Fault-aware, utility-based job scheduling on Blue, Gene/P systems
ABSTRACT Job scheduling on large-scale systems is an increasingly complicated affair, with numerous factors influencing scheduling policy. Addressing these concerns results in sophisticated scheduling policies that can be difficult to reason about. In this paper, we present a general utility-based scheduling framework to balance various scheduling requirements and priorities. It enables system owners to customize scheduling policies under different circumstances without changing the scheduling code. We also develop a fault-aware job allocation strategy for Blue Gene/P systems to address the increasing concern of system failures. We demonstrate the effectiveness of these facilities by means of event-driven simulations with real job traces collected from the production Blue Gene/P system at Argonne National Laboratory.
- SourceAvailable from: iit.edu[show abstract] [hide abstract]
ABSTRACT: As the scale of parallel systems continues to grow, fault management of these systems is becoming a critical challenge. While existing research mainly focuses on developing or improving fault tolerance techniques, a number of key issues remain open. In this paper, we propose runtime strategies for spare node allocation and job rescheduling in response to failure prediction. These strategies, together with failure predictor and fault tolerance techniques, construct a runtime system called FARS (Fault-Aware Runtime System). In particular, we propose a 0-1 knapsack model and demonstrate its flexibility and effectiveness for reallocating running jobs to avoid failures. Experiments, by means of synthetic data and real traces from production systems, show that FARS has the potential to significantly improve system productivity (i.e., performance and reliability).IEEE Transactions on Parallel and Distributed Systems 05/2009; · 1.80 Impact Factor
Conference Proceeding: Fault-Aware Job Scheduling for BlueGene/L Systems.[show abstract] [hide abstract]
ABSTRACT: Summary form only given. Large-scale systems like BlueGene/L are susceptible to a number of software and hardware failures that can affect system performance. We evaluate the effectiveness of a previously developed job scheduling algorithm for BlueGene/L in the presence of faults. We have developed two new job-scheduling algorithms considering failures while scheduling the jobs. We have also evaluated the impact of these algorithms on average bounded slowdown, average response time and system utilization, considering different levels of proactive failure prediction and prevention techniques reported in the literature. Our simulation studies show that the use of these new algorithms with even trivial fault prediction confidence or accuracy levels (as low as 10%) can significantly improve the performance of the BlueGene/L system.18th International Parallel and Distributed Processing Symposium (IPDPS 2004), CD-ROM / Abstracts Proceedings, 26-30 April 2004, Santa Fe, New Mexico, USA; 01/2004
Conference Proceeding: Anomaly localization in large-scale clusters[show abstract] [hide abstract]
ABSTRACT: A critical problem facing by managing large-scale clusters is to identify the location of problems in a system in case of unusual events. As the scale of high performance computing (HPC) grows, systems are getting bigger. When a system fails to function properly, health-related data are collected for troubleshooting. However, due to the massive quantities of information obtained from a large number of components, the root causes of anomalies are often buried like needles in a haystack. In this paper, we present a localization method to automatically find out the potential root causes (i.e. a subset of nodes) of the problem from the overwhelming amount of data collected system-wide. System managers can focus on examining these potential locations, thereby significantly reducing human efforts required for anomaly localization. Our method consists of three interrelated steps: (1) feature collection to assemble a feature space for the system; (2) feature extraction to obtain the most significant features for efficient data analysis by applying the principal component analysis (PCA) algorithm; and (3) outlier detection to quickly identify the nodes that are “far away” from the majority by using the cell-based detection algorithm. Preliminary studies are presented to demonstrate the potential of our method for localizing anomalies in a computing environment where the nodes perform comparable tasks.Cluster Computing, 2007 IEEE International Conference on; 10/2007
Fault-Aware, Utility-Based Job Scheduling on Blue
Wei Tang,∗Zhiling Lan,∗Narayan Desai,†Daniel Buettner‡
∗Department of Computer Science, Illinois Institute of Technology
Chicago, IL 60616, USA
†Mathematics and Computer Science Division
‡Argonne Leadership Computing Facility
Argonne National Laboratory, Argonne, IL 60439, USA
Abstract—Job scheduling on large-scale systems is an in-
creasingly complicated affair, with numerous factors influencing
scheduling policy. Addressing these concerns results in sophisti-
cated scheduling policies that can be difficult to reason about. In
this paper, we present a general utility-based scheduling frame-
work to balance various scheduling requirements and priorities.
It enables system owners to customize scheduling policies under
different circumstances without changing the scheduling code. We
also develop a fault-aware job allocation strategy for Blue Gene/P
systems to address the increasing concern of system failures.
We demonstrate the effectiveness of these facilities by means of
event-driven simulations with real job traces collected from the
production Blue Gene/P system at Argonne National Laboratory.
Job scheduling is a critical task on large-scale systems,
where small differences in scheduling policies can result in
poor use of substantial resources. However, while traditional
metrics for scheduling such as utilization rates and average
response times have long been used to assess scheduler policy
performance, large centers now face an increasing set of
considerations in scheduling. Examples of these considerations
include avoiding system faults, minimizing power consump-
tion during peak demand, and sharing I/O resources. At the
same time, these individual considerations do not occur in a
vacuum; systemwide metrics, such as utilization and average
response time, as well as other characteristics such as fairness,
remain important. A mechanism is needed to explicitly balance
these considerations. Moreover, this balance is not universal;
different systems have varied priorities that result in some
considerations prevailing over others.
While many scheduling algorithms have been presented in
the past, this paper is motivated by operational problems in
job scheduling and aims to provide a general utility-based
scheduling framework to balance various scheduling require-
ments and priorities. In our framework, scheduling policies are
described as job-scoring functions called utility functions that
can be changed on the fly. During each scheduling iteration,
each job’s score is evaluated, allowing the scheduler to take
the appropriate action. Utility functions are implemented in
Python code and can take job parameters, such as length, size,
and waiting time, as well as other factors, into consideration.
