Multi objective task scheduling in fog computing

Abstract

newline In the modern Internet of Things (IoT) era, several applications generate a vast amount of data, and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service (QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource-constrained, how the fog resources are effectively utilized for executing latency and time-sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. The workflow in the IoT applications is represented as a Directed Acyclic Graph (DAG) where the nodes denote the tasks and the edges indicate the reliance on the task. The proposed work deals with effective task scheduling algorithms for fog computing environments to minimize the makespan, energy consumption, and price, and maximize the reliability and Utilization Rate.