Differential Scale based Multi-objective Task Scheduling and Computational Offloading in Fog Networks

Abstract

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.