For example, administrators can use them to alter the balance
between responsiveness and utilization rate on a dynamic
Further, we have developed a fault-aware job allocation
strategy for Blue Gene/P systems to address the increasing
concern of system failures. Together, the utility-based schedul-
ing and the fault-aware job allocation not only enable system
owners to explicitly control scheduling behavior but also
enable intelligent, fault-aware resource allocation to improve
system performance. These facilities have been implemented
in Cobalt , a resource management and scheduling system
used at various supercomputing centers and laboratories.
The performance of scheduling policies is completely de-
pendent of the system workload, so intuitive assessment of
scheduling policies is frequently not reliable. To address this
issue, we have developed Qsim, a simulator of queue behavior
over time, based on real system workload inputs. Using this,
we can explore the behavior of different scheduling policies
and measure the resources lost to system failures. In other
words, not only can basic functionality be tested, but likely
behavior under system load can be predicted. Thus, by using
Qsim to test utility functions and fault-aware allocations strate-
gies, machine owners can build confidence in new scheduling
and allocation policies prior to deployment.
We have evaluated our fault-aware, utility-based job
scheduling with job logs collected from the 40-rack Blue
Gene/P system called Intrepid at Argonne National Labora-
tory . The results demonstrate that, as compared to the
conventional scheduling policy FCFS (first-come first-serve)
with backfilling, our utility functions can lower the average
response time and slowdown significantly (by up to 55%).
We have also examined how the utility functions achieve their
scheduling goals individually, and we have provided instruc-
tive comparison among them that results in the deployment of
utility functions for the real system. Further, we have verified
that the fault-aware job allocation mitigates the performance
loss caused by system failures (by up to 39%).
The remainder of the paper is organized as follows. Section
2 discusses related work. Section 3 provides a short description
of the Blue Gene/P system at Argonne and the Cobalt resource
manager used on the system. Section 4 presents our new
scheduling method. Section 5 addresses how to use Qsim to
evaluate scheduling policies. Section 6 shows the experimental
results. Section 7 presents a brief summary and our next steps.
II. RELATED WORK
Utility functions are widely used in economics to calculate
the relative values of comparable items. This approach has
been applied more recently in scheduling research to model
the value of particular jobs in batch systems for the submitting
users. Lee and Snavely present precise and realistic utility
functions for user-centric performance analysis of schedulers
. Vengerov et al. use utility functions to address the prob-
lem of dynamic scheduling of data-intensive multiprocessor
jobs . Chen presents a utility-based approach to schedule
multimedia streams in peer-to-peer systems .
The Maui scheduler  uses a policy scheduling mech-
anism that is similar to the utility scheduling implemented in
Cobalt. Cobalt’s utility functions are implemented as simple
python functions, whereas Maui uses a complex combination
of policy knobs and explicit callouts. Also, Cobalt does not
reserve resources in advance for jobs, instead performing
explicit drains and backfilling when needed.
Recently, increasing attention has been paid to fault-aware
scheduling in high-performance computing. In , Zhang et
al. suggest utilizing temporal and spatial correlations among
failure events for better scheduling. Oliner et al.  present
a fault-aware job scheduling algorithm for Blue Gene/L sys-
tems by exploiting node failure probabilities. In , fault-
aware scheduling scheme is presented for the HA-OSCAR
framework. Our work is distinguished from these studies
in two aspects. First, unlike existing studies focusing on
FCFS scheduling, our work presents and evaluates utility-
based scheduling, which enables system owners to customize
scheduling policies based on their needs. Second, to the best
of our knowledge, this is the first study on fault-aware job
scheduling for Blue Gene/P systems.
In , we studied fault-aware rescheduling for high-
performance computing. That work emphasized dynamically
adjusting the placement of active jobs (i.e., running jobs) to
avoid imminent failures discovered by on-line failure predic-
tors. In contrast, this work aims to intelligently select and
allocate inactive jobs (i.e., queued jobs) to execute on available
nodes. An active research project in our group focuses on
designing and developing effective failure diagnosis and pre-
diction mechanisms for large-scale systems . The
proposed failure prediction mechanisms can be directly used
by this work.
Simulation is a vital technique in both scheduling and
fault tolerance research. The Maui scheduler  features
a simulation mode for performing long-time scale analysis
of scheduling policies. In , Tikotekar et al. present a
simulation framework that evaluates different fault-tolerant
mechanisms such as checkpoint/restart and process migration.
Failure simulation has been used in the Cobalt project to
analyze system software behavior in the presence of hardware
and software faults . In this paper, our simulator is closely
related to the batch scheduler and has two main uses: one
is to aid the devising of scheduling policies by comparing
possible utility functions to those already in use; the other is
to measure the impact of potential failures on the system and
the effectiveness of fault-aware job allocation.
III. SYSTEM OVERVIEW
Intrepid is a 557 TF, 40-rack Blue Gene/P system at
Argonne. This system comprises 40,960 computing nodes with
more than 160,000 cores, and associated I/O nodes, storage
servers, and an I/O network. It is operated as a part of the DOE
INCITE  program. It was ranked the third fastest overall in
the June 2008 TOP500 list .
The IBM Blue Gene/P platform is a scalable, low-power
architecture. Intrepid is currently the largest installation; how-
ever, the architecture is scalable to upwards of 80 racks, with
a peak performance above 1 petaflop. In order to provide a
scalable, high-performance network, Blue Gene systems use
a partitioned torus network for communication. Each node is
part of a unit of allocation called a midplane, which includes
512 nodes, a 3D torus to connect them, and connectivity
with other midplanes. Each midplane can be used either
individually or in conjunction with other midplanes that are
adjacent on any of the three dimensions in the network.
Partitions must be of uniform length in each dimension. Also,
the wiring that connects midplanes together is also shared,
further limiting system partitioning possibilities.
Cobalt is an open-source, component-based resource man-
agement suite used on a large number of Blue Gene/L and
Blue Gene/P systems worldwide, including Intrepid. Cobalt
comprises 12,000 lines of Python code at the current release.
