Task arrival based energy efficient optimization in smart-IoT data center

Bin Wang; Fagui Liu; Bin Wang; Fagui Liu

doi:10.3934/mbe.2021138

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2713-2732. doi: 10.3934/mbe.2021138

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Research article Special Issues

Task arrival based energy efficient optimization in smart-IoT data center

Bin Wang ,
Fagui Liu ^,

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China

Received: 08 December 2020 Accepted: 04 March 2021 Published: 22 March 2021

With the growth and expansion of cloud data centers, energy consumption has become an urgent issue for smart cities system. However, most of the current resource management approaches focus on the traditional cloud computing scheduling scenarios but fail to consider the feature of workloads from the Internet of Things (IoT) devices. In this paper, we analyze the characteristic of IoT requests and propose an improved Poisson task model with a novel mechanism to predict the arrivals of IoT requests. To achieve the trade-off between energy saving and service level agreement, we introduce an adaptive energy efficiency model to adjust the priority of the optimization objectives. Finally, an energy-efficient virtual machine scheduling algorithm is proposed to maximize the energy efficiency of the data center. The experimental results show that our strategy can achieve the best performance in comparison to other popular schemes.
- energy efficiency,
- task arrival,
- green data centers,
- smart cities,
- VM placement
Citation: Bin Wang, Fagui Liu. Task arrival based energy efficient optimization in smart-IoT data center[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2713-2732. doi: 10.3934/mbe.2021138

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Abstract

With the growth and expansion of cloud data centers, energy consumption has become an urgent issue for smart cities system. However, most of the current resource management approaches focus on the traditional cloud computing scheduling scenarios but fail to consider the feature of workloads from the Internet of Things (IoT) devices. In this paper, we analyze the characteristic of IoT requests and propose an improved Poisson task model with a novel mechanism to predict the arrivals of IoT requests. To achieve the trade-off between energy saving and service level agreement, we introduce an adaptive energy efficiency model to adjust the priority of the optimization objectives. Finally, an energy-efficient virtual machine scheduling algorithm is proposed to maximize the energy efficiency of the data center. The experimental results show that our strategy can achieve the best performance in comparison to other popular schemes.

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