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 bottlenecks 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. Therefore, 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.

<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>

In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines.

No abstract

Mobile-edge computing (MEC) is a new paradigm with a great potential to extend mobile users capabilities becauseof its proximity. It can contribute efficiently to optimize the energy consumption to preserve privacy, and reduce the bottlenecks of the network traffic. In addition, intensive-computation offloading is an active research area that can lessen latencies and energy consumption. Nevertheless, within multiuser networks with a multi-task scenario, select the tasks to offload is complex and critical. Actually, these selections and the resources' allocation have to be carefully considered as they affect the resulting energies and delays. In this work, we study a scenario considering a user-adaptive offloading where each user runs a list of heavy computation-tasks. Every task has to be processed in its associated MEC server within a fixed deadline. Hence, the proposed optimization problem target the minimization of a weighted-sum normalized function depending on three metrics. The first is energy consumption, the second is the total processing delays, and the third is the unsatisfied processing workload. The solution of the general problem is obtained using the solutions of two sub-problems. Also, all solutions are evaluated using a set of simulation experiments. Finally, the execution times are very encouraging for moderate sizes, and the proposed heuristic solutions give satisfactory results in terms of users cost function in pseudo-polynomial times.