E-MOGWO Algorithm for Computation Offloading in Fog Computing

Abstract

Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the first time MOGWO has been applied for computation offloading in fog computing. A gravity reference point method is also integrated with MOGWO to propose an enhanced multiobjective grey wolf optimization (E-MOGWO) algorithm. It finds the optimal offloading target by taking into account two parameters, i.e., energy consumption and computational time in a heterogeneous, scalable, multifog, multi-user environment. The proposed E-MOGWO is compared with MOG-WO, non-dominated sorting genetic algorithm (NSGA-II) and accelerated particle swarm optimization (APSO). The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption, computational time and the number of tasks successfully executed.

Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks

“…The goal is to optimize the balance between fog node delay and energy consumption. Yadav et al [15] view the offloading of fog computing as a multi-objective optimization problem and find the optimal offloading objective by considering two parameters, energy consumption and computation time, and proposing the enhanced multi-objective gray wolf algorithm (E-MOGW), but because it only considers the computational offloading of the fog layer and does not consider resource allocation, it causes competition for computing resources among users, overloading fog nodes, and increasing costs. M.T.…”

“…The constraints in the above equation are interpreted as: (15) the range of guaranteed offload ratio, (16) the range of guaranteed user offload only to cloud or fog, (17) the range of ensuring that the sum of allocated computing resources cannot exceed the computing capacity of FS, (18) the range of user transmit power, and (19) the range of ensuring that the upper layer resources are greater than the lower layer resources.…”