A New Meta-Heuristic Algorithm Based on Tabu Search for the Job Scheduling Problem in a Fog-Cloud system

Today, with the expansion of communications and the high volume of data, the need for processing them at a low time and high speed has increased. On the other hand, conducting this high volume of computing operations requires systems with high processing and storage capacities, which increase total costs. Therefore, proposing a suitable and affordable infrastructure can be very significant. The purpose of this article is to design and build an infrastructure with low cost and low response time using cloud computing and fog computing. In addition, one of the key issues for creating such systems at a high speed and minimum time is to allocate appropriate system resources to user requests and, as a result, load balances in the system. Among the various meta-heuristic methods, the Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Therefore, in this paper, a new method based on Tabu Search is proposed that is optimized using approximate nearest neighbor (ANN) and Fruit Fly Optimization (FOA) Algorithms. Finally, to evaluate and validate the proposed method, a case study is simulated on a smart home using the proposed infrastructure and the real dataset. Both methods have been implemented in this infrastructure and their performance has been calculated based on runtime and memory consumption. The results show the capability and efficiency of the proposed method for the various problems.