skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on February 16, 2025

Title: Heavy-Traffic Optimal Size- and State-Aware Dispatching
Dispatching systems, where arriving jobs are immediately assigned to one of multiple queues, are ubiquitous in computer systems and service systems. A natural and practically relevant model is one in which each queue serves jobs in FCFS (First-Come First-Served) order. We consider the case where the dispatcher is size-aware, meaning it learns the size (i.e. service time) of each job as it arrives; and state-aware, meaning it always knows the amount of work (i.e. total remaining service time) at each queue. While size- and state-aware dispatching to FCFS queues has been extensively studied, little is known about optimal dispatching for the objective of minimizing mean delay. A major obstacle is that no nontrivial lower bound on mean delay is known, even in heavy traffic (i.e. the limit as load approaches capacity). This makes it difficult to prove that any given policy is optimal, or even heavy-traffic optimal. In this work, we propose the first size- and state-aware dispatching policy that provably minimizes mean delay in heavy traffic. Our policy, called CARD (Controlled Asymmetry Reduces Delay), keeps all but one of the queues short, then routes as few jobs as possible to the one long queue. We prove an upper bound on CARD's mean delay, and we prove the first nontrivial lower bound on the mean delay of any size- and state-aware dispatching policy. Both results apply to any number of servers. Our bounds match in heavy traffic, implying CARD's heavy-traffic optimality. In particular, CARD's heavy-traffic performance improves upon that of LWL (Least Work Left), SITA (Size Interval Task Assignment), and other policies from the literature whose heavy-traffic performance is known.  more » « less
Award ID(s):
2307008
PAR ID:
10532337
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Volume:
8
Issue:
1
ISSN:
2476-1249
Page Range / eLocation ID:
Article 9
Subject(s) / Keyword(s):
dispatching, FCFS, response time, latency, sojourn time, heavy traffic, asymptotic optimality
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , ACM MobiHoc: International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks)
    null (Ed.)
    In multi-server queueing systems where there is no central queue holding all incoming jobs, job dispatching policies are used to assign incoming jobs to the queue at one of the servers. Classic job dispatching policies such as join-the-shortest-queue and shortest expected delay assume that the service rates and queue lengths of the servers are known to the dispatcher. In this work, we tackle the problem of job dispatching without the knowledge of service rates and queue lengths, where the dispatcher can only obtain noisy estimates of the service rates by observing job departures. This problem presents a novel exploration-exploitation trade-off between sending jobs to all the servers to estimate their service rates, and exploiting the currently known fastest servers to minimize the expected queueing delay. We propose a bandit-based exploration policy that learns the service rates from observed job departures. Unlike the standard multi-armed bandit problem where only one out of a finite set of actions is optimal, here the optimal policy requires identifying the optimal fraction of incoming jobs to be sent to each server. We present a regret analysis and simulations to demonstrate the effectiveness of the proposed bandit-based exploration policy. 
    more » « less
  2. ; ( , Management Science)
    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. This paper was accepted by Baris Ata, stochastic models & simulation. 
    more » « less
  3. ; ( , Queueing Systems)
    Abstract

    Optimal scheduling in single-server queueing systems is a classic problem in queueing theory. The Gittins index policy is known to be the optimal nonanticipating policy minimizing the mean delay in the M/G/1 queue. While the Gittins index is thoroughly characterized for ordinary jobs whose state is described by the attained service, it is not at all the case with jobs that have more complex structure. Recently, a class of such jobs, multistage jobs, were introduced, and it was shown that the computation of Gittins index of a multistage job decomposes into separable computations for the individual stages. The characterization is, however, indirect in the sense that it relies on the recursion for an auxiliary function (the so-called SJP—single-job profit—function) and not for the Gittins index itself. In this paper, we focus on sequential multistage jobs, which have a fixed sequence of stages, and prove that, for them, it is possible to compute the Gittins index directly by recursively combining the Gittins indices of its individual stages. In addition, we give sufficient conditions for the optimality of the FCFS and SERPT disciplines for scheduling sequential multistage jobs. On the other hand, we demonstrate that, for nonsequential multistage jobs, it is better to compute the Gittins index by utilizing the SJP functions.

     
    more » « less
  4. ; ; ; ( , Proceedings of the ACM on Measurement and Analysis of Computing Systems)
    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. 
    more » « less
  5. ; ; ( , Proceedings of the ACM on Measurement and Analysis of Computing Systems)
    null (Ed.)
    The Gittins scheduling policy minimizes the mean response in the single-server M/G/1 queue in a wide variety of settings. Most famously, Gittins is optimal when preemption is allowed and service requirements are unknown but drawn from a known distribution. Gittins is also optimal much more generally, adapting to any amount of available information and any preemption restrictions. However, scheduling to minimize mean response time in a multiserver setting, specifically the central-queue M/G/k, is a much more difficult problem. In this work we give the first general analysis of Gittins in the M/G/k. Specifically, we show that under extremely general conditions, Gittins's mean response time in the M/G/k is at most its mean response time in the M/G/1 plus an $O(łog(1/(1 - ρ)))$ additive term, where ρ is the system load. A consequence of this result is that Gittins is heavy-traffic optimal in the M/G/k if the service requirement distribution S satisfies $\mathbfE [S^2(łog S)^+] < \infty$. This is the most general result on minimizing mean response time in the M/G/k to date. To prove our results, we combine properties of the Gittins policy and Palm calculus in a novel way. Notably, our technique overcomes the limitations of tagged job methods that were used in prior scheduling analyses. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email