Cobalt components correspond to pieces of functionality in
resource management systems, such as scheduling, queue
management, hardware resource management, and process
management. Its component architecture allows easy replace-
ment of key software functionality. This allows Qsim, our
queue simulation component, to service the queue manager
component and system component interface without changing
any other software components. Hence, Qsim can interface
directly with an unmodified Cobalt scheduler. The Qsim
architecture will be described in Section V.
Cobalt is used on Intrepid for job scheduling by using utility
functions. In particular, a utility function that favors old/short
jobs and attempts to avoid large job starvation (i.e., WFP3
in Table II) is currently being used for production jobs, and
a utility function that provides fast turnaround for small jobs
(i.e., UNICEF in Table II) is being used for development jobs
on Intrepid. In Section VI-C, we will present simulation results
by using these utility functions.
Estimated job running time (wall time)
Job queued (waiting) time
Number of compute nodes requested by the job
Smallest partition size in the system
IV. SCHEDULING METHOD
User jobs are submitted via a job scheduler. For example,
on Intrepid, job submission and management are handled
by Cobalt. The Cobalt queue manager component maintains
submitted jobs in a waiting queue. The Cobalt scheduler com-
ponent is responsible for selecting queued jobs and allocating
them to appropriate resources in the system. Specifically, a
utility-based mechanism is used for job selection, and a fault-
aware method is adopted for job allocation. Job selection is
based on two values: a utility score and a fallback score.
For each queued job, Cobalt calculates its utility score and
fallback score according to a predefined utility function. The
scheduler attempts to run the job with the highest score. If
this job cannot be accommodated by any available partition,
the scheduler will attempt to run the job with utility scores
higher than the fallback score.
Resource allocation is based on allocation cost. All the
candidate jobs are mapped to the available partitions one by
one in a decreasing order of their utility scores. If a job
cannot be accommodated by any available (idle) partition, the
subsequent, lower-priority job can be considered, depending
on the fallback score. For each job, if multiple partitions are
available for its allocation, allocation costs on these partitions
are calculated and compared. The partition with the lowest
allocation cost will be chosen. If none of the jobs in the
candidate list can be allocated, a backfilling is invoked to
schedule other queued jobs.
Next, we will discuss the utility functions definition and the
resource allocation scheme in more detail. Before that, we first
summarize some nomenclature used through out the paper in
A. Utility-based Job Selection
In the job selection phase the scheduler selects jobs based
on their utility scores calculated by the utility function.
The utility function also returns a fallback score for each
job. The fallback score is the product of the job utility
score and a predefined threshold (i.e., a percentage). If
the job with the highest utility score (e.g., Sh) cannot be
executed, jobs whose utility scores above its fallback score
are considered. For example, the threshold 0.70 means that
only jobs with utility scores higher than 0.70 · Sh will be
selected for job allocation. Hence, the fallback score controls
the aggressiveness of scheduling and tries to avoid the job
with the highest utility score from starvation. In Cobalt, the
utility function can be specified as Python functions as follows
score = [define the utility function]
TH = [define the fallback threshold]
return (score, score * TH)
The design of the utility function depends on many
factors, including owner’s needs and job characteristics. As
a result, the job utility score can be influenced by arbitrary
arguments. In our experience, the arguments can be classified
into two categories: one indicates the job’s urgency, while the
other reflects the job’s importance. Thus, we have an abstract
definition of a utility function as follows
Here, func is an arbitrary function. The function has an arbi-
trary set of arguments that some determines the job’s urgency
while others reflect the jobs importance. Job waiting time (qi)
is a job attribute directly indicating urgency. Considering the
influence of job length, we usually use the ratio of job waiting
time to the job wall time (qi/ti) to represent the urgency. This
value increases as jobs wait and captures the fact that, for
example, waiting an hour before running is more painful for
a 10-minute job than for a 6-hour job. We usually refer to
this ratio as “unitless waiting time.” The arguments indicating
importance can be any job attributes reflecting system owner’s
needs. For example, user name and project name can be used
if a certain project or user is considered really important at a
certain period of time. Job scale (number of computing nodes)
can also be used as an argument reflecting importance, since
sometimes the system owner may consider large job more
One example function is defined as follows,
Si= (qi/ti)3× ni
The first part (qi/ti)3adopts a nonlinear “unitless waiting
time” to represent job urgency, and the second part niindicates
job importance regarding its scale. This function suggests that
jobs get increasingly high utility scores the longer they wait,
especially for shorter and larger jobs. Because of the cube
operator, the weight of job urgency grows faster than that of
the job importance when the waiting time is larger than the
wall time. Generally speaking, this utility function attempts to
achieve low average response time and avoid starvation of
large jobs. The utility function is resilient to user’s abuse.
Since a job will be killed at wall time expiration, the user
may not fail the system by requesting a much shorter wall
time than needed for higher priority.
Our utility-based scheduling framework is compatible with
all the existing scheduling policies since they can be described
in the form of utility functions. Our design goal is not to give
up existing scheduling policies but to provide more possibility
and flexibility for system owners to devise scheduling policies
according to their amorphous needs.
When devising utility functions, selecting arguments and
appropriately combining them are challenging tasks. A feasible
approach is to define a tentative function and then use a high-
fidelity scheduling simulator such as Qsim to measure the
impact of the scheduling policy on system workloads. The
simulation results can be used to refine the function. In Section
VI, real system workloads will be used to demonstrate the
effectiveness of the cited example utility function and others.
B. Fault-Aware Job Allocation
Once the candidate list is determined, the scheduler moves
on to map the candidate jobs to suitable resources. In order
to facilitate resource allocation, allocation cost is used to
calculate a penalty caused by placing a job on different
resources. The goal is to minimize allocation cost. Here,
penalty may come from two sources.
First, the Blue Gene architecture places several restrictions
on node allocation. For this reason, two identically sized
partitions may have very different allocation costs; one might
fit will with existing partitions while another prevents still-
free resources from being usable in multi-midplane partitions.
