11 research outputs found
Invariance of fluid limits for the Shortest Remaining Processing Time and Shortest Job First policies
We consider a single-server queue with renewal arrivals and i.i.d. service
times, in which the server employs either the preemptive Shortest Remaining
Processing Time (SRPT) policy, or its non-preemptive variant, Shortest Job
First (SJF). We show that for given stochastic primitives (initial condition,
arrival and service processes), the model has the same fluid limit under either
policy. In particular, we conclude that the well-known queue length optimality
of preemptive SRPT is also achieved, asymptotically on fluid scale, by the
simpler-to-implement SJF policy. We also conclude that on fluid scale, SJF and
SRPT achieve the same performance with respect to response times of the
longest-waiting jobs in the system.Comment: 24 page
Fluid Limits for Shortest Job First with Aging
We investigate fluid scaling of single server queueing systems under the
shortest job first with aging (SJFA) scheduling policy. We use the
measure-valued Skorokhod map to characterize the fluid limit for SJFA queues
with a general aging rule and establish convergence results to the fluid limit.
We treat in detail examples of linear and exponential aging
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Parallel job scheduling policies to improve fairness : a case study.
Balancing fairness, user performance, and system performance is a critical concern when developing and installing parallel schedulers. Sandia uses a customized scheduler to manage many of their parallel machines. A primary function of the scheduler is to ensure that the machines have good utilization and that users are treated in a 'fair' manner. A separate compute process allocator (CPA) ensures that the jobs on the machines are not too fragmented in order to maximize throughput. Until recently, there has been no established technique to measure the fairness of parallel job schedulers. This paper introduces a 'hybrid' fairness metric that is similar to recently proposed metrics. The metric uses the Sandia version of a 'fairshare' queuing priority as the basis for fairness. The hybrid fairness metric is used to evaluate a Sandia workload. Using these results, multiple scheduling strategies are introduced to improve performance while satisfying user and system performance constraints
Asymptotic Convergence of Scheduling Policies with Respect to Slowdown
We explore the performance of an M/GI/1 queue under various scheduling policies from the perspective of a new metric: the slowdown experienced by the largest jobs. We consider scheduling policies that bias against large jobs, towards large jobs, and those that are fair, e.g., processor-sharing (PS). We prove that as job size increases to infinity, all work conserving policies converge almost surely with respect to this metric to no more than 1/(1−ρ), where ρ denotes the load. We also find that the expected slowdown under any work conserving policy can be made arbitrarily close to that under PS, for all job sizes that are sufficiently large
Asymptotic Convergence of Scheduling Policies with Respect to Slowdown
We explore the performance of an M/GI/1 queue under various scheduling policies from the perspective of a new metric: the slowdown experienced by largest jobs. We consider scheduling policies that bias against large jobs, towards large jobs, and those that are fair, e.g., Processor-Sharing. We prove that as job size increases to infinity, all work conserving policies converge almost surely with respect to this metric to no more than 1=(1 \Gamma ae), where ae denotes load. We also find that the expected slowdown under any work conserving policy can be made arbitrarily close to that under Processor-Sharing, for all job sizes that are sufficiently large
Scheduling for today’s computer systems: bridging theory and practice
Scheduling is a fundamental technique for improving performance in computer systems. From web servers
to routers to operating systems, how the bottleneck device is scheduled has an enormous impact on the performance of the system as a whole. Given the immense literature studying scheduling, it is easy to think that we already understand enough about scheduling. But, modern computer system designs have highlighted a number of disconnects between traditional analytic results and the needs of system designers.
In particular, the idealized policies, metrics, and models used by analytic researchers do not match the policies, metrics, and scenarios that appear in real systems.
The goal of this thesis is to take a step towards modernizing the theory of scheduling in order to provide
results that apply to today’s computer systems, and thus ease the burden on system designers. To accomplish
this goal, we provide new results that help to bridge each of the disconnects mentioned above. We will move beyond the study of idealized policies by introducing a new analytic framework where the focus is on scheduling heuristics and techniques rather than individual policies. By moving beyond the study of individual policies, our results apply to the complex hybrid policies that are often used in practice. For example, our results enable designers to understand how the policies that favor small job sizes are affected by the fact that real systems only have estimates of job sizes. In addition, we move beyond the study of mean response time
and provide results characterizing the distribution of response time and the fairness of scheduling policies.
These results allow us to understand how scheduling affects QoS guarantees and whether favoring small job sizes results in large job sizes being treated unfairly. Finally, we move beyond the simplified models traditionally used in scheduling research and provide results characterizing the effectiveness of scheduling in multiserver systems and when users are interactive. These results allow us to answer questions about the how to design multiserver systems and how to choose a workload generator when evaluating new scheduling designs