JOINT TASK SCHEDULING AND OFFLOADING IN FOG OPTIMIZED COMPUTING SYSTEM (FOCS) ALGORITHM FOR IOT BASED NETWORK APPLICATIONS

Abstract

Fog computing, which serves as a middle layer between the cloud and Internet of Things (IoT) devices such as sensors, mobile devices, and smart infrastructures, is becoming more prevalent over cloud computing. Because of its proximity to edge devices, it can process data optimally, enabling real-time reaction demands and reducing latency, energy consumption, and communication costs. In this paper, the Fog Optimized Computing System (FOCS) algorithm is proposed to solve network overloading and processing difficulties that arise from the explosion of IoT devices. FOCS uses a task scheduling and offloading algorithm that classifies data into groups according to its size and routes it to the appropriate fog nodes. Larger data packets are simultaneously sent to fog nodes with higher capacity, while smaller packets are routed to fog devices with low data size in unit million instructions per second (MIPS). Load-balancing strategies ensure that data is delivered to the closest idle fog nodes when there is network congestion. FOCS provides faster response times and low latency while stabilizing the system in terms of energy consumption and utilization cost. The latency, energy consumption and utilization costs are minimized and become stable by the FOCS after a certain number of Inputs (tasks from IoT devices). The proposed FOCS algorithm with organized approach, optimizes the energy consumption by 71% and reduces latency by 35.8%.