Received: 22 November 2018 Revised: 18 March 2019 Accepted: 23 May 2019 DOI: 10.1002/ett.3673 SPECIAL ISSUE ARTICLE Energy-efficient and delay-aware multitask offloading for mobile edge computing networks Tarik Chanyour Mohamed El Ghmary Youssef Hmimz Mohammed Ouçamah Cherkaoui Malki FSDM, LIIAN Labo, Sidi Mohamed Ben Abdellah University, Fez, Morocco Correspondence Tarik Chanyour, FSDM, LIIAN Labo, Sidi Mohamed Ben Abdellah University, P.O. Box 1796, Atlas, 30003 Fez, Morocco. Email: tarik.chanyour@usmba.ac.ma Present Address Tarik Chanyour, P.O. Box 1796, Atlas-Fez, Morocco Abstract Mobile edge computing (MEC) is a recent technology that intends to free mobile devices from computationally intensive workloads by offloading them to a nearby resource-rich edge architecture. It helps to reduce network traffic bot- tlenecks and offers new opportunities regarding data and processing privacy. Moreover, MEC-based applications can achieve lower latency level compared to cloud-based ones. However, in a multitask multidevice context, the decision of the part to offload becomes critical. Actually, it must consider the available communication resources, the resulting delays that have to be met during the offloading process, and particularly, both local and remote energy consumption. In this paper, we consider a multitask multidevice scenario where smart mobile devices retain a list of heavy offloadable tasks that are delay constrained. There- fore, we formulated the corresponding optimization problem, and we derive an equivalent multiple-choice knapsack problem formulation. Because of the short decision time constraint and the NP-hardness of the obtained problem, the optimal solution implementation is infeasible. Hence, we propose a solution that provides, in pseudopolynomial time, the optimal or near-optimal solutions depending on the problem's settings. In order to evaluate our solution, we carried out a set of simulation experiments to evaluate and compare the performances of the different components of this solution. Finally, the obtained results in terms of execution's time as well as energy consumption are satisfactory and very encouraging. 1 INTRODUCTION The diversity of applications used in modern smart mobile devices has led to their rapid development and then their widespread use in people's daily lives. Even with the improvement of their capacity because of the fierce competition, a new class of modern applications that are greedy in computation resources and energy have emerged, for instance, inter- active gaming, virtual reality, and natural language processing. 1,2 Thus, a new conflict appears between resource-hungry applications and resource-limited capacity. It creates unprecedented challenges while executing such applications, espe- cially with regard to energy considerations. Such is the background where mobile edge computing (MEC) provides, via the offloading technique, 3 a solution to migrate computations to a nearby resource-rich infrastructure. This last can offer cus- tomized services that require good transmission bandwidth, additional data storage, and processing. 4 Hence, the power consumption of a mobile device can be effectively reduced by offloading heavy tasks to a MEC infrastructure. On the other hand, the 5G standardization roadmap stipulates that such infrastructures will be more beneficial with multiple-server Trans Emerging Tel Tech. 2019;e3673. wileyonlinelibrary.com/journal/ett © 2019 John Wiley & Sons, Ltd. 1 of 22 https://doi.org/10.1002/ett.3673