Metaheuristic task offloading approaches for minimization of energy consumption on edge computing: a systematic review - Rohaya Latip; Jafar Aminu; Zurina Mohd Hanafi; Shafinah Kamarudin; Danlami Gabi (Discover Internet of Things)
Abstract With the evolution of edge computing, which complements the cloud computing environment, services are now provided to nearby customers. Although the computing capacity of edge computing seems limited, offloading tasks with higher computational demands for mobile edge computing to reduce mobile device computations can be challenging. In addition, outsourcing computing resources, on the other hand, is costly and can increase energy consumption due to congestion in networks and server queues. Moreover, a task offloading technique is required to reduce energy consumption through efficient provisioning of computing resources to meet the expectations of tasks with higher demands for resources. On the other hand, existing researchers have attempted to propose solutions using both heuristic and metaheuristic techniques to minimize energy consumption at the edge saver through various task offloading methods. However, it will be prudent to assume that these methods are different from the existing methods. This is because combining these strategies allows for a more global search while simultaneously benefiting from the efficiency of heuristics and the exploratory powers of metaheuristics. Therefore, to understand the existing techniques in terms of their methods, strengths and weaknesses, this paper reviews the literature on metaheuristic task offloading techniques used in the minimization of energy consumption in an edge computing environment. Literature from the Web of Science, Elsevier, IEEE and Google Scholar databases was drawn using the PRISMA approach. A systematic review was then carried out, and a summary of the findings was tabulated based on performance metrics, offloading type and type of simulation environment. The findings from the review conducted here show that the majority of the existing techniques rely on the use of conventional metaheuristic approaches for reducing energy consumption and thus are limited in terms of poor convergence speed, imbalance between local and global searches and high computational complexity. Future research directions are also noted.