-processor, stand-alone machines,
and a number of scheduling algorithm options are available for their operating system (OS) design. For a
heterogeneous cluster or group of clusters, the problem is far more complex. Yet, at the same time, considerable
performance gains can be realized if the jobs assigned to the cluster or clusters can be scheduled in an optimal, or
near-optimal fashion. While the general problem of scheduling has been proven to be an intractable one for the
majority of cases, scheduling algorithms based on optimization mathematics and general heuristics have proven
themselves to be useful [5]. The research effort described this section outlines a novel approach to generating a
near-optimal solution to this problem in the presence of uncertainty.
SmartNet marks the current state of the art in terms of job-based (rather than task-based) scheduling for
heterogeneous computing. However, it does have a number of limitations in its basic model, most notably the lack
of an ability to represent a single resource as being available both as a computational unit in and of itself and as a
member of a more powerful cluster. Research has been conducted into addressing some of the limitations of
SmartNet. An interface was developed allowing SmartNet to make use of parallel applications written for the Linda
coordination language. This interface was developed, tested, and verified in simulation in [8].
From this prior research, it is clear that scheduling performance can be improved substantially by allowing a job
scheduler to decide where and when to execute jobs in parallel. By loosening some of restrictions in the current
SmartNet model, while maintaining much of the same approach and principal assumptions, substantial performance
improvements may be realized through the use of the PJS algorithm. The working hypothesis for job scheduling
within a heterogeneous cluster is as follows:

Through the use of experiential data regarding both parallel and sequential c. 'uj ,,- ,,, resources, aggregate job
performance can be improved using scheduling ,i... aim, that impose modest overhead.

Within the context of this research, most advantageous job schedule is defined to be the schedule for that job
queue that yields the smallest summation of expected execution and delay times. This metric is distinguished from
the expected makespan, which is the amount of time expected to be necessary until the last job in the queue has
completed. The novelty of this approach is that it represents a fusion of both parallel and sequential experiential
data and supports the aggregation of computational resources in an intelligent manner. The PJS was developed to
prove this hypothesis. An overview of the PJS approach is provided in the paragraphs to follow.
The PJS maintains a database for each site. A site is defined as a group of machines that may be used
indifferently by a set of users. Thus, machines at a site will generally share substantial portions of their file systems

and user databases, a feature of definite assistance for remote job execution. Within a site are machines,

interconnects, clusters, users, job classes, and job queues. The PJS maintains information regarding the current and

previous states of these objects. The database organization used by the PJS is depicted in Figure 1.



Operational interconnects Specifications Job execution time Jobs in queue
and cluster memberships (performance rating, OS, etc) history and statistics or executing

Machine class


Current traffic Committed bandwdth Specifications Machines and clusters
from network monitor (bandwdth, latency, etc) served


Machines constituting Interconnects serving Job execution time Cluster policies
cluster cluster history and statistics

S Users

Job execution time Jobs submitted Policy constraints Security information
history and statistics by queue and execution Time

Job Classes

Execution time Job description Parallel execution possibilities Bandwdth and network usage
history and statistics (allowable machine classes) (allowable clusters) data from network monitor

Job Queues
Jobs in queue Scheduling algorithm Priority and preemption policy

Figure 1. Database organization for the PJS.

The first component of a site is its machines. Each machine has a number of resources, as illustrated in Figure 1,

whose state is ideally known by the job scheduler. While a job scheduler will obviously be aware of all the jobs it

has scheduled to a machine, it may not be aware of the interactive tasks running on that machine. This information

can be provided by a resource-monitoring tool such as PARMON [7]. System specifications, such as number of

processors, memory, stable storage space, and operating system must also be known, to permit the user and the job

scheduler to know if a job can actually be executed on that machine. For example, a binary will not typically run

without recompilation on a machine with a different OS from its original target. Similarly, certainjobs require vast

amounts of physical memory to execute in a reasonable amount of time. These resource requirements are abstracted

into the notion of a machine class. The level of abstraction of a machine class is left as a policy decision to the site's

support staff. At a minimum, a class should indicate that all executables that typically run on one member of a class

can be run on all members of a class. Furthermore, the operational interconnects servicing the machine and those

clusters that the machine is a member of must be known. The state of these interconnects can be provided to the
scheduler by a resource monitoring program. Lastly, the jobs currently executing and the job execution time history
for each machine and cluster are maintained in the experiential database. This information allows the job scheduler
to make an informed estimate of the execution time of each new job.
The next part of a site is its interconnects. Each interconnect is defined in terms of its specifications such as
bandwidth and latency and which machines and clusters are connected by it.
Another important piece of information for a job scheduler is the clusters available. Each cluster is defined as a
group of machines capable of executing a job in parallel. For example, a group of six Sun Ultra 2 workstations
connected via a high-speed data network might be designated as a cluster. The cluster also is defined by those
interconnects that serve it. Since each machine within a cluster is also a candidate for sequential execution, it is also
important to define cluster policies. Specifically, at what level of available cluster membership should parallel
execution be rejected? A clear statement of this policy is particularly important when the cluster membership grows
into the tens and hundreds, as it is probable that at least one machine will be unavailable.
Another important component of a site is the users. The number of jobs submitted by each user and the
execution times of said jobs is retained in the experiential database of the PJS. The particular job queue that the job
is submitted to is recorded in the experiential database. The policies set by the site's support staff governing each
user will also be recorded in the experiential database. Finally, provision for retaining security information for each
user is made in the experiential database.
Each site also maintains a set of job classes. These classes are an abstraction that may be set to the desired level
of specificity by the administrative staff at the site. A job class represents a set of potential jobs that have
statistically correlated execution times when executed on a given parallel or sequential resource. This correlation is
the justification for aggregating the execution time data for each (job class, computing resource) pair. Execution
time prediction from this data uses the same procedure as in SmartNet [12].
Lastly, the site maintains a set of job queues. Eachjob queue maintains its own policies regarding preemption,
priority, and user access. Furthermore, eachjob queue maintains a record of eachjob submitted to it. Also, eachjob
queue will have a scheduling algorithm associated with it. The scheduling algorithm assigns the jobs in the queue to
the appropriate parallel and sequential resources. These algorithms will be detailed later in this section.
An overall goal for the PJS is the minimization of the overhead of its software implementation. Due to this
objective, the level of centralization has been decided to be a single multithreaded daemon per schedulable machine.
This worker daemon accepts jobs sent to it by the manager, executes them, and returns experiential information to
the manager. One overall multithreaded manager daemon maintains the complete experiential database and
communicates with the client and any other tools in use.
The job queues have several scheduling algorithms available to them. The scheduling algorithms implemented
are as follows:

1. The OLB algorithm.
2. The duplex greedy algorithm (best of the min-min and min-max scheduling algorithms), hereafter
referred to as the Sequential Job Scheduler (SJS) algorithm and described later in this section.
3. The duplex greedy algorithm, with parallel cluster scheduling extensions, hereafter referred to as the
PJS algorithm and described later in this section.

OLB was selected for implementation in the PJS primarily for comparison purposes for future research, since
OLB is the primary scheduler in most RMS tools. OLB, due to its extreme simplicity, is also not computationally
demanding, which indicates that it may be a good choice for queues where extremely large numbers of relatively
short jobs are scheduled.
The second algorithm implemented within the job scheduler is the SJS algorithm. This algorithm takes the min-
min and min-max scheduling algorithms and chooses the result which yields the most advantageous schedule.
The min-min algorithm first determines the best resource for eachjob in the schedule and the amount of time
necessary for its completion on that resource. Best, in this context, refers to that resource that has the smallest
expected execution time for that job. Then the job with the smallest amount of time required for its completion is
scheduled on its best resource and the time of next availability is updated for that resource. This process is then
iterated until all of the jobs have been assigned to resources. Min-max uses the same procedure, but it schedules the
job with the largest, rather than the smallest, amount of time required for completion at each step. The SJS
algorithm is the primary algorithm used by SmartNet, and is included within the PJS implementation for purposes of
The final scheduling algorithm implemented is a novel extended version of the SJS algorithm. This algorithm
consists of the following steps:

1. Use the SJS algorithm in the same manner as in SmartNet to develop ajob schedule where all jobs are
executed sequentially. This schedule will form a baseline for improvement. Compute also the expected
summation of execution times for this schedule.
2. Construct a list of all sequential jobs that may be executed in parallel.
3. For each possible parallel (job, cluster) pair, compute the reduction in expected execution time from the
best sequential execution option for that job. This number is hereafter referred to as the profitability.
4. Allocate the necessary resources for parallel execution to the most profitable remaining parallel job.
Commit the resources used by this job for the expected duration. If there are no profitable jobs then stop
and use the best schedule determined thus far.
5. Using the SJS algorithm, schedule the sequential jobs around the jobs assigned to clusters thus far and
compute the new expected summation of execution times. If the parallel job improved the schedule
according to this metric, repeat steps 2-5 until the resulting schedule fails to be an improvement. At this
point, use the best schedule determined thus far.

It should be noted that this algorithm always produces a result that is superior or equal to the SJS algorithm.

Therefore, its costs over that algorithm lie solely in the overhead of its implementation.

4. SWIL Simulation Framework
SWIL simulation is one of the only viable approaches to the analysis of a job scheduling system without both a

complete prototype and access to all of the resource set to be used. Using this approach, one can develop

implementations of only the scheduling algorithms and the basic interface to the site database rather than a complete

RMS. With only the "brain" of the scheduling system implemented in actual code, a SWIL framework is then

constructed, providing a very powerful and flexible platform for analysis and refinement of the proposed system.

Four case studies have been identified to investigate the performance, overhead, and capabilities of the PJS and

the SJS approaches using SWIL methodology. These case studies include the determination of the overhead

imposed by the implementations of the PJS and the SJS algorithms, the comparison of performance of the PJS and

the SJS algorithms over a givenjob set and job load, the impact of priority scheduling on performance absent

preemption, and the effect of preemptive priority scheduling on performance. Each of these case studies is enhanced

through the use of SWIL simulation, as will be demonstrated in Section 5.

Software Implementation of Job Scheduling Algorithm
JobQueue 0 Job Queue


Co Co
L u

Display Data

Jobs Submitted

Figure 2. Configuration of the Software-in-the-Loop (SWIL) approach to job scheduling simulation.

Figure 2 depicts the configuration for the SWIL simulation approach used for the evaluation and analysis of the

PJS and the SJS software implementations. All of the simulation models were constructed using object-oriented

C++ code. The simulated client creates the jobs and allocates scheduling tables of the appropriate size. These
scheduling tables are actually processed by the software implementation of the PJS or the SJS algorithm that is "in
the loop." The simulation environment also provides the experiential data necessary for scheduling predictions.
This experiential data was experimentally determined and thus validated by performing a series of test runs of each
of the applications on the machines and clusters within the resource set. The SWIL environment used also contains
provision for a simulated network and machine status monitor. Although not used in any of the four case studies,
this provision may find use in future research. Finally, the simulation environment simulates the actual completion
of the jobs according to the experimentally determined sequential or parallel execution times. This data is provided
to the job scheduling software, as it is important for the determination of when resources are next available. This
simulation of job execution also interacts with the preemption policy in the fourth case study, as a simulated job
needs to be halted and rescheduled if a simulated preemption occurs. A fairly similar arrangement for the simulation
of various job scheduling algorithms is used in [2], although all components in that arrangement including the
scheduling algorithms are simulated and all the algorithms considered are for scheduling only sequential jobs.

5. Case Studies
A description of the four case studies and the experiments devised to investigate them is provided in this section.
Each case also includes a discussion of the results and analysis for that case study, as well as the factors that
motivated the use of the SWIL method.

5.1. Case Study 1: Scheduling Overhead Experiment with SWIL Simulation
The first experiment run on the PJS was devised to determine and compare the overhead for both the
implementation of the SJS algorithm and that of the PJS algorithm. The determination of the overhead of the SJS
algorithm is carried out with the number of machines and the number of jobs as variables ranging from 5 to 100.
Because the number of machines and the number of jobs was allowed to vary over a broad range, the method of
simulation was chosen. A strictly simulative approach could be used here, but a SWIL simulation allows more
faithful measurement of the overhead of a particular implementation of an algorithm running on a particular
machine. The job and machine sets were input to the actual scheduler objects and the schedule generated was then
discarded, as the machine set was purely simulated. The machine used for the overhead experiments was a 200Mhz
UltraSPARC workstation with 128 MB of main memory running Solaris V2.5. The overhead of the scheduler for a
particular number of machines and number of jobs data point was determined by subtracting the timestamp of the
starting time for that particular iteration from the ending time of said iteration. The high-resolution system clock
was employed. For the scheduling table itself, random values were filled in for the estimated time of completion for
each (job, machine) pair. This assignment was made because for the sequential algorithm, the actual contents of the
scheduling table do not affect the time needed to process it into a final schedule. This statement is not true in
general for the PJS trials, where the suitability of the job set for parallel execution may drive the overhead.
Figure 3 shows the overhead of the SJS as a function of the number of jobs and the number of machines. In this
figure, it is observed that the overhead grows with both the number of machines and the number of jobs in the
particular iteration. According to theory, the execution time trend should eventually approach O(n2m), where n is

the number of jobs and m is the number of machines [2]. This chart displays a relationship more akin to O(nm),
which is consistent with the explanation that the process of memory management (e.g., scheduling tables must be

allocated and deallocated and have a size proportional to nm) is the dominant factor for the values of n and m
investigated. The most important insight here is that the scheduling overhead is at all times very modest (generally
well below 0.2 seconds), particularly when compared to the expected run times of typical batchjobs. There is one
unusual peak in the center of the chart caused by the need to acquire additional memory from the operating system.


002 0
100 60

#ofJobs 20 /

Figure 3. Scheduling delay of the SJS.

Next, the overhead of the PJS was determined. Here a 40-machine resource set was stipulated, which contained
five clusters of four machines each and twenty "loose" machines. Recall that the PJS recognizes the dual nature of
the machines within a cluster. Each is scheduled both as an individual machine and as part of a cluster. When
scheduled a job as an individual machine, that machine's availability is pushed outward by the estimated time of
completion. This modification affects the time at which the cluster itself becomes available, as the cluster is
available only when all its components are ready. Scheduling a job to a cluster moves the available time of all its

components outwards as well as its own available time. Several cases were considered: no possibly parallel jobs,
10% possibly parallel jobs, 50% possibly parallel jobs, 75% possibly parallel jobs, 90% possibly parallel jobs, and
100% possibly parallel jobs. A job is possibly parallel if there is at least one cluster on which it can run in parallel.

It is possible that many jobs will not have a parallel implementation available. For instance, the speedups expected

may not justify implementing an application for parallel execution. Once again, the execution times were assigned
randomly for sequential (job, machine) pairs. The parallel cases were assigned randomly as well, but with

approximately one-third the mean estimated time of completion. This assignment was made so that the scheduler
would have a reasonable probability of finding favorable parallel options. The number of jobs was allowed to vary
from 5 to 1000, and overhead determination used the same SWIL technique as in the previous experiment.

Figure 4 depicts the average of the scheduling delays for each of the cases considered over a range of 5 to 1000

jobs. The PJS typically takes from two to three times longer to execute than does the SJS (i.e., the 0% possibly
parallel case).


4 -

DE2 -

Percentage of Possibly Parallel Jobs

Figure 4. Average PJS overhead compared by case.

Longer execution times with the PJS are expected, since additional steps are required with this algorithm. The
PJS will first compute a sequential job schedule. Next, it will find the most profitable job for parallel execution. If
there are no profitable jobs, the sequential job schedule is used. Otherwise, the most profitable job is then scheduled

in parallel and a new sequential job schedule is computed around it. This process is continued as long as the
schedule's performance metric improves. When the addition of another parallel job to the schedule fails to improve
it, the best schedule thus far is used and the process terminates. Thus, we expect the execution time to increase by

roughly the amount of time needed for a sequential schedule for every parallel job that is fit into the final schedule.
The averages in Figure 5 are consistent with that expectation. It is also noteworthy that the percentage of jobs that
are candidates for parallel execution appears to have no significant overall effect on the amount of time needed to

compute a schedule. This observation is explained by the fact that scheduling algorithms are designed to optimize
overall performance over the entire set of jobs given them. Since parallel jobs rarely approach unity parallel
utilization, only a few jobs will typically be scheduled in parallel under conditions of heavy load by the PJS. In a

large job set under conditions of heavy load, having 10% of the jobs as candidates for parallelism is not much more
restrictive in terms of the useful options for the PJS than having all of the jobs as candidates. Further, the amounts
of time required to compute a schedule, even in cases where a very large number of jobs are considered
simultaneously, are modest by the standards of batchjobs.
There are significant advantages to taking a simulative approach in this case study. First, the job set can be

quickly input into the scheduler without need for the entry of information that is irrelevant to the particular purpose
of the experiment. Job submissions normally include such things as command lines for each machine class or
cluster that the user deems fit to execute the code in question. When the objective is simply to calculate the
overhead imposed by the scheduler, the particular command line is unimportant, as the jobs need not be actually
executed. The SWIL simulation modules can simply intercept the job dispatch orders that would normally be sent to
the worker daemons. This technique allows the jobs to simply be scheduled, not executed, which is a serious
consideration when simulating the scheduling of large numbers of jobs. By using this technique, it is feasible to

perform a scheduling overhead analysis of a far largerjob set than would be practical otherwise. Yet another benefit
of this simulation technique is that any machine within the resource set may be real or simply notional. So long as
of this simulation technique is that any machine within the resource set may be real or simply notional. So long as

the performance characterization data is made available to the scheduler, notional machines can be scheduled along
with real machines. In this experiment the use of SWIL simulation allows the exploration of a considerably broader
range of resource and job sets than would be feasible through conventional means while retaining the fidelity
conferred through the use of the actual software implementation running on the actual target machine.
In summary, this case study shows that the overhead of the SJS is small over the range of n and m analyzed and
the overhead of the PJS is from two to three times greater. The percentage of jobs that are candidates for parallel
execution does not appear to significantly affect the overhead of the PJS, which indicates that the PJS algorithm is
efficient in its search for parallelism.

5.2. Case Study: Scheduling Performance Experiment with SWIL Simulation
This experiment was performed to determine the performance improvement, if any, that would be realized by
using the PJS algorithm versus the SJS algorithm. Only the PJS algorithm has the capability of scheduling jobs to
run in parallel on clusters. Just how much of a difference this capability makes is what the experiment was devised
to determine. The parameters of the experiment follow:

The scheduler was tested using SWIL methodology (i.e., the schedulers were directly fed experiential
data and used simulated dispatch facilities).
The machine set that was used was constant and reflected the machines then present at the research site
(142 machines, forming 18 clusters).
Machines were generally grouped into clusters of 8 identical machines, with one cluster having only 6.
The machines classes included several varieties from the Sun SPARC architecture family, and several
varieties from the Intel Pentium architecture family. This machine set and cluster arrangement was
used for this and subsequent case studies.
The job set consisted of an equal mix of five job classes.

Execution times for each (job, resource) combination were determined experimentally to produce the
experiential data for the schedulers for both sequential and cluster resources. This job set and its
experimentally determined execution times were used for this and subsequent case studies.

The five job classes for the test cases were chosen to reflect a reasonably diverse set of computing requirements.
The first job class was a vector-matrix benchmark. This benchmark reflects floating-point performance and is
typical of data parallel code. Parallel code was available for this benchmark as applications of this type are often
parallelized. The second job class was a numerical integration benchmark. Floating-point performance is stressed,
and parallel code was available for this benchmark as well. The third benchmark was an in-core integer sort.
Parallel code was available, but speedups were modest. The fourth benchmark was a statistical analysis application.
It used a mixture of floating-point and integer operations and was strictly sequential. The final benchmark used was
the Numerical Aerospace Simulation (NAS) conjugate gradient benchmark. The conjugate gradient benchmark
solves an unstructured sparse linear system using the conjugate gradient method and parallel code was available [3].

Both parallel and sequential performance for these benchmarks varied considerably over the resource set. The job

set was not chosen to favor any particular type of machine, but there was considerable heterogeneity to be exploited.

Figure 5 shows that the performance of the PJS is in all cases at least equal to that of the SJS. We expect to see

significant performance improvements under conditions of light load, as the cluster resources can then be employed

without overly slighting the sequential jobs. Such behavior is in evidence, as Figure 5(a) shows percentage savings

of total job time that range up to nearly 50%. In addition, there are also small performance improvements that

occur periodically under conditions of moderate and heavy load, although they are too small to be seen in this chart.

Such modest improvements reflect the ability of the job scheduler to occasionally find a profitable parallel job even

when the cluster is heavily loaded.

50 Sequential Job Scheduler (SJS)
E 10000 Parallel Job Scheduler (PJS)
0- 40 -

Sa 6000-
20 0
> 0 4000 -
10 E
E 2000-
0 -
0 20 40 60 80 100 0 10 20 30 40 50
Number of Jobs Number of Jobs
(a) (b)

Figure 5. Performance improvement realized using the PJS (a), and performance comparison of the PJS and the SJS (b).

There are significant advantages to employing simulative techniques. In this case and in the case studies to

follow, SWIL techniques were chosen over purely simulative methods because the SWIL framework constructed to

faithfully analyze the first case study required very little modification to enable further case studies.

By running the application code that forms the job set on the various machine classes and clusters under

controlled circumstances, the experiential data needed by the job scheduler can be determined without much of the

error typically encountered. This error comes about because it is often difficult to obtain totally exclusive access to

all of the machines in a site for an extended period of time. Thus, the interactive and otherjobs that the normal users

at the site are likely to perform in the course of their daily routine are likely to distort measurements of job

performance on particular machines. This distortion would be less of a problem were there no particular bias for or

against particular machines at the time of testing, but experience suggests that such is unlikely to be the case. By

testing each particular (job, resource) combination in isolation, better control over outside interference and the error

thus introduced is possible.

By intercepting the job dispatches as in the previous experiment, we are also freed from the necessity of actually

running the scheduled jobs at the time when the experiment is conducted. Since batch jobs are typically quite large,

and performance over a large range of load conditions is desired, actually executing the jobs in question could

consume a prodigious amount of computing time and network bandwidth. Such consumption would be very likely

to draw the ire of other users of the same resources, particularly if exclusive access was necessary. Another benefit
that could be exploited is the use of notional machines along with the real machines in the resource set. This
particular feature was not used in this experiment but it would be a powerful tool for deciding which, if any, new
machine types to add to a resource set. By adding additional notional machines or clusters, the performance impact
on a typical job set could be determined for each option and informed decisions about resource acquisition could
then be made. Due to the heterogeneity in many COTS-based environments and their typical jobs, the most
effective marginal addition to the resource set may not be obvious due to the gains possible from allowing additional
"specialization" by machines injobs for which they are particularly suited. Just as in the discipline of economics,
possible gains due to specialization often require analysis before becoming apparent. The benefits of allowing a
compressed form of the experiential data and the jobs to be scheduled to be input directly into the scheduling
module are also again present. Changing the job set, resource set, or the experiential data for (job, resource)
combinations is a simple matter when done in a simulation environment.
In summary, this experiment shows that the PJS has succeeded in its objective of making possible significant
performance improvements over the SJS. At the same time, the PJS retains the performance of the SJS under
conditions of heavy load.

5.3. Case Study 3: Priority Scheduling Experiment with SWIL Simulation
Priority scheduling is a feature often desired within a job scheduling system. It seems intuitively clear that the
addition of a priority scheme will degrade the overall performance of the system to some degree (although,
presumably the higher priority jobs will receive a performance benefit), however there is little literature available
that quantifies this burden in the context of heterogeneous computing environments. Thus, an experiment was
constructed to measure this cost when using the PJS. For purposes of comparison, the performance of a slightly
modified SJS was also analyzed.
The priority system used has three priority levels: high, medium, and low. All the jobs were immediately
presented to the job scheduler at time zero in the appropriate job queues. The job scheduler then completely
processes the high-priority queue. Afterwards, the medium-priority and then the low-priority jobs are processed.
Finally, all the jobs are dispatched, although this dispatch is intercepted by the simulation modules.
The number of jobs was allowed to vary up to 500 jobs, and four priority distributions were considered. The
first priority distribution had 10% high-priority jobs, 10% medium-priority jobs, and 80% low-priority jobs (i.e.
hereafter signified as 10/10/80). This distribution was intended to represent a typical job-scheduling environment,
where a few jobs are high-priority but most are not. The second distribution had 25% high-priority, 25% medium-
priority, and 50% low-priority jobs (i.e. hereafter signified as 25/25/50). This distribution reflects a site that makes
heavy use of priority levels. The third distribution had 50% high-priority, 25% medium-priority, and 25% low-
priority jobs (i.e. hereafter signified as 50/25/25). This distribution represents a site that sells its idle cycles but
wants to insure the performance of its ownjobs. That site would assign its ownjobs a high or medium priority and
relegate lent or sold "cycles" to a low priority. The fourth and final distribution had no priority levels and is
included for purposes of comparison. Priority assignment becomes particularly important in the final case study,

where the interaction of preemption and priority is investigated. In addition, the SJS was modified to use priority

queues in the same fashion to allow for comparison.

Thus, this experiment investigates the performance impact of using priority levels injob scheduling. Both the

sequential and parallel algorithms are compared, and four different priority distributions are considered.

Furthermore, the job load seen by the scheduler is allowed to vary as well. While the resource set is held constant in

this case study, the SWIL environment makes changing the resource set relatively straightforward, a feature that

may find use in future research into priority scheduling.

Number of High-priority Jobs

Number of High-priority Jobs

Number of High-priority Jobs

Number of Medium-prionty Jobs

Number of Medium-prionty Jobs



0 5 10 15 20 2
Number of Medium-prionty Jobs

Number of Low-prionty Jobs


20 40 60 80 1
Number of Low-prionty Jobs


20 40 60 80 1
Number of Low-Prionty Jobs

Figure 6. Comparison of PJS and SJS performance for priority scheduling for 10/10/80 priority distribution ((a),
(b), (c)), for 25/25/50 priority distribution ((d), (e), (f)), and for 50/25/25 priority distribution ((g), (h), (i)).

The charts of Figure 6 were generated using the results of a series of SWIL simulations for each of the fourjob

priority distributions. Each job priority level and job priority distribution is treated in a separate chart of the figure.

Each point on a chart depicts the summation of the job times for those jobs at the indicated priority level as a

function of the number of jobs at that priority level for the specified priority distribution. The total number of jobs
(i.e., aggregating all priority levels) ranges from 5 to 500. It should be noted that jobs at a particular priority level
coexist with jobs at the other priority levels as defined by the job priority distribution. For example, the point
representing 5 medium-priority jobs in the 10/10/80 distribution represents the scheduling of 5 high-priority jobs and
40 low-priority jobs as well, although only the summation of the total job times for the 5 medium-priority jobs is
displayed. Only the regions of interest are depicted in the figure and results are shown for both the PJS and the SJS.
Figure 6 shows that the PJS obtains performance superior to the SJS for high-priority jobs. This behavior is the
objective, as the PJS can favor high-priority jobs more than the SJS by executing them in parallel and thereby
obtaining speedups. The extent to which it can do so is directly related to the rarity of high-priority jobs. Figures
6(a), 6(d), and 6(g) support this observation. It can be concluded that the PJS algorithm gives greater ability to
optimize the performance of high-priority jobs. This feature is the most important capability of a priority scheduler.
The medium-priority jobs show interesting behavior as well in Figure 6(b). Here, a sharp rise injob time occurs
in the medium-priority chart just as the high-priority trends for the SJS and the PJS of Figure 6(a) are converging.
The convergence happens after approximately 30 high-priority jobs have been scheduled. This rise can be explained
by the fact that, at this point, the PJS has exhausted all of the clusters in its unfettered desire to optimize the high-
priority jobs. The medium-priority jobs then are receiving the negative consequences resulting from that high-level
decision and thus generally need to wait to run on the better machines. Shortly after this point, the high-priority jobs
themselves can no longer benefit in the aggregate from parallel execution and thus more are scheduled as sequential
jobs. This decision benefits the high-priority jobs slightly, but it benefits the medium-priority jobs greatly, as shown
in the sharp fall in their total job times. The same behavior is evidenced in Figures 6(e) and 6(h), but it occurs
earlier in the latter chart because high-priority jobs outnumber medium-priority jobs in the 50/25/25 distribution.
The performance of the low-priority jobs is shown in Figures 6(c), 6(f), and 6(i). In Figures 6(c) and 6(f), the
PJS realizes overall performance marginally inferior to that of the SJS. The degradation of low-priority job
performance relative to the SJS is not unexpected, because the PJS makes use of its ability to execute some of the
higher priority jobs in parallel. This scheduling decision naturally leads to longer delays in obtaining the necessary
resources for low-priority jobs relative to sequential job scheduling. Figure 6(i) shows that the performance of the
PJS is comparable to the SJS under the 50/25/25 priority distribution. This behavior results from the fact that the
high-priority jobs are numerous enough in this distribution to block their own parallel execution most of the time.
Thus, the low-priority jobs are in roughly the same situation for this distribution in the PJS as they are in the SJS.
The data in Figure 7 was collected by computing the sum of the job times of all the jobs (i.e., at every priority
level) for each number of total jobs considered. The mean of these summations was then displayed as a bar in the
chart. Thus, the chart displays the mean over the entire job range of the summation of job times for each priority
distribution and scheduler type (i.e., PJS and SJS).
Figure 7 shows that in terms of total performance (aggregating all priority levels), the priority scheduling
systems are generally, but not always, slightly worse than the systems would have been without the priority
schemes. It should be noted that both the PJS and the SJS algorithms are heuristic in nature and do not necessarily
produce optimal schedules. There is a certain amount of noise in the scheduling performance metric based on how

well the algorithm did in finding a minimum for each particular case. For small job sets, the algorithms generally

come closer to optimality than for large job sets. Thus, because priority scheduling reduces the effective size of the

scheduling problem (i.e., by dividing it into three problems), the efficiency of the algorithms themselves is slightly

enhanced. This enhancement appears to partially offset the adverse effect imposed by the priority system due to the

fact that the jobs at each priority level are scheduled without any knowledge of the jobs at lower priority levels. The

tension between this intentional ignorance and the improved efficiency of the scheduling algorithm for smaller

problem sizes explains why the priority scheduling systems generally, but not always, have an aggregate

performance degradation that is somewhat less than 10%. As seen in Figure 7, there does not appear to be a

significant difference between the PJS and the SJS in terms of aggregate overall performance in this priority

experiment. This observation indicates that the PJS improves the performance of higher-priority jobs relative to the

SJS by approximately the same amount as it worsens the performance of the lower-priority jobs relative to the SJS.

1 2e+5
a) -- -
1 0e+5 -
-o ~

S8 Oe+4-

S 6 0e+4 -
E 40e+4 -
CO >

00 -------.-----
co co c0 ) 0 Lo ao _
S 0 0
o o L L L L o C
oLO 0 0 o
z z

Job Priority Distribution and Scheduler Type

Figure 7. Comparison of average total performance of all jobs by scheduler type and priority distribution.

In summary, it can be concluded that the PJS is well suited for priority scheduling environments, as it effectively

favors the high-priority jobs relative to its sequential ancestor. A determination of priority scheduling policy should

also consider the performance penalties evidenced in aggregate performance over all priority levels. Finally, it

should be noted that the SJS and the PJS have approximately the same aggregate performance penalty under priority

scheduling, but that penalty is concentrated more on the low-priority jobs when the PJS is used.

5.4. Case Study 4: Preemptive Priority Scheduling Experiment with SWIL Simulation
The fourth and final experiment determines the performance impact of the use of a preemptive priority scheme

on the PJS. Preemption is a moot issue when all the jobs in a job set are initially provided to the scheduler, so it was

necessary to simulate the jobs as arriving at some average rate. In this experiment, high-priority jobs preempt low-

priority jobs if the scheduler covets their assigned resources. The low-priority jobs are then rescheduled and no

incremental progress is assumed. Medium-priority jobs are immune to preemption but cannot initiate preemption

against low-priority jobs. Simulation makes this experiment possible and a Poisson arrival process for jobs was

chosen. Several different mean job arrival rates are used (i.e., 1, 2, 4, 8, 16, and 32 jobs on the average per time

quantum of 10 seconds). The job priority distributions used are the same as in the previous case study. The SWIL

simulation environment for this case can be viewed as a superset of the other cases. For instance, if we set the mean

arrival rate such that all of the jobs will arrive in the first time quantum, the problem reduces to that of the third case

study on priority scheduling. Furthermore, if we also set the priority distribution such that all of the jobs have equal

priority, the problem reduces to that of the second case study onjob scheduling performance. Thus, the SWIL

simulation environment prepared for this experiment is suitable for a broad range of possible experiments. The

SWIL simulation environment in this case provides the jobs to the scheduler at an average rate, with priority levels

randomly drawn from the underlying distribution, and with the job types drawn from the job type distribution in a

random fashion as well. This task would be difficult to achieve faithfully without simulation, and the flexibility

gained makes the environment highly reusable for future experiments and revised scheduling systems.

This final case study analyzes the effect of implementing a preemptive priority scheme on the performance of the

PJS. In this experiment, 500 jobs were simulated using SWIL as arriving randomly according to each Poisson

arrival rate used and with priority levels randomly determined using each priority level distribution. The experiment

continued until all jobs had arrived and were completed. The meanjob time for eachjob priority level for the

10/10/80 distribution is shown by arrival rate in Figure 8(a). Recall that job time is the difference between the time

of final job completion and the time of first job submission. Figures 8(b) and 8(c) show the same information for

the 25/25/50 and 50/25/25 priority level distributions, respectively.

700 10/10/80 1200 25/25/50 1400 50/25/25
700 1200 1400

600 1000 1200-

500 1000 -
Low-priority Jobs w 800 -
H 400 Med-priority Jobs 800
High-priority Jobs I 600 -
300- i 600-
E 20-- .. 400 40-
200 00
100 200 -
100---------- -------------- ----- 200 200 -
0 00 0
5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30
Mean Job Arrival Rate per Time Quantum Mean Job Arrival Rate per Time Quantum Mean Job Arrival Rate per Time Quantum

(a) (b) (c)

Figure 8. Comparison of average job times by priority level versus arrival rate for
10/10/80 priority distribution (a), 25/25/50 priority distribution (b), and 50/25/25 priority distribution (c).

High-priority status forjobs significantly reduces their mean total job time for all of the priority level

distributions in Figure 8. As expected, the degree to which the average job time for high-priority jobs is reduced is

directly related to the rarity of suchjobs. For instance, the 10/10/80 distribution of Figure 8(a) is able to contain the

average job time for its high-priority jobs to slightly over 100 seconds, whereas the high-priority jobs in the

50/25/25 priority distribution take on average over four times as long to complete under conditions of high arrival

rate. The high rates of parallel execution (analyzed later in this section via Table 1) for high-priority jobs explain

their high performance relative to jobs with lower priority. Furthermore, the medium-priority jobs also experience

performance that is significantly superior to that of low-priority jobs. These observations show that the priority

scheme well serves the expressed goals in implementing preemptive priority.

In all of the charts of Figure 8, it is observed that an increasing arrival rate generally increases the average job

times. This statement is true in general because contention for resources increases with increasing arrival rates.

However, the trends are not monotonic increasing for two reasons. One reason is that the more jobs available to the

scheduler at one scheduling time, the more efficient the scheduling algorithms can be in resource allocation. This

scheduling myopia has its greatest impact for slow arrival rates. Also, when the jobs come in slowly, there is a

strong tendency for low-priority jobs to be preempted well into their execution. This effect is strongest for the

10/10/80 distribution for low arrival rates.

The average job time aggregated over all priority levels for each of the four priority-level distributions is

depicted in Figure 9(a). Here, using the preemption scheme in conjunction with priority generally results in a slight

degradation in overall performance relative to the no-priority case. For low arrival rates, the priority distributions

that make more heavy use of the higher priorities do better in the aggregate whereas the trend reverses after passing

a mean arrival rate of 8 jobs per time quantum.

800 10
700 -
S08 |- -No Priority
600 -- 10/10/80
E o --- - 25/25/50
50 06 50/25/25
400 '
300 04 -
200 -- --
100 V
0- 00
5 10 15 20 25 30 5 10 15 20 25 30
Mean Job Arrival Rate per Time Quantum Mean Job Arrival Rate per Time Quantum
(a) (b)

Figure 9. Comparison of average performance of priority distributions using preemptive priority (a),
and proportion of jobs executed in parallel compared by priority distribution (b).

At low job arrival rates, the high-priority jobs in the 10/10/80 distribution are frequently preempting the low-

priority jobs that are close to finishing. This frequent preemption occurs because the high-priority jobs in the

10/10/80 distribution and a mean job arrival rate of one job per time quantum appear every 100 seconds on average,

whereas low-priority jobs occur eight times more often. Thus, the low-priority jobs are likely to be able to make a

good amount of progress towards completion before a high-priority job appears. The arriving high-priority job is

very likely to covet those resources being used by the low-priority jobs. This contention occurs because when the

job arrival rate per time quantum is low, jobs tend to be concentrated on the most effective resources available. For

the 25/25/50 and 50/25/25 distributions, this effect is less prominent because the effective arrival rate of high-

priority jobs is greater and that of low-priority jobs is lesser, meaning that the low-priority jobs make less progress

on average before being preempted. As the job arrival rate per time quantum increases, the impact of preempting
low-priority jobs after substantial progress decreases, as the low-priority jobs are both coerced into sequential
execution (analyzed later in this section via Table 1) and relegated to the least preferred machines as can be
observed by their high average job times.
When the meanjob arrival rate per time quantum is high, the smaller overall number of high-priority jobs in the
10/10/80 case means that the scheduler can be more efficient than in the 25/25/50 and 50/25/25 cases. This
efficiency results from the fact that fewer constraints due to the scheduling of jobs with higher priority are being
applied to the lower priority jobs than in the other cases. The improvement in scheduling efficiency begins to
dominate the effect of frequent preemption after the mean arrival rate passes 8 jobs per time quantum, thus
explaining why the 10/10/80 case experiences performance superior to the 25/25/50 and 50/25/25 cases whenjobs
arrive quickly.
The proportion of parallelism (i.e., where 0% means no jobs are executed in parallel, and 100% means all jobs
are parallel) for each case is shown in Figure 9(b). In general, parallelism decreases as the arrival rate increases.

Table 1. Proportion of parallelism, by distribution, priority level, and rate of job arrival.

Mean Job Arrival Rate per Time Quantum
Priority Distribution, Priority Level 1 2 4 8 16 32
10r10ise Hegd -Orte n ,opo .lobs, in"c:',: a-"r:', ra ":e, -a ':', dec:',:, -0'a:',:
10/10/SO h redium-priorit, Jobs 7.. -2':' f .r:t c4'.-.:' c i.-.4r:'
10/10'0O Lo ,-pnortf Job 1:. 7 .. -47.. 0: '. .:. 10..
exp.'ece':,s :0 HiAgh-prorit., Jfobso -S":', -:e. 7i':', r 47.. 4'?-...t. o 4..-:
'.'' ede0 PaedUIIIm-prnorntia Jobs S.h S n : . 4?.. 4i'..
r ,t ''i io0 LO ,-priorit, Jiobs 0 7o':'. 47.. 1S':'. 7 ."
0'>''/2'F High-priorit.l Jobs a SO:' S0':,. 0'".:. '.2':' 4S.:. 40.:.
lo,0w-ri i", rediimal -priorite, Jobs 0 / 0 :' : ., i 'd5".:.t '. i: ,':
-,0s':-,2':-, Lo ,-prort, Jeobst i SOri0' -.'. 7:h. l. 12e"e ... :
I o rir it,r Le els 1 -11 Jobis 1'SO: 0 :. ', .:. 44..