Moreover, the same partition may have a different allocation
cost depending on the state of existing partitions on the
system. Second, any partition is subject to failure. A fault-
aware scheduler can place jobs where they are least likely to
fail, minimizing the likelihood of the failure of a high-priority
A four-step method is employed to calculate allocation cost.
1) Find appropriately sized partitions: First we determine
the smallest partition on the system that can accommodate the
job. For example, if a job requests 500 nodes, all the idle 512-
node partitions are considered. If multiple candidate partitions
are available, the next two steps are used to choose between
2) Calculate partition failure probability: Next, we esti-
mate likelihood of failure for each candidate partition during
the job execution period. Specifically, for each job i, we
define Pf(X) as failure probability of the partition X during
the period of time between the job start and the wall time
expiration. Obviously, the partition with lower value is more
3) Calculate allocation footprint: Identically sized parti-
tions can have different impact on a partially allocated system;
we refer to this as the allocation footprint. Part of this quantity
is derived from a combination of location of the partition
and the current allocation status of the machine. When fault
prediction information is added, the predicted failure state of
related resources, such as adjacent midplanes or midplanes that
share wiring resources, must also be considered. To this end,
we scale the footprint using fault predictor data. For example,
blocking the use of a partition with 0.50 chance of failure
costs less than blocking a similarly sized partition with an
0.05 chance of failure. We use BLK(X) denote the footprint
without considering failure chance, as described in Figure 1,
and use BLKf(X) represent the footprint considering failure
chance. For example, to block a partition with failure chance
0.2 contributes only 0.8 into the footprint value BLKf(X).
4) Calculate overall allocation cost: Finally, we calculate
the aggregate allocation cost of placing the job on a partition
X as follows
Allocation Cost(X) = αBLKf(X) + βPf(X)
The parameters α and β, which adjust the relative weights
of these allocation costs, can be set by system managers.
In our experiments, we have set both parameters to 1.0. For
example as shown in Figure 1, for a job requesting 512 nodes,
partition A is the best choice since it is failure free and
blocks least parent partitions. Further, we assume a later job
needs to choose between partition E and G, which have the
same failure probability (0.05) and will block same number of
parent partitions. In this situation, the failure probabilities of
their neighbor partitions make a difference. In this example,
partition G is preferred because it blocks a partition (GH)
with higher probability while preserving the more reliable one
– H) contains 512 nodes. The dashed-line boxes illustrate larger partitions.
Assume that the partition B is already occupied and an incoming job is
requesting 512 nodes. All the partitions (A - H), except for B, are best-
fit partitions for the job. Besides these 512-node partitions, larger partitions
are AB, CD, EF, GH, ABCD, EFGH, and ABCDEFGH. The
occupancy of the partition B indicates that its parent partitions AB, ABCD,
and ABCDEFGH are blocked from job allocation. BLK(X) is defined
as the number of available partitions blocked by placing a job on partition
X. Thus, (1) BLK(A) = 0, since all the parent partitions of A (i.e.,
AB, ABCD, and ABCDEFGH) are already blocked by the partition B,
and placing the incoming job on A will not block any additional partitions,
(2) BLK(C) = BLK(D) = 1, where the blocked partition is CD, (3)
BLK(E) = BLK(F) = 2, where the blocked partitions are EF and
EFGH, and (4) BLK(G) = BLK(H) = 2, where the blocked partitions
are GH and EFGH.
Partition example in Blue Gene/P systems. Each labeled partition (A
V. QSIM: COBALT SIMULATOR
Simulation is an integral part of our work for evaluating
utility-based job selection and fault-aware job allocation, as
well as their aggregate effect on system performance. Job
execution is influenced not only by the scheduling policy used
by the job scheduler but also by the users themselves. In order
for a site to evaluate a new scheduling policy, it is desirable
to get some idea of how the new policy will affect system
performance. Historical data from a site’s actual workloads
can be used to approximate what scheduling would be like
under the policy being evaluated.
Motivated by this situation, we have built Qsim, an event-
driven scheduling simulator for Cobalt, to evaluate the fault-
aware, utility-based scheduling presented in the previous sec-
tion. Using real-world workloads from Intrepid, we can exam-
ine how performance metrics such as average response time
and bounded slowdown are affected by different scheduling
policies. In addition, we can study the impact of hardware
and software failures on system performance and how much
improvement is possible by adopting fault-aware job allocation
In addition to being used to compare scheduling policies,
Qsim’s integration with Cobalt means that changes to the
production utility function need not involve untested code.
That is, by virtue of having used Qsim to generate simulation
data, the utility function has been evaluated on thousands of
jobs. Thus, we have ample opportunity to test utility functions
and correct any faults or failures in the utility function before
it is deployed.
Figure 2 presents the major components of Qsim. Simi-
lar to Cobalt, Qsim comprises three components: a queue
manager that maintains queued job lists, a system manager
that maintains system hardware status, and a scheduler that
makes scheduling decision. The scheduler in the figure is the
unmodified Cobalt scheduler component. These components
have the same internal interfaces as those used in Cobalt.
The major difference is that Qsim reads job input from a
historical workload file, whereas Cobalt gets jobs through real
user commands. Moreover, Qsim can inject failures by parsing
a synthetic failure log. Qsim is available as an open-source
tool, included with the Cobalt release .
Fig. 2.Qsim components and interfaces.
Figure 3 demonstrates how Qsim works. Qsim reads job
arrival times, job execution time, and system failure events as
inputs. At initialization, Qsim determines a list of times where
scheduling decisions may need to be made. This list initially
consists of the times when new jobs arrive; however, over time,
job completion times are added into this same collection.
Qsim advances the clock between the time stamps in this
list. At each one, Qsim updates job and partition states based
on the workload and system activity. This information is
consumed by the scheduler, which determines whether any
new jobs should be executed.
