Meta-heuristic-based offloading task optimization in mobile edge computing

Abstract

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

“…It is not greedy in particular. In particular, a slight regression of a method can also be tolerated the designed to simulate methodology which helps to investigate an optimal solution very systematically and therefore to achieve a better solution that can often correspond with either the best solution [ 6 , 39 , 40 ]. Metaheuristic would be a problem-independent method, but to apply the methodology to the nature of the problem, it will be essential to do great of all its intrinsic parameters.…”

“…To improve the computational difficulties of smart devices with offloading, cloud computing is considered to play a significant role [ 3 – 5 ]. By optimizing the device's parameters so that it becomes easier to find optimal decisions for offloading tasks, a metaheuristic algorithm is used to migrate the data or to offload the task [ 6 ]. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has a lower battery or makespan time than FCFS or ACO [ 7 ].…”

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing

“…The studies to date have focused on optimizing latency and energy consumption during computational offloading. Abbas et al utilized meta-heuristic algorithms, such as ant colony, whale, and grey wolf optimization, to establish an offloading strategy aimed at minimizing energy and delay, albeit within the confines of a singular MEC server context [16]. While meta-heuristic algorithms are robust, their complexity and numerous parameters often complicate the tuning process.…”

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

“…On the basis of these methods, a security mechanism was introduced in IoT networks for the healthcare applications in different works. These studies implemented closely related existing schemes in the simulation, for instance, delay optimal long short-term memory (LSTM) [ 21 ], workflow metaheuristic system (WFMS), [ 22 ] and workflow metaheuristic cloud (WMC) [ 23 ]. These studies are closely related to our work to execute workflow applications on heterogeneous nodes in cloud computing.…”