Abstract
Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.
Highlights
Edge computing brings the advantages of low latency, small network load and low data management cost to the Internet of Things (IOT) [1]
In order to solve these problems, this paper proposes a resource constrained task scheduling profit optimization algorithm (RCTSPO) to maximize the profit of edge cloud, and constructs the task scheduling model based on Petri nets
In order to verify that RCTSPO algorithm has better performance in task scheduling and load balancing than the compared algorithm with the best profit weight and the best clustering value K
Summary
Edge computing brings the advantages of low latency, small network load and low data management cost to the Internet of Things (IOT) [1]. Computation offloading is used to transfer some tasks from the edge to the cloud for processing based on the attributes of task, such as energy consumption and calculation volume, extending the life cycle of edge devices and improving the task response time [5]. With the increase of tasks in the edge cloud, how to design an effective task scheduling strategy under the limited virtual machines has become a challenging problem. In order to solve these problems, this paper proposes a resource constrained task scheduling profit optimization algorithm (RCTSPO) to maximize the profit of edge cloud, and constructs the task scheduling model based on Petri nets. (3) We construct a task scheduling model in edge cloud based on Petri nets, which is used to model different components of edge cloud, the internal logic and time attribute are considered.
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