Abstract
Fog computing provides users with data storage, computing, and other services by using fog layer devices close to edge devices. Tasks and resource scheduling in fog computing has become a research hotspot. For the multi-objective task-scheduling problem in fog computing, an adaptive multi-objective optimization task scheduling method for fog computing (FOG-AMOSM) is proposed in this paper. In this method, the total execution time and the task resource cost in the fog network are taken as the optimization target of resource allocation, and a multi-objective task scheduling model is designed. Since the objective model is a Pareto optimal solution problem, the global optimal solution can be obtained by using multi-objective optimization theory and the improved multi-objective evolutionary heuristic algorithm. Moreover, to obtain a better distribution of the current task scheduling group, the neighborhood is adaptively changed according to the current situation of the task scheduling group in fog computing, which avoids the problem that the neighborhood value caused by the neighborhood policy in the multi-objective algorithm affects the distribution of the task scheduling population. This algorithm is used to solve the non-inferior solution set of the utility function index of fog computing task scheduling to try to solve the multi-objective cooperative optimization problem in fog computing task scheduling. The results show that the proposed method has better performance than other methods in terms of total task execution time, resource cost and load dimensions.
Highlights
With the development of network communication technology, cloud computing as a new type of computing has attracted increasing attention and been widely integrated into various industries
The multi-objective task scheduling method adopted in this paper is to find the optimal solution from the Pareto solutions according to the needs of task and resource scheduling
The task scheduling problem in fog computing is an important problem in fog computing, and its calculation method will directly affect the efficiency and results of task execution in fog environments
Summary
With the development of network communication technology, cloud computing as a new type of computing has attracted increasing attention and been widely integrated into various industries. Fog computing is a highly virtualized platform, adding a fog layer between the IoT terminal node and the traditional cloud, using the devices in the fog layer to provide elastic computing, storage, network services, and other resources [4]. We use the method of the adaptive neighborhood to keep the diversity of the object solution set and try to solve the problem of multi-objective coordination in fog computing task scheduling. On this basis, FOG-AMOSM based on the adaptive neighborhood method is proposed.
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