The proportion of parallelism is shown in Table 1 for each priority level, priority distribution, and job arrival
rate. It is observed that the proportion, as it does with increased arrival rate, also decreases with a drop in the
priority level of the job. Since the jobs with higher priority have the first call on the clusters, this behavior is an
expected result. As the contention for resources rises, the PJS automatically reduces the proportion of jobs that it
schedules to clusters rather than sequential machines. The proportion of parallelism falls faster with increasing job
arrival rate for high-priority jobs in priority distributions with a higher percentage of suchjobs. This effect is a clear
consequence of the fact that high-priority jobs are less "special" in distributions that allocate such status more
frequently. However, there are several confounding factors. If a job is subjected to frequent preemption, as with
low-priority jobs with small arrival rates for the 10/10/80 case, when it finally does get to execute, it will usually get
a cluster. Also, for low effective arrival rates, there is little difference between high- and medium-priority jobs in
terms of proportion of parallelism, as there are enough cluster resources for both sets of jobs, although the best
clusters are usually allocated to the high-priority jobs, as is evidenced by their lower average job times. Finally, the
relative abundance of high-priority jobs in the 50/25/25 case at high arrival rates reduces the proportion of suchjobs

executed in parallel. This effect occurs because a large number of high-priority jobs often appear for scheduling
during one time quantum under these conditions, causing the PJS to schedule fewer of them to clusters.
In summary, addition of preemption to the priority scheme does have a slight negative impact on overall job
performance of the PJS. This negative impact is highly dependent on the arrival rate and the priority distribution.
Overall performance degradation due to the use of preemptive priority is most pronounced for the combinations of
low arrival rates and the 10/10/80 priority distribution, and for high arrival rates and the 25/25/50 and 50/25/25
priority distributions. Overall, preemption greatly benefits the high-priority jobs by giving them greater access to
cluster resources for parallel execution and by reducing the amount of delay in obtaining the needed resources for
execution. Since a preemptive priority job scheduler is principally judged by the performance of its high-priority
jobs, it is clear that the PJS is effective in this role.

6. Conclusions
This paper provides an overview of the application of the SWIL simulation technique for the analysis and
evaluation of a novel job scheduling algorithm intended to support parallel as well as sequential jobs in a
heterogeneous, cluster computing environment. This approach uses the software implementation of a scheduling
algorithm running in a partially or wholly simulated environment of computing resources and submitted jobs.
Thereby, scheduling system performance is investigated under a range of conditions that were not feasible to
duplicate with entirely real resources. Results are validated by the use of actual execution experiments on
representative machines for each machine class in the resource set. The usefulness of this SWIL approach for the
analysis of scheduling algorithms is then demonstrated with a series of four case studies that explore the
performance and overhead of the PJS algorithm representing a significant evolution of those in the literature.
The first case study demonstrates that the scheduling algorithms analyzed have modest overheads, even when
running on a fairly low-end machine. For instance, the SJS has a scheduling overhead of less than 0.2 seconds for a
schedule involving 100 jobs and 100 machines. Further, the SWIL simulations show that the PJS imposes from two
to three times the overhead of the SJS, and that this overhead is largely independent of the percentage of jobs that
were candidates for parallel execution.
The second case study shows that the total execution time of a set of jobs can be reduced by up to 50% through
the use of the PJS algorithm over that achieved by the SJS algorithm. This result is particularly notable in that the
sequential job scheduling algorithm has excellent performance when compared to OLB and other RMS scheduling
algorithms detailed in the literature. This performance improvement is always nonnegative as well, and is
concentrated in regions of light job load, making the PJS algorithm a useful evolution of the SJS algorithm
developed and employed in the SmartNet effort.
The effect of imposing a priority scheduling regime is examined in the third case study. It is demonstrated that
the PJS algorithm has significantly more ability to favor high-priority jobs than the SJS algorithm. In addition,
performance in the aggregate (i.e., considering all priority levels) is analyzed and the adverse effect of
nonpreemptive priority scheduling is shown to be fairly minor under the conditions studied.
The fourth and final case study investigates the effect of imposing a preemptive priority scheme on the
scheduling algorithms under varying conditions of job arrival rate and priority distribution. Under these conditions,

SWIL testing proves that the PJS algorithm used in a preemptive priority mode of operation is very effective at
favoring the high-priority jobs over the lower priority jobs as is the objective for a preemptive, priority-based
scheduling system. There does appear to normally be a performance penalty in the aggregate sense associated with
the use of preemption and priority under these conditions, but the penalty is not generally very large and in many
cases the improved performance of higher priority jobs may be well worth the cost.
Several future directions to this research are possible using the SWIL simulation approach to the investigation of
scheduling algorithms and RMS implementations. One possible direction is to investigate the effect of modifying
the PJS algorithm as its heuristic nature indicates that there may well be room for improvement on both its
performance and on its scheduling overhead. The development and analysis of analogous extensions for parallel job
scheduling to some of the more exotic SmartNet scheduling algorithms, such as Genetic Simulated Annealing
(GSA), might be one such possibility. Another possible direction of research is to further investigate the
possibilities and trade-offs in a preemptive scheduling scheme. For instance, the effect of adding provisions to help
prevent job starvation, such as increasing the priority level of a job after a certain number of preemptions, could be
investigated. Another possible direction would be to examine the effect of modeling job execution time as a
statistical distribution instead of a known quantity, as is the case in [2]. Yet another possibility is to investigate
making the preemptive version of the PJS only preempt low-priority jobs if it sees a potential gain greater than some
threshold. This change would increase scheduling overhead, but might reduce unnecessary contention over
resources. A final possible area of future research would involve linking the SWIL approach to one of the many
simulation tools available rather than the object-oriented C++ code that is currently used to simplify the
development of experiments, and thereby produce a more integrated SWIL simulation environment.

This work was sponsored in part by the U.S. Department of Defense.

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