Fog computing effective load balancing and strategy for deadlock prediction management

Abstract

The reliability, real-time performance, and quality of service of fog computing are dramatically affected by deadlock. In this paper, a new Deadlock Management System for a fog computing environment is proposed to achieve the best task scheduling while considering deadlock prediction. It contains five modules; the receiver, the matchmaker, the deadlock predictor, the dispatcher, and the ranking module. The receiver is responsible for incoming tasks, assigning their initial priority and task envelope, and calculating server computational distance. The match-making is a fuzzy inference algorithm used to calculate the server matchmaking degree. This determines the best fog server to perform the task. The deadlock module calculates the deadlock prediction risk degree while The dispatcher module dispatches the task depending on first-fit or best-fit modes. The ranking module is responsible for ranking all suitable fog servers and choosing the most suitable one to implement the task. This paper contributes to providing a new load-balancing strategy in fog computing while taking deadlock into consideration. Thus, resolving drawbacks such as load and complexity that strain the network, and computation latency. The results show that the proposed strategy outperforms other load-balancing techniques reported in the literature.

Keywords

• Fog computing
• Quality of service (QoS)
• Cloud computing
• Deadlock
• Load balancing
• Real-time task scheduling