Upon job start, Qsim determines the completion time of
the job. If Qsim is configured to simulate faults, it determines
whether the job will succeed or fail. Different times stamps
are inserted into the time stamp collection in these cases;
succeeding jobs have their completion time inserted, while
failing jobs have their failure time inserted. Failing jobs are
then reinserted into the waiting queue when the failure time
Qsim logs all job events (submission, start, end, failure) into
the output log, which can be used to compute the quantitative
metrics such as response times and slowdown.
Fig. 3.Qsim simulation example.
We used Qsim to evaluate the effectiveness of the schedul-
ing with utility functions and the fault-aware allocation.
Specifically, using the real workload from 40-rack production
Intrepid, we conducted simulations for a set of utility functions
and examined how they influence the scheduling performance.
We also evaluated the benefits of combining utility functions
with fault-aware allocation.
A. Experimental Configuration
1) Utility Functions: Table II summarizes the utility func-
tions used in the experiments. FCFS is the utility function
form of first-come first-served, a commonly used scheduling
policy. We propose smarter utility functions mainly based on
three elements: job age (qi), job length (ti), and job scale
(ni). All these proposed utility functions favor older jobs and
shorter jobs. Favoring old jobs is to guarantee the fairness
regarding job arrival order, and favoring short jobs can achieve
better average waiting/response time. The utility functions
differ in their attitudes toward the job scale and the weights
used to balance between the job length and the job scale. As
DESCRIPTION OF UTILITY FUNCTIONS
Si= (qi/ti) · (ni/ns)3
Si= (qi/ti) · ni
Si= (qi/ti)3· ni
Si= qi/(log2(ni) ∗ ti)
first come first served
favors large jobs, then old/short job
favors old/short jobs, avoiding large job starvation
favors old/short jobs more, avoiding large job starvation
favors old/short jobs, regardless job scale
provides fast turnaround for small jobs
shown in the table, using FAT, large jobs are favored most
because a cube operator is imposed on the job scale. WFP1
and WFP3 also care about large jobs, but the weights on the
job scale decrease as the cube is added on the first factor.
FCSJ (i.e., first-coming short jobs) does not take the job scale
into account. UNICEF, however, goes to the other extreme: it
favors the small jobs. In terms of the scheduling goals, FCFS
consider only the fairness regarding job arrival time; the other
five not only consider the arrival fairness, but also aim to lower
job average waiting time and slowdown. Meanwhile, UNICEF
aims to provide fast turnaround for small jobs; FAT and WFPs
attempt to avoid the starvation of large-scale jobs. Note that
all of above scheduling polices are supported with the same
In our experiments, we seek to see whether smart utility
functions are better than FCFS and how these utility functions
influence the performance metrics, thereby achieving their
scheduling goals. Besides simulating all the utility functions
under the failure-free condition, we measure some of them
combined with fault-aware job allocation under failure-present
2) Workload Characteristic: We used a job trace from
Intrepid after its 40 racks went into full production in January
2009. The workload covers 35 days and contains 7,630 jobs,
with average and median running time 4457 and 3075 seconds,
respectively. The maximum job size is 32,768 nodes and the
median size is 512 nodes. We use the 40-rack partition con-
figuration in the simulation, with a minimum partition size of
64 nodes. To provide more insight into the simulation results,
we classify the jobs into various categories based on the job
length (running time) and job scale (number of computing
nodes). Based on job length, we have four categories: Very
Short, Short, Long, and Very Long. Based on job scale, we
also have four categories: Very Small, Small, Large, Very
Large. For simplicity, we use one-dimensional classifying for
either job length or job scale. Tables III and IV summarize
the categories, together with the criteria and distribution of
each category. In Table IV, considering partition restrictions,
we count, for example, the size of a job requesting 1000 nodes
as 1024, which is the size of the smallest partition that can
accommodate the job.
3) Failure Model and Prediction Mechanism: Since natural
failures are hard to trigger on demand, we emulate failure
events using a Weibull distribution, which produces realistic
failure behaviors based on recent studies of production systems
. By tuning the scale parameter, failure events can
JOB LENGTH CATEGORY, CRITERIA, AND DISTRIBUTION
Time of Running
JOB SCALE CATEGORY, CRITERIA, AND DISTRIBUTION
No. of Nodes
be generated in a reasonable range. Before simulation begins,
we generate failure event lists for all partitions. We assume
the system failure is transient. That is, when the job fails, the
partition is available for allocating to other queued jobs after
a reboot time (20 minutes). We also assume that when a job
fails, it is automatically resubmitted to the waiting queue and
will run from the original beginning when it gets the chance.
Much progress has been made in failure analysis and predic-
tion. For example, hardware sensors are commonly deployed
in modern computer systems for early detection of hardware
errors , and a variety of predictive techniques have
been devised to learn fault patterns for failure prediction
. In a previous study, we developed a dy-
namic meta-learning mechanism for failure prediction .
The detailed discussion of failure prediction is beyond the
scope of this paper, and interested readers can refer to our
previous papers for details. Failure prediction is typically
described by two metrics. sensitivity, defined as
measures the proportion of correct failure predictions to the
number of actual failures. specificity, defined as
measures the proportion of correct nonfaulty predictions to
the number of actual nonfaulty cases. Here, TP, FP, FN, and
TN denote the number of true positives, false negatives, false
positives, and true negatives, respectively.
Specifically, we use a Weibull distribution to model failure
arrivals on each 512-node partition. At each scheduling point,
based on the failure events generated, Qsim calculates the
failure probability Pf(X) for each partition as follows. If there
is a failure in the failure list on partition X before the job’s
expected completion time, Pf(X) = sensitivity. Otherwise,
Pf(X) = 1 − specificity. Our prediction simulation scheme
is similar with the one presented in  except for one
enhancement that we consider the existence of false alarms.
B. Evaluation Metrics
In the experiment we used the following metrics, which
represent either performance or reliability,
• Average waiting time. The job waiting time is the time
period between the job’s arrival time and the time of job
• Average response time (RESP). Job response time is
also called job turnaround time, representing the time
period between job’s arrival and successful completion.
It includes waiting time and running time of the job if
no failure interrupts the job.
