Abstract
Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.
Highlights
For the past several years, as mobile devices (MDs) such as smartphones, handheld game consoles, and vehicle multimedia computers have become virtually ubiquitous, an increasing number of new mobile applications such as augmented reality, image processing, natural language processing, face recognition, and interactive gaming have emerged and become the focus of considerable attention [1, 2]. ese types of mobile applications are typically latency-sensitive, demand intensive computation, and have high-energy consumption characteristics
Recent studies have shown that mobile edge computing offloading (MECO) technology provides a promising opportunity to effectively overcome the limitations related to the hardware and energy consumption problems of MDs by offloading computation-intensive tasks to adjacent clouds at the edges of mobile networks for execution [5,6,7]
Compared to local computing, the partial offloading scheme can reduce the efficiency cost (EEC) significantly. is is because the proposed algorithm can optimally offload a fraction of the computation for execution on the MEC server according to the EEC on the edge and the local device. ird, the proposed algorithm has a lower EEC for a long completion time deadline compared with the full offloading method. is is reasonable given that the proposed algorithm adopts the optimal offloading policy and transmission power allocation
Summary
For the past several years, as mobile devices (MDs) such as smartphones, handheld game consoles, and vehicle multimedia computers have become virtually ubiquitous, an increasing number of new mobile applications such as augmented reality, image processing, natural language processing, face recognition, and interactive gaming have emerged and become the focus of considerable attention [1, 2]. ese types of mobile applications are typically latency-sensitive, demand intensive computation, and have high-energy consumption characteristics. You et al [15], based on insight into the input data arrival time instants and computation deadlines, studied an energy-efficient resource management policy for MECO systems and formulated an optimization strategy that minimizes the total mobile-energy consumption. Consider that MD n has a computation task Tn cn, dn, where cn denotes the total number of CPU cycles required to accomplish the computation task Tn and dn describes the input data size of computation task Tn. we will discuss the EEC spent by the MDs with respect to energy consumption and completion time for the local computing and edge computing approaches, respectively. According to the communication model, the transmission time and energy consumption of MD n for transmitting its computation task Tn to the MEC server are, respectively, calculated by tn,trs dn rn tn,. As in the existing works [17, 24], the receiving time tn,rece and the receiving energy en,rece can be ignored, because for many applications, such as face recognition, the size of the result is typically much smaller than that of the input data
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