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Using the TSP Solution Strategy for Cloudlet Scheduling in Cloud Computing
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DOI：10.1007/s10922-018-9469-9.png)
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Abstract
En 中文
Cloudlet scheduling in cloud computing is one of the most issues that face the cloud computing environment. This paper presents a new efficient approach, called Traveling Salesman Approach for Cloudlet Scheduling (TSACS), to solve the cloudlet-scheduling problem. The main idea is to convert the cloudlet-scheduling problem into an instance of the Traveling Salesman Problem (TSP) and then apply one of the TSP solution strategies to solve the problem. The proposed approach consists of three phases: clustering phase, converting phase, and assignment phase. In the clustering phase, the proposed approach converts the large size cloudlet-scheduling problem into a small size cluster-scheduling problem to minimize computation time complexity of the proposed approach. In the converting phase, the approach forms the cluster-scheduling problem as an instance of the TSP. In the assignment phase, the approach schedules the clusters into the available virtual machines by using the nearest neighbor algorithm. The proposed approach is evaluated by using the CloudSim and the results are compared with that obtained by the most recent algorithms. The results show that the proposed approach enhances the overall system performance in terms of schedule length, balancing degree, and time complexity. In addition, the proposed TSACS overcomes the oscillation problem of the existing cloudlet-scheduling algorithms.
Keywords:
Cloudlet-scheduling
Nearest neighbor algorithm
Makespan
Load balancing
Oscillation problem
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