• Average bounded slowdown (BSD). The slowdown of
a job is the ratio of the job’s response time to its
actual running time. Usually, a small running time bound
(10 seconds) is imposed to avoid the value skewed by
extremely small jobs. The lengths of jobs in our job trace
are all more than 10 seconds, so what we referred as
slowdown in later text is the same as bounded slowdown.
• Job failure rate (JFR). JFR is defined as the ratio between
the number of failed jobs and the total number of jobs
submitted. It reflects the percentage of jobs that are
interrupted by failures and is an important indicator of
system’s quality of service.
• Service unit loss rate (SULR). SULR is defined as the
ratio of wasted service units (i.e., product of job running
hours and number of computing nodes) to the entire
service units in a given time span. This metric directly
indicates the percentage of computing cycles lost due
to failures. It is an important metric for both system
managers and users.
Average waiting time and response time are actually linearly
related. Thus we use only waiting time under failure-free
conditions since it is more illustrative when comparing the
value on different job length categories. And we use only
response time under failure-present condition because it covers
the time loss in the case of failure.
1) Effect of Utility Functions: We first conducted simula-
tions with different utility functions under failure-free con-
dition. Figure 4 illustrates the overall results. The x-axis
indicates different utility functions. The y-axis shows the
average performance results among all the jobs. As shown
in the figure, the average waiting times range from 3560 to
7882 seconds and the average slowdowns are in the range
4.38–9.7. For both metrics, FCFS performs the worst. This
means that the smart utility functions can improve system
performance against FCFS. The relative performance gains on
average waiting time are between 13.4% (WFP1) and 54.8%
(FCSJ), with a median value of 25.7% (WFP3); the relative
gains on average BSD are between 11.4% (WFP1) and 54.8%
(FCSJ), with a median value 36.1% (WFP3).
Because of the skewed distribution, merely measuring the
average values does not suffice in presenting the scheduling
performance. As a supplement, Figure 5 presents more detailed
Fig. 4.Overall performance comparison of all utility functions.
distribution of the performance values. As shown in the figure,
under every scheduling policy, large amount of jobs can start
immediately at submission (i.e., waiting time is zero and
slowdown is 1). There are also very few jobs enduring very
long waiting; they either have waiting time larger than 10
hours or slowdown larger than 100. The proportion of zero-
waiting jobs is nearly 50% for FCFS and around 55% for
other utility functions. The long waiting jobs are less than
5% for each scheduling policy, and the FCFS also has a
comparatively larger number. The distributions demonstrate in
another perspective that the smart utility functions are better
than FCFS in eliminating the number of long-waiting jobs and
increasing the number of zero-waiting jobs.
of the number of jobs with performance value in a certain range to the total
number of jobs.
Performance value distributions. The y-axis shows the percentage
Next we explored how the utility function influences the
performance. Figure 6 illustrates the performance variation by
different job categories, under various scheduling policies.
The results show that smart utility functions achieve better
performance than FCFS only for relatively short jobs. As
shown in Figure 6 (a and b), for short and very short jobs
the smart utility functions are noticeably better than FCFS in
lowering the average waiting time and slowdown. For long
and very long jobs, this is not the case; on the contrary, the
FCFS is relatively good regarding the average waiting time of
very long jobs. Thus, the overall waiting time and slowdown
improvement of smart utility functions against FCFS comes
from the improvement on comparatively short jobs.
The results also indicate that the average slowdown of
shorter jobs is significantly larger than that of longer ones.
As shown in Figure 6 (b), For very short jobs the average
slowdown is between 11.5 and 30, dramatically larger than
that of other categories, in 2.9–6.5, 1.9–2.3, and 1.2–1.3, re-
spectively. In other words, no matter what scheduling policy is
used, comparatively short jobs dominate the average slowdown
metric. Hence, achieving lower average slowdown requires
providing fast turnaround for short jobs.
We noted that UNICEF achieves its scheduling goal for
benefiting small jobs but does harm for very large jobs. As
shown in Figure 6 (c and d), the average waiting times increase
strictly (1038, 3420, 4535, and 11928) as the jobs get larger.
A similar trend is shown for average slowdown. Notably, by
using UNICEF, the average waiting time for very small jobs
(1038 s) is remarkably less than all the others, lower by
85% than that of FCFS and by 88% than that of the worst
case (WFP1). UNICEF effectively provides small jobs fast
turnaround. This benefit, however, comes from the sacrifice of
large jobs; indeed, for very large jobs, UNICEF performs the
worst among all.
FAT achieves its preference for large-scale jobs but its
overall performance is not good. As shown in Figure 6 (c and
d), FAT achieves lower average waiting time and slowdown
for larger jobs than smaller jobs. The average waiting times
are strictly decreased (8576, 6124, 5193, and 4115) as the jobs
get larger in category. The average slowdowns have a similar
trend. Compared with other utility functions, FAT achieves the
best performance for very large jobs. But aside from that, the
overall performance of FAT is not good. Therefore, FAT is not
considered as a practical utility function.
FCSJ performs better than WFP1 in most cases, except for
very large jobs. This means that by avoiding starvation of
large jobs, WFP1 sacrifices some interests of small jobs, even
some short jobs. On the contrary, FCSJ focus on only job
length, regardless of the job scale, so it can achieve overall
good performance more easily. WFP3 compromises between
those two; it achieves better overall performance than WFP1
and is also better than FCSJ for very large jobs. Considering
that large-scale jobs are usually important jobs in our system,
we tend to give them more priority. Therefore, although FCSJ
performs better in most cases, we prefer WFP3 when selecting
the default utility function to deploy on-site.
We conclude that utility functions are effective in achieving
their scheduling goals and concerns. Specifically, smart utility
functions achieve overall better performance than FCFS. FAT
and UNICEF achieve their concerns on job scale. FCSJ
performs the best for most job categories but is beaten by
WFP1 and WFP3 for large-scale jobs. WFP3 achieves overall
good performance also for large-scale jobs. Considering the
importance of large-sale jobs, we deploy WFP3 as the default
utility function on Intrepid. UNICEF is also considered as
an alternative under some special conditions when small jobs
(e.g., development jobs) should have fast respond.
