Optimal Speed Setting for Cloud Servers With Mixed Applications

Li, Keqin

doi:10.1109/tii.2018.2856909

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…In Reference 16, the author explored the technique of variable and task type dependent server speed management to optimize the server performance and to minimize the power consumption of a server with mixed applications. By establishing an M/G/1 queueing model for a server with variable and task type dependent speed, the problems of power constrained performance optimization and performance constrained power minimization were formulated and solved.…”

Section: Related Researchmentioning

confidence: 99%

Workload management and server speed setting for cost‐performance ratio optimization

2022

Softw Pract Exp

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The cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

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Section: Related Researchmentioning

confidence: 99%

Workload management and server speed setting for cost‐performance ratio optimization

2022

Softw Pract Exp

Self Cite

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“…The technique of variable and task‐type–dependent server speed management can be explored to optimize the server performance and to minimize the power consumption of a server with mixed applications. For instance, the power supply and the server speed can be increased for a type of applications with greater arrival rate and greater coefficient of variation of execution requirement . Such runtime power and speed adjustment can be supported by a mechanism called dynamic voltage scaling, or equivalently, dynamic frequency scaling, dynamic speed scaling, or dynamic power scaling…”

Section: Introductionmentioning

confidence: 99%

Optimal load distribution for multiple classes of applications on heterogeneous servers with variable speeds

2018

Softw Pract Exp

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Genetic algorithm for delay efficient computation offloading in dispersed computing

Liu

Niu

et al. 2023

Ad Hoc Networks

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Optimal Speed Setting for Cloud Servers With Mixed Applications

Cited by 6 publications

References 12 publications

Workload management and server speed setting for cost‐performance ratio optimization

Workload management and server speed setting for cost‐performance ratio optimization

Optimal load distribution for multiple classes of applications on heterogeneous servers with variable speeds

Genetic algorithm for delay efficient computation offloading in dispersed computing

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