Energy-efficient computation offloading strategy with tasks scheduling in edge computing
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Energy-efficient computation offloading strategy with tasks scheduling in edge computing Yue Zhang1 • Jingqi Fu1 Accepted: 25 September 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In mobile edge computing systems, the energy consumption and execution delay can be reduced dramatically by mobile edge computation offloading (MECO) . However, due to the limited computing capacity of edge cloud, an energy-efficient offloading strategy plays a significant role. In this paper, the offloading decision problem for multi-device edge computing systems based on time-division multiple access is studied. The scheduling of offloading devices at the edge cloud is considered when modelling the edge computing system. Then, the offloading decision problem is formulated as an energy consumption minimization problem with the constraint of latency tolerance. It is a mixed integer programming problem of NP-hardness. To address the problem, a Dynamic Programming-based Energy Saving Offloading (DPESO) algorithm is designed to obtain the offloading strategy including the offloading option, offloading sequence and transmission power. First, the MECO with infinite edge cloud capacity is solved by device classification and transmission power decision. Then, we sort and adjust the offloading devices to meet the latency tolerance for the MECO with finite edge cloud capacity. Finally, simulation results demonstrate that the DPESO algorithm achieves better energy efficiency than the baseline strategies and has good scalability. Keywords Mobile edge computing Computation offloading Resource competition Dynamic programming Energy-efficient
1 Introduction With the dramatical development of the Internet of Things (IoT) technology, more and more devices running computation-intensive applications access the Internet. However, devices have limited battery lifetime and computation resources in general, making them unqualified for processing resource-intensive services [1]. Cloud computing has been envisioned as an efficient way to address the above challenge. By offloading tasks to cloud infrastructures, cloud computing can enhance the computation capabilities of devices [2]. Nevertheless, the weaknesses of cloud computing are revealed with the & Jingqi Fu [email protected] Yue Zhang [email protected] 1
School of Mechanical Engineering and Automation, Shanghai University, Shanghai, China
emergence of the Internet of Everything (IoE). More than 50 billion terminal devices will access the Internet by 2020 [3]. As a result, cloud resources are not infinite any more, and the bandwidth limitation may lead to unexpected latency as well. Mobile edge computing has been proposed to overcome the weaknesses [4]. Figure 1 shows the architecture of edge computing systems. Mobile edge computing can provide computing power through the resources deployed in the ‘‘edge’’, such as smart gateways, base stations and so on. Devices usually access the edge cloud via a single-hop ne
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