2) Benefit of Fault-Aware Job Allocation: We also mea-
sured the impact of failure on systems and evaluated the ef-
fectiveness of the fault-aware job allocation. By tuning Weibull
parameters, we generated a set of failure event sequences
corresponding to different system failure rates. By setting the
Weibull shape parameter as 1.0, and tuning the Weibull scale
parameter from 1 × 106to 6 × 106, the system-wide MTBF
Fig. 6. Performance value by job categories. (a)(b) categorize jobs by length
and (c)(d) categorize jobs by job scale, based on the criteria described in
Table III and IV. For each category, the performance values of all the utility
functions are compared.
is tuned in the range of 4 to 20 hours. Because the 40-rack
Intrepid is new in production, we do not yet have steady-
state production failure rates for the system. So, for this work,
we used moderate to high failure rates for a system of this
size based on several recent studies of productions systems
. We choose the sensitivity and specificity both
as 0.6, a moderate value in previous study .
With each failure sequence, we run simulations with fault-
aware allocation and without fault-aware allocation to evaluate
their impacts. For convenience, we denote the allocation
policies without fault awareness as “ordinary allocation.” We
first use our default scheduling utility function WFP3. Figure
7 illustrates the simulation results. In each chart, the upper
lines represent the performance value of the scheduling with
ordinary allocation while the lower lines indicate the case with
fault-aware allocation. The performance lines fluctuate up as
the failure rate gets higher, meaning that system failures de-
grade system performance. The generally lower “fault-aware”
lines suggest that fault-aware allocation mitigates the failure
impact on system performance.
Specifically, at the highest failure rate (MTBF=4 hr), the
average response time of ordinary allocation goes up to 11790,
degraded by 14.35% as against failure-free case; the fault-
aware allocation decreases the maximum response time and
the degrading percentage to 11003 and 6.72%, respectively.
Average slowdown has the same trend. For ordinary allocation,
the value goes up to 9.67, while the fault-aware allocation
maintains it up to 8.73. Job failure rate increases up to
1.05% and 0.83% for the two allocation policies, respectively.
Without fault-aware allocation, the service unit loss can be up
to 14.8%, which is a dramatic figure that cannot be ignored
by system owners. The fault-aware allocation decreases the
highest service unit loss rate to 11.79%.
Fig. 7. Failure-present simulation using WFP3. The infinity sign ∞ indicates
an ideal case – a failure-free environment. “Ordinary” means using ordinary
allocation scheme without failure awareness. “Fault-Aware” means using fault-
aware job allocation.
As shown in Figure 7, the trend of job failure rates is strictly
increased by system failure rate, while other metrics show
some volatility at the low failure rates. First, we observe that
the performance degradations on response time and slowdown
are not obvious when system failure rates are low, suggesting
that with a few failures in the system, the average response
time is not significantly impacted. On the contrary, it could be
even better than the failure-free case (e.g., average response
time at MTBF=20 is smaller than that at ∞) because of the
uncertainty of the scheduling behavior; the failure interrupts
of some long or large jobs could bring more chances for
other long-waiting jobs. Second, in a few cases fault-aware
allocation is not better than ordinary allocation for the per-
formance metrics such as average response time and average
slowdown, especially under low failure rate. One example is
at the failure rate (MTBF=12) in the upper two charts. For
reliability metrics, however, fault-aware allocation is always
better than the ordinary one.
We also conducted similar experiments using FCFS, since
it is a widely used job scheduling policy. Figure 8 shows
the simulation results. The performance trends are similar to
those using WFP3, except that the absolute values of average
response time and slowdown are higher than that of WFP3.
Hence, the benefit brought by smart utility functions as against
FCFS applies to the failure-present condition. The job failure
rate and service unit loss rate, however, do not show any
gap with that using WFP3. The reason may be that the two
reliability metrics are not sensitive to scheduling policies but
depend mainly on system failure rate, failure location, and
job allocation. Overall, we can observe that, using FCFS, the
system performance degrades as the system failure rate gets
higher and that the fault-aware job allocation mitigates the
impact of failures.
Fig. 8. Failure-present simulation using FCFS. The infinity sign ∞ indicates
an ideal case – a failure-free environment. “Ordinary” means using ordinary
allocation scheme without failure awareness. “Fault-Aware” means using fault-
aware job allocation.
Figure 9 presents the relative gains brought by fault-aware
job allocation compared to the ordinary method, for WFP3
and FCFS respectively. As shown in the figure, the gains
on average response time and slowdown are comparatively
modest. For WFP3, the response time gain is up to 6.68%,
and the slowdown gain is up to 9.72%. For FCFS, the values
are up to 5.03% and 7.47%, respectively. The gains on JFR
and SULR are more significant. For WFP3, the gains are
between 17.24% and 23.77% on JFR and between 17.96%
and 31.21% on SULR; FCFS also achieves relative gains of
16.66%–34.45% and 18.11%–38.94% on these two metrics.
ordinary job allocation, using scheduling policy WFP3 and FCFS, respectively.
Relative gains brought by fault-aware job allocation as against
In summary, the ranges of relative gains suggest that fault-
aware allocation can significantly improve the reliability met-
rics as against ordinary allocation scheme, while the perfor-
mance metrics, such as average response time and slowdown,
depend more heavily on utility functions. Collectively, to
achieve overall good system-wide performance, we need well-
designed utility functions to achieve better job scheduling per-
formance and fault-aware allocation to achieve high reliability
of jobs and to mitigate unnecessary loss of computing resource
caused by system faults.
