Energy-aware workload management models for operation cost reduction in data centers

Bodenstein, Christian; Schryen, Guido; Neumann, Dirk

doi:10.1016/j.ejor.2012.04.005

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…Therefore, it is necessary to design an energy-efficient mechanism for effectively saving energy of idle servers. Previous studies demonstrate that a potential power cutting could be as remarkable as 40% [4]. For this purpose, a key technique, called an energy-efficient state 'sleep' or 'off', was introduced to save energy for idle servers.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Xia

2019

Discrete Event Dyn Syst

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In this paper, we propose a novel dynamic decision method by applying the sensitivity-based optimization theory to find the optimal energy-efficient policy of a data center with two groups of heterogeneous servers. Servers in Group 1 always work at high energy consumption, while servers in Group 2 may either work at high energy consumption or sleep at low energy consumption. An energy-efficient control policy determines the switch between work and sleep states of servers in Group 2 in a dynamic way. Since servers in Group 1 are always working with high priority to jobs, a transfer rule is proposed to migrate the jobs in Group 2 to idle servers in Group 1. To find the optimal energy-efficient policy, we set up a policy-based Poisson equation, and provide explicit expressions for its unique solution of performance potentials by means of the RG-factorization. Based on this, we characterize monotonicity and optimality of the long-run average profit with respect to the policies under different service prices. We prove that the bang-bang control is always optimal for this optimization problem, i.e., we should either keep all servers sleep or turn on the servers such that the number of working servers equals that of waiting jobs in Group 2. As an easy adoption of policy forms, we further study the threshold-type policy and obtain a necessary condition of the optimal threshold policy. We hope the methodology and results derived in this paper can shed light to the study of more general energy-efficient data centers.

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Section: Introductionmentioning

confidence: 99%

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Xia

2019

Discrete Event Dyn Syst

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“…They also studied impact on supporting number of concurrent users and throughput. Bodenstein et al [19] focused on understanding energy use in operating data centers workloads, and how the overall cost can be minimized. Moreno et al [24] focused on workload allocation that improves energy-efficiency in cloud datacenters.…”

Section: Insight-3mentioning

confidence: 99%

Optimal Resource Provisioning and the Impact of Energy-Aware Load Aggregation for Dynamic Temporal Workloads in Data Centers

Qian

Li²,

Ravindran

et al. 2014

IEEE Trans. Netw. Serv. Manage.

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An important goal of data center providers is to minimize their operational cost, which reflected through the wear-and-tear cost and the energy consumption cost. In this paper, we present optimization formulations to minimize the cost of ownership in terms of server energy consumption and serverwear-and-tear cost under three different data center server setups (homogeneous, heterogeneous, and hybrid hetero-homogeneous clusters) for dynamic temporal workloads. Our studies show that the homogeneous model takes significantly less computational time than the heterogeneous model (by an order of magnitude). To compute optimal configurations in near real time for large-scale data centers, we propose two modes for using our models: aggregation by maximum (preserves workload deadline) and aggregation by mean (relaxes workload deadline). In addition, we propose two aggregation methods for use in each of the two modes: static (periodic) aggregation and dynamic (aperiodic)aggregation. We found that in the aggregation by maximum mode, dynamic aggregation resulted in cost savings of up to approximately 18% over the static aggregation. In the aggregation by mean mode, dynamic aggregation saved up to approximately a 50% workload rearrangement compared with the static aggregationby mean mode.

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“…A few of works on machine learning algorithms have been proposed for the resource management problem [16][17][18][19]. For admission control, [8] derived a complex rule set that can be used to identify the optimal configuration for unobserved workload based on machine learning algorithms.…”

Section: Review Of Literaturementioning

confidence: 99%

Dynamic request distribution for enhanced Quality of Service

Joldasbayev

Balakayeva

Aidarov

et al. 2018

JMMCS

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The emergence of Web-services as an open components supporting flexible and inexpensive set of distributed applications as well as exploiting them as a promising solution for integration with other application and providers of software-hardware resources is very much in demand. Using Web-services simplifies and enhances system functionality because of the availability of programs interaction with each other through Internet using open protocols. Thereby it is necessary to provide QoS provision issues such as distribution of request streams, enhanced efficiency of operations at peak loads. One of the ways to tackle these issues is to apply dynamic request distribution which ensures continuity of data transfer and processing ignoring crashes and equipment failures, redistribution of data flow to the available nodes in case of their loss. Abovementioned can be achieved using certain heuristics based on methods of Mathematical statistics and Probability modeling. Moreover, we provide analysis of methods and models developed for QoS provision.

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Energy-aware workload management models for operation cost reduction in data centers

Abstract: In the last century, the costs of powering datacenters have increased so quickly

Cited by 13 publications

References 25 publications

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Optimal Resource Provisioning and the Impact of Energy-Aware Load Aggregation for Dynamic Temporal Workloads in Data Centers

Dynamic request distribution for enhanced Quality of Service

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