Load Balancing in Processor Sharing Systems

Altman, Eitan; Ayesta, Urtzi; Prabhu, Balakrishna

doi:10.4108/icst.valuetools2008.4462

Cited by 35 publications

(69 citation statements)

References 15 publications

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“…Note however that SRPT is not necessarily the optimal back-end scheduler for a load balancing design. 1 In particular, because the load balancer interacts with the scheduling policy, it is not a priori clear that SRPT is even necessarily a good choice for a back-end scheduler.…”

Section: Background On Back-end Schedulersmentioning

confidence: 99%

On the Impact of Heterogeneity and Back-End Scheduling in Load Balancing Designs

2009

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Abstract-Load balancing is a common approach for task assignment in distributed architectures. In this paper, we show that the degree of inefficiency in load balancing designs is highly dependent on the scheduling discipline used at each of the backend servers. Traditionally, the back-end scheduler can be modeled as Processor Sharing (PS), in which case the degree of inefficiency grows linearly with the number of servers. However, if the backend scheduler is changed to Shortest Remaining Processing Time (SRPT), the degree of inefficiency can be independent of the number of servers, instead depending only on the heterogeneity of the speeds of the servers. Further, switching the back-end scheduler to SRPT can provide significant improvements in the overall mean response time of the system as long as the heterogeneity of the server speeds is small.

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Section: Background On Back-end Schedulersmentioning

confidence: 99%

On the Impact of Heterogeneity and Back-End Scheduling in Load Balancing Designs

2009

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“…In particular, non-cooperative Game Theory is used to study and understand decentralized algorithms in which the autonomous agents behave "selfishly", that is each agent makes decisions so as to optimize its own performance, without coordination with the other agents. In the past decade, Game Theory has found applications in as diverse areas as load-balancing in server farms [2,5,9,10,15,18,28], power control and spectrum allocation in wireless networks [14,25,26,35,38,39,43,44,51], congestion control in the Internet [1,21,37,49,55] or decentralized routing in communication networks [4,16,29,31,36,45].…”

Section: Introductionmentioning

confidence: 99%

Performance of Non-Cooperative Routing Over Parallel Non-Observable Queues

Brun¹

2016

Prob. Eng. Inf. Sci.

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Autonomic computing is emerging as a significant new approach to the design of computer services. Its goal is the development of services that are able to manage themselves with minimal direct human intervention, and, in particular, are able to sense their environment and to tune themselves to meet end-user needs. However, the impact on performance of the interaction between multiple uncoordinated self-optimizing services is not yet well understood. We present some recent results on a non-cooperative load-balancing game which help to better understand the result of this interaction. In this game, users generate jobs of different services, and the jobs have to be processed on one of the servers of a computing platform. Each service has its own dispatcher which probabilistically routes jobs to servers so as to minimize the mean processing cost of its own jobs. We first investigate the impact of heterogeneity in the amount of incoming traffic routed by dispatchers and present a result stating that, for a fixed amount of total incoming traffic, the worstcase overall performance occurs when each dispatcher routes the same amount of traffic. Using this result we then study the so-called Price of Anarchy (PoA), an oft-used worstcase measure of the inefficiency of non-cooperative decentralized architectures. We give explicit bounds on the PoA for cost functions representing the mean delay of jobs when the service discipline is PS or SRPT. These bounds indicate that significant performance degradations can result from the selfish behavior of self-optimizing services. In practice, though, the worst-case scenario may occur rarely, if at all. Some recent results suggest that for the game under consideration the PoA is an overly pessimistic measure that does not reflect the performance obtained in most instances of the problem.

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“…Note that as opposed to communication networks, splitting in the context of web-server farms is not always possible. Another related paper is [7] where the authors consider routing policies of the model in a distributed vs. centralized optimization. In general our queueing model falls within the framework of a fork-join queueing network [9].…”

Section: Related Workmentioning

confidence: 99%

Optimal File Splitting for Wireless Networks with Concurrent Access

Hoekstra

Mei

Nazarathy

et al. 2009

Lecture Notes in Computer Science

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Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multitude of wireless access networks. Today, little is known about how to effectively exploit this potential. Motivated by this, we consider N parallel communication networks, each of which is modeled as a processor sharing (PS) queue that handles two types of traffic: foreground and background. We consider a foreground traffic stream of files, each of which is split into N fragments according to a fixed splitting rule (α1, . . . , αN ), where P αi = 1 and αi ≥ 0 is the fraction of the file that is directed to network i. Upon completion of transmission of all fragments of a file, it is re-assembled at the receiving end. The background streams use dedicated networks without being split. We study the sojourn time tail behavior of the foreground traffic. For the case of light foreground traffic and regularly varying foreground filesize distributions, we obtain a reduced-load approximation (RLA) for the sojourn times, similar to that of a single PS-queue. An important implication of the RLA is that the tail-optimal splitting rule is simply to choose αi proportional to ci − ρi, where ci is the capacity of network i and ρi is the load offered to network i by the corresponding background stream. This result provides a theoretical foundation for the effectiveness of such a simple splitting rule. Extensive simulations demonstrate that this simple rule indeed performs well, not only with respect to the tail asymptotics, but also with respect to the mean sojourn times. The simulations further support our conjecture that the same splitting rule is also tail-optimal for non-light foreground traffic. Finally, we observe near-insensitivity of the mean sojourn times with respect to the file-size distribution.

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Load Balancing in Processor Sharing Systems

Cited by 35 publications

References 15 publications

On the Impact of Heterogeneity and Back-End Scheduling in Load Balancing Designs

On the Impact of Heterogeneity and Back-End Scheduling in Load Balancing Designs

Performance of Non-Cooperative Routing Over Parallel Non-Observable Queues

Optimal File Splitting for Wireless Networks with Concurrent Access

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