In this paper, we have presented a flexible utility-based
scheduling framework. More specifically, the utility-based job
selection mechanism enables system owners to customize
scheduling policies under different circumstances without
changing the scheduler code. The fault-aware job allocation
mechanism allows the scheduler to intelligently map user jobs
onto suitable partitions by considering the failure possibility
of system components. Furthermore, we have developed a new
event-driven scheduling simulator, Qsim, which can be directly
used with the Cobalt resource manager. We have demonstrated
the effectiveness of the integrated facilities by comprehensive
experiments with real job logs collected from the 40-rack Blue
Gene/P system at Argonne National Laboratory.
While this study can directly benefit the system management
of the Blue Gene/P systems, it has some limitations that remain
as our future work. For example, we are designing more utility
functions and measuring them on more job traces. Further
study and extensive experiments will be conducted to explore
the complicated relationship between utility functions, work-
load characteristic, and the performance metrics. Ultimately,
we plan to integrate Cobalt with our previous work on failure
This work is supported in part by US National Science
Foundation grants CNS-0834514, CNS-0720549, and CCF-
0702737. The work at Argonne National Laboratory is sup-
ported by the Office of Advanced Scientific Computing Re-
search, Office of Science, U.S. Department of Energy, under
Contract DE-AC02-06CH11357. This research uses resources
of the Argonne Leadership Computing Facility.
 Cobalt project web site, http://trac.mcs.anl.gov/projects/cobalt.
 Blue Gene/P red books, http://www.redbooks.ibm.com/abstracts/sg247417.html.
 B. Allen, “Monitoring Hard Disk with SMART,” Linux Journal, 2004.
 F. Chen, “A Utility-based Approach to Scheduling Multimedia Streams
in Peer-to-Peer Systems,” Proc. of the 18th International Parallel and
Distributed Processing Symposium (IPDPS’04), 2004.
 DOE INCITE project web site, http://www.sc.doe.gov/ascr/incite.
 N. Desai, E. Lusk, D. Buettner, A. Cherry, and Theron Voran, “Simulating
Failures on Large-Scale Systems,” International Conference on Parallel
Processing - Workshops (ICPPW’08), 2008.
 Hardware Monitoring by LM Sensors, http://secure.netroedge.com/-
 Health API, http://www.renci.org.
 J. Gu, Z. Zheng, Z. Lan, J. White, E. Hocks, and B. Park, “Dynamic Meta-
Learning for Failure Prediction in Large-Scale Systems: A Case Study,”
Proc. of International Conference on Parallel Processing (ICPP’08), 2008.
 P. Gujrati, Y. Li, Z. Lan, R. Thakur, and J. White, “A Meta-Learning
Failure Predictor for Blue Gene/L Systems,” Proc. of International Con-
ference on Parallel Processing (ICPP’07), 2007.
 D. Jackson, Q. Snell, and M. Clement, “Core Algorithms of the Maui
Scheduler,” International Workshop on Job Scheduling Strategies for
Parallel Processing (JSSPP’01), 2001.
 C. Lee and A. Snavely, “Precise and Realistic Utility Functions for User-
Centric Performance Analysis of Schedulers,” Proc. of IEEE Symposium
on High-Performance Distributed Computing (HPDC’07), 2007.
 Y. Li, Z. Lan, P. Gujrati, and X. Sun, “Fault-Aware Runtime Strategies
for High Performance Computing,” IEEE Transactions on Parallel and
Distributed Systems, vol. 20, no. 4, pp. 460-473, 2009.
 Y. Liang, Y. Zhang, A. Sivasubramaniam, M. Jette, and R. Sahoo,“Blue
Gene /L Failure Analysis and Models,” Proc. of DSN’06, 2006.
 K. Limaye, C. Leangsuksun, and A. Tikotekar, “Fault Tolerance En-
abled HPC Scheduling with HA-OSCAR Framework,” Proc. of the High
Availability and Performance Workshop, 2005.
 Maui project web site, http://mauischeduler.sourceforge.net.
 A. Oliner, R. Sahoo, J. Moreira, and M. Gupta, “Fault-aware Job
Scheduling for BlueGene/L Systems,” Proc. of the 18th International
Parallel and Distributed Processing Symposium (IPDPS’04), 2004.
 A. Oliner and J. Stearly, “What Supercomputers Say: A Study of Five
System Logs,” Proc. of DSN’07, 2007.
 TOP500 Super Computing Web Site, http://www.top500.org.
 R. Sahoo, A. Oliner, I. Rish, M. Gupta, J. Moreira, S. Ma, R. Vilalta, and
A. Sivasubramaniam, “Critical Event Prediction for Proactive Management
in Large-scale Computer Clusters,” Proc. of International Conference on
Knowledge Discovery and Data Mining, 2003.
 B. Schroeder and G. A. Gibson, “A Large-scale Study of Failures in
High Performance Computing Systems,” Proc. of DSN’06, 2006.
 A. Tikotekar, G. Vallee, T. Naughton, S. Scott, C. Leangsuksum,
“Evaluation of Fault-Tolerant Policies Using Simulation,” Proc. of IEEE
 K. Trivedi and K. Vaidyanathan, “A Measurement-based Model for
Estimation of Resource Exhaustion in Operational Software Systems,”
Proc. of ISSRE’99, 1999.
 D. Vengerov, L. Mastroleon, D. Murphy, and N. Bambos, “Adaptive
Data-Aware Utility-Based Scheduling in Resource-Constrained Systems,”
Research Disclosure, No. 513, pp. 38-39, 2007.
 R. Vilalta and S. Ma, “Predicting Rare Events in Temporal Domains,”
Proc. of ICDM’02, 2002.
 G. Weiss and H.Hirsh, “Learning to Predict Rare Events in Eevent
Sequences,” Proc. of SIGKDD, 1998.
 Y. Zhang, M. Squillante, A. Sivasubramaniam and R. Sahoo, “Per-
formance Implications of Failures in Large-Scale Cluster Scheduling,”
Proc. of Workshop on Job Scheduling Strategies for Parallel Processing
 Z. Zheng, Y. Li, and Z. Lan, “Anomaly Localization in Large-Scale
Clusters,” Proc. of IEEE Cluster’07, 2007.