41 research outputs found
Optimal Scheduling in the Multiserver-job Model under Heavy Traffic
Multiserver-job systems, where jobs require concurrent service at many
servers, occur widely in practice. Essentially all of the theoretical work on
multiserver-job systems focuses on maximizing utilization, with almost nothing
known about mean response time. In simpler settings, such as various known-size
single-server-job settings, minimizing mean response time is merely a matter of
prioritizing small jobs. However, for the multiserver-job system, prioritizing
small jobs is not enough, because we must also ensure servers are not
unnecessarily left idle. Thus, minimizing mean response time requires
prioritizing small jobs while simultaneously maximizing throughput. Our
question is how to achieve these joint objectives.
We devise the ServerFilling-SRPT scheduling policy, which is the first policy
to minimize mean response time in the multiserver-job model in the heavy
traffic limit. In addition to proving this heavy-traffic result, we present
empirical evidence that ServerFilling-SRPT outperforms all existing scheduling
policies for all loads, with improvements by orders of magnitude at higher
loads.
Because ServerFilling-SRPT requires knowing job sizes, we also define the
ServerFilling-Gittins policy, which is optimal when sizes are unknown or
partially known.Comment: 32 pages, to appear in ACM SIGMETRICS 202
SRPT Scheduling Discipline in Many-Server Queues with Impatient Customers
The shortest-remaining-processing-time (SRPT) scheduling policy has been extensively studied, for more than 50 years, in single-server queues with infinitely patient jobs. Yet, much less is known about its performance in multiserver queues. In this paper, we present the first theoretical analysis of SRPT in multiserver queues with abandonment. In particular, we consider the M/GI/s+GI queue and demonstrate that, in the many-sever overloaded regime, performance in the SRPT queue is equivalent, asymptotically in steady state, to a preemptive two-class priority queue where customers with short service times (below a threshold) are served without wait, and customers with long service times (above a threshold) eventually abandon without service. We prove that the SRPT discipline maximizes, asymptotically, the system throughput, among all scheduling disciplines. We also compare the performance of the SRPT policy to blind policies and study the effects of the patience-time and service-time distributions
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
Performance of the Gittins Policy in the G/G/1 and G/G/k, With and Without Setup Times
How should we schedule jobs to minimize mean queue length? In the preemptive
M/G/1 queue, we know the optimal policy is the Gittins policy, which uses any
available information about jobs' remaining service times to dynamically
prioritize jobs. For models more complex than the M/G/1, optimal scheduling is
generally intractable. This leads us to ask: beyond the M/G/1, does Gittins
still perform well?
Recent results indicate that Gittins performs well in the M/G/k, meaning that
its additive suboptimality gap is bounded by an expression which is negligible
in heavy traffic. But allowing multiple servers is just one way to extend the
M/G/1, and most other extensions remain open. Does Gittins still perform well
with non-Poisson arrival processes? Or if servers require setup times when
transitioning from idle to busy?
In this paper, we give the first analysis of the Gittins policy that can
handle any combination of (a) multiple servers, (b) non-Poisson arrivals, and
(c) setup times. Our results thus cover the G/G/1 and G/G/k, with and without
setup times, bounding Gittins's suboptimality gap in each case. Each of (a),
(b), and (c) adds a term to our bound, but all the terms are negligible in
heavy traffic, thus implying Gittins's heavy-traffic optimality in all the
systems we consider. Another consequence of our results is that Gittins is
optimal in the M/G/1 with setup times at all loads.Comment: 41 page
Performance Analysis of Modified SRPT in Multiple-Processor Multitask Scheduling
In this paper we study the multiple-processor multitask scheduling problem in
both deterministic and stochastic models. We consider and analyze Modified
Shortest Remaining Processing Time (M-SRPT) scheduling algorithm, a simple
modification of SRPT, which always schedules jobs according to SRPT whenever
possible, while processes tasks in an arbitrary order. The M-SRPT algorithm is
proved to achieve a competitive ratio of for
minimizing response time, where denotes the ratio between maximum job
workload and minimum job workload, represents the ratio between maximum
non-preemptive task workload and minimum job workload. In addition, the
competitive ratio achieved is shown to be optimal (up to a constant factor),
when there are constant number of machines. We further consider the problem
under Poisson arrival and general workload distribution (\ie, system),
and show that M-SRPT achieves asymptotic optimal mean response time when the
traffic intensity approaches , if job size distribution has finite
support. Beyond finite job workload, the asymptotic optimality of M-SRPT also
holds for infinite job size distributions with certain probabilistic
assumptions, for example, system with finite task workload
Frequency scaling in multilevel queues
In this paper, we study a variant of PS+PS multilevel scheduling, which we call the PS+IS queue. Specifically, we use Processor Sharing (PS) at both queues, but with linear frequency scaling on the second queue, so that the latter behaves like an Infinite Server (IS) queue. The goals of the system are low response times for small jobs in the first queue, and reduced power consumption for large jobs in the second queue. The novelty of our model includes the frequency scaling at the second queue, and the batch arrival process at the second queue induced by the busy period structure of the first queue which has strictly higher priority. We derive a numerical solution for the PS+IS queueing system in steady-state, and then study its properties under workloads obtained from fitting of TCP flow traces. The simulation results confirm the
SEH: Size Estimate Hedging for Single-Server Queues
For a single server system, Shortest Remaining Processing Time (SRPT) is an
optimal size-based policy. In this paper, we discuss scheduling a single-server
system when exact information about the jobs' processing times is not
available. When the SRPT policy uses estimated processing times, the
underestimation of large jobs can significantly degrade performance. We propose
a simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated
processing times for scheduling decisions. A job's priority is increased
dynamically according to an SRPT rule until it is determined that it is
underestimated, at which time the priority is frozen. Numerical results suggest
that SEH has desirable performance when estimation errors are not unreasonably
large