Energy‐efficient and delay‐aware multitask offloading for mobile edge computing networks

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 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.

Edge-Enabled WBANs for Efficient QoS Provisioning Healthcare Monitoring: A Two-Stage Potential Game-Based Computation Offloading Strategy

“…Mobile edge computing enhanced base stations (BSs) and powerful distributed edge devices make it possible for local knowledge extraction from massive IoT data [17], [18]. MEC-based applications can achieve lower latency levels than cloud-based applications [19]. Based on Lyapunov's optimization theory, a joint computation allocation and resource management algorithm [20] was proposed by transforming the original problem into a series of deterministic optimization problems in each time block.…”

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

“…Moreover, due to the complexity of evaluating the delay and energy consumption of the communication process between remote MCs and without loss of generality, we ignore the power consumption occurred at the backhaul network. Further, the network model of our previous work [12] is adopted. Thus, we assume that e i uses an estimated uplink rate r i in each allocated subchannel.…”

“…To solve problem P3 we propose two algorithms: the first is the recent BISSA [17] solution which demonstrated its effectiveness in our previous work [12]. It is a pseudopolynomial time complexity algorithm.…”

“…Accordingly, we propose to implement a relevant variant: Very Fast Simulated Annealing [18] which we denote VFSA-TD. Different to the classical Simulated Annealing (SA) algorithm which we adopted in our previous work [12] which denote SA-TD, This variant is characterized with a small convergence time. In this algorithm, the thermodynamic system's energy is represented by the cost function F i .…”

“…Different to [12] where the system's energy only is optimized while all the offloadable tasks of a given SMD are constrained to the same delay, this paper presents a generalization scenario regarding mainly the following three points: the first generalization point relies on the per-task delay property where we consider every task with its proper delay constraint. The second point concerns the existence of a one-to-many association that links every SMD to many available MEC servers.…”