Performance analysis and system optimization of an energy-saving mechanism in cloud computing with correlated traffic

Xuena Yan; Shunfu Jin; Wuyi Yue; Yutaka Takahashi

doi:10.3934/jimo.2021106

Article Contents

2022, Volume 18, Issue 5: 3133-3154. Doi: 10.3934/jimo.2021106

This issue Previous Article Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms Next Article A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

Performance analysis and system optimization of an energy-saving mechanism in cloud computing with correlated traffic

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China
3.
Langfang Yanjing Vocational Technical College, Langfang 065200, China
4.
Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan
5.
Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan

^* Corresponding author: Shunfu Jin
^* Corresponding author: Shunfu Jin

Received: August 31, 2020

Revised: March 31, 2021

Early access: June 2021

Published: November 2022

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Abstract

Energy consumption is becoming a significant part of overall operational cost in cloud data centers. For the purpose of satisfying the Service Level Agreement (SLA) of cloud users while enhancing the energy efficiency in cloud computing systems, in this paper we propose an energy-saving mechanism with a sleep mode. Taking into consideration the traffic's correlation and the stochastical behavior of data arrival requests in a random cloud environment with the proposed energy-saving mechanism, we model the system as a MAP/M/$ N $/$ N $+$ K $ queue with a synchronous multi-vacation. Then, we present a theoretical basis for analyzing and evaluating the system performance by taking a state transition rate matrix in the steady state. Next, we investigate the change trends for the energy saving rate of the system and the average latency of tasks by carrying out numerical experiments. Moreover, we give a

Keywords:

Mathematics Subject Classification: Primary: 68M20, 60K25; Secondary: 90B22.

Citation:

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Figure 1. State transition of a PM with the energy-saving mechanism

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Figure 2. Change trend for the energy saving rate $ \omega $ of the system

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Figure 3. Change trend for the average latency $ \sigma $ of tasks

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Figure 4. Change trend for the cost function $ \phi \left(\alpha \right) $

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Table 1. Numerical results for the optimization of the energy-saving mechanism

Buffer size $ K $	VM-number $ N $	Optimal sleep parameter $ \alpha^* $	Minimal system cost $ \phi \left( \alpha^* \right) $
	6	0.2860	0.8414
24	7	0.4127	0.6951
	8	0.5025	0.6046
	6	0.2887	0.8920
27	7	0.4407	0.7224
	8	0.5439	0.6203
	6	0.2942	0.9401
30	7	0.4746	0.7462
	8	0.5886	0.6330
	6	0.3026	0.9857
33	7	0.5141	0.7666
	8	0.6346	0.6432

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