Fairness and classifications

Wierman, Adam

doi:10.1145/1243401.1243405

Cited by 43 publications

(28 citation statements)

References 46 publications

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“…Finally, since unfairness is always a worry when using policies based on SRPT scheduling, we provide a study of unfairness under MaxSRPT and SplitSRPT. Similarly to the analysis of SRPT in a single server queue [11,12,26,[34][35][36], our results highlight that unfairness is not a major worry under these policies.…”

Section: Summary Of Contributionssupporting

confidence: 72%

“…In the context of a single server queue, the unfairness of SRPT has received significant attention, e.g., [11,12,[34][35][36]. These studies have revealed that, in practical settings, large jobs actually receive as good (and sometimes better) performance under SRPT as they do under a fair scheduler like Processor Sharing (PS), which splits the service capacity evenly among all present jobs.…”

Section: What Happens To Large Jobs?mentioning

confidence: 99%

“…To study unfairness, the typical approach, e.g., in [34,35], is to use the conditional expected slowdown for a job of size z, E[S(z)], which is the ratio between the expected response time for a job with processing time z divided by z. In this context, fair policies have expected E[S(z)] that is independent of z, e.g., PS in M/GI/1 queue has E[S(z)] = 1/(1 − ρ), where ρ is the load.…”

Section: What Happens To Large Jobs?mentioning

confidence: 99%

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Joint optimization of overlapping phases in MapReduce

Lin

Zhang

Wierman

et al. 2013

Performance Evaluation

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MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. This paper studies the scheduling challenge that results from the overlapping of the "map" and "shuffle" phases in MapReduce. We propose a new, general model for this scheduling problem, and validate this model using cluster experiments. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate (i.e., in the resource augmentation framework). Finally, we validate the algorithms using a workload trace from a Google cluster and show that the algorithms are near optimal in practical settings.

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Section: Summary Of Contributionssupporting

confidence: 72%

Section: What Happens To Large Jobs?mentioning

confidence: 99%

Section: What Happens To Large Jobs?mentioning

confidence: 99%

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Joint optimization of overlapping phases in MapReduce

Lin

Zhang

Wierman

et al. 2013

Performance Evaluation

Self Cite

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“…For example, while proportional fairness relates to the idea that it is fair for jobs to receive response times proportional to their service times, temporal fairness respects the seniority of customers and the first-come-first-serve policy. Wierman [46] provides an overview and comparison of various scheduling policies which are focused on guaranteeing equitable response times to all job sizes.…”

Section: Notion Of Fairness In Shared Environmentsmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

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“…In [5], the notions of endogenous and exogenous unfairness are defined. In [6], algorithms are considered as fair if they do not favor jobs depending on their sizes. In [7], an algorithm is said to be fair iff it provides a response time that is less or equal than under PS for every task and any sample path.…”

Section: B Considering Resources Sharingmentioning

confidence: 99%

Efficient Web Requests Scheduling Considering Resources Sharing

Duquennoy

Grimaud

2010

2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-Requests scheduling in Web servers is a hot research topic. Many works aim at providing optimal algorithms according to various metrics. Most of these works are based on classical scheduling metrics, considering jobs completion times, but ignoring intermediate states. We claim that this choice conduces to the design of algorithm that do not efficiently share the system resources. Indeed, Web servers have some properties that make them different than the system considered in usual scheduling theory. The classical round-robin policy, used in most production Web servers, has intrinsic qualities: it shares equally the system resources and avoids any job starvation. We introduce a novel parameterizable algorithm proposing a compromise between the benefits of the round-robin and the policies that provide the best performances. Then, we discuss the appropriate choice of the parameter depending in the requirements and the context of the Web server.

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Fairness and classifications

Cited by 43 publications

References 46 publications

Joint optimization of overlapping phases in MapReduce

Joint optimization of overlapping phases in MapReduce

A new QoE fairness index for QoE management

Efficient Web Requests Scheduling Considering Resources Sharing

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