MULTI-OBJECTIVE OPTIMIZATION BASED DISTRIBUTED TASK OFFLOADING IN FOG COMPUTING

Abstract

Due to an increasing number of IoT devices, a massive amount of data is generating daily. Initially, Cloud-centric Internet of Things (CIoT) based architecture is used for data processing, storage, and analysis. But it is difficult for the CIoT to handle the huge amount of data produced by these devices. Furthermore, cloud data centers are far away from IoT devices so transmitting data to the cloud will result in more bandwidth consumption, cost, and latency issues that’s why cloud computing is not suitable for real-time applications. To overcome these problems the concept of fog computing is introduced that extends cloud computing by moving the facilities to the edge of the network. Fog computing provides low latency, real-time processing near the edge of the network. Fog computing contains heterogeneous and intrinsically resource-constrained devices. Due to resource-constrained devices at the edge of the network resource management is a necessary issue to efficiently distribute resources and move some tasks to the other entity for execution to balance the load it’s called offloading. Different techniques of task offloading are used in fog computing that’s main purpose is to maximize and effective resource utilization, minimizing latency, cost, and energy consumption. This research is concerned with the design and implementation of a multi-objective optimization-based distributed task offloading algorithm in which we will optimize more than two objectives simultaneously like optimization of performance and resource utilization metrics.