Deadline-aware Cost and Energy Efficient Offloading in Mobile Edge Computing

The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.