On Choosing a Task Assignment Policy for a Distributed Server System

Harchol-Balter, Mor; Crovella, Mark; Murta, Cristina Duarte

doi:10.1006/jpdc.1999.1577

Cited by 260 publications

(87 citation statements)

References 10 publications

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“…Another subtle use of mass-count disparity comes from task assignment in the context of server farms [318]. Consider a situation in which two servers are available to serve a stream of incoming requests, as is done in many large websites (typically, many more than two are used).…”

Section: Discussionmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Discussionmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

191

131

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“…From the perspective of scheduling, [29] presents various policies in a distributed server system and suggests optimal ones for different situations. Similarly, [30] attempts to quantify the benefits parallelization in a dispatching system, where jobs, arriving in batches, are assigned to single-server FCFS queues.…”

Section: Related Workmentioning

confidence: 99%

Provisioning and Performance Evaluation of Parallel Systems with Output Synchronization

KhudaBukhsh

Kar

Rizk

et al. 2019

ACM Trans. Model. Perform. Eval. Comput. Syst.

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Frameworks, such as MapReduce and Hadoop are abundant nowadays. They seek to reap benefits of parallelization, albeit subject to a synchronization constraint at the output. Fork-Join (FJ) queuing models are used to analyze such systems. Arriving jobs are split into tasks each of which is mapped to exactly one server. A job leaves the system when all of its tasks are executed.As a metric of performance, we consider waiting times for both work-conserving and non-work conserving server systems under a mathematical set-up general enough to take into account possible phase-type behavior of the servers, and as suggested by recent evidences, bursty arrivals.To this end, we present a Markov-additive process framework for an FJ system and provide computable bounds on tail probabilities of steady-state waiting times, for both types of servers separately. We apply our results to three scenarios, namely, nonrenewal (Markov-modulated) arrivals, servers showing phasetype behavior, and Markov-modulated arrivals and services. We compare our bounds against estimates obtained through simulations and also provide a theoretical conceptualization of provisions in FJ systems. Finally, we calibrate our model with real data traces, and illustrate how our bounds can be used to devise provisions.

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“…Another interesting study of limiting behavior is done in [4] where the authors study FJ networks with nonexchangeable tasks under a heavy traffic regime and show asymptotic equivalence between this network and its corresponding assembly network with exchangeable tasks. From the perspective of choosing task assignment policies in distributed server systems, the authors of [14] study various policies and suggest different optimal policies in different situations. Similarly, the work in [16] seeks to quantify the benefits of splitting a task into different queues.…”

Section: Related Workmentioning

confidence: 99%

Optimizing stochastic scheduling in fork-join queueing models: Bounds and applications

KhudaBukhsh

Rizk

Frömmgen

et al. 2017

IEEE INFOCOM 2017 - IEEE Conference on Computer Communications

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Fork-Join (FJ) queueing models capture the dynamics of system parallelization under synchronization constraints, for example, for applications such as MapReduce, multipath transmission and RAID systems. Arriving jobs are first split into tasks and mapped to servers for execution, such that a job can only leave the system when all of its tasks are executed.In this paper, we provide computable stochastic bounds for the waiting and response time distributions for heterogeneous FJ systems under general parallelization benefit. Our main contribution is a generalized mathematical framework for probabilistic server scheduling strategies that are essentially characterized by a probability distribution over the number of utilized servers, and the optimization thereof. We highlight the trade-off between the scaling benefit due to parallelization and the FJ inherent synchronization penalty. Further, we provide optimal scheduling strategies for arbitrary scaling regimes that map to different levels of parallelization benefit. One notable insight obtained from our results is that different applications with varying parallelization benefits result in different optimal strategies. Finally, we complement our analytical results by applying them to various applications showing the optimality of the proposed scheduling strategies.

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On Choosing a Task Assignment Policy for a Distributed Server System

Cited by 260 publications

References 10 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Provisioning and Performance Evaluation of Parallel Systems with Output Synchronization

Optimizing stochastic scheduling in fork-join queueing models: Bounds and applications

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