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Fog computing is a promising computing paradigm for time-sensitive Internetof Things (IoT) applications. It helps to process data close to the users, inorder to deliver faster processing outcomes than the Cloud; it also helps toreduce network traffic. The computation environment in the Fog computing ishighly dynamic and most of the Fog devices are battery powered hence thechances of application failure is high which leads to delaying the applicationoutcome. On the other hand, if we rerun the application in other devices afterthe failure it will not comply with time-sensitiveness. To solve this problem,we need to run applications in an energy-efficient manner which is achallenging task due to the dynamic nature of Fog computing environment. It isrequired to schedule application in such a way that the application should notfail due to the unavailability of energy. In this paper, we propose a multiplelinear, regression-based resource allocation mechanism to run applications inan energy-aware manner in the Fog computing environment to minimise failuresdue to energy constraint. Prior works lack of energy-aware applicationexecution considering dynamism of Fog environment. Hence, we propose A multiplelinear regression-based approach which can achieve such objectives. We presenta sustainable energy-aware framework and algorithm which execute applicationsin Fog environment in an energy-aware manner. The trade-off betweenenergy-efficient allocation and application execution time has beeninvestigated and shown to have a minimum negative impact on the system forenergy-aware allocation. We compared our proposed method with existingapproaches. Our proposed approach minimises the delay and processing by 20%,and 17% compared with the existing one. Furthermore, SLA violation decrease by57% for the proposed energy-aware allocation.

Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment