Abstract
This article defines the QoS-guaranteed efficient cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than \(46\%\) efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about \(50\%\) averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.
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Notes
Following [29] capacity means the ability to handle user requests which is represented by the maximum number of user requests one cloudlet can receive. In the following paragraphs, we do not rank capacity according to CPU cycles or data storage sizes one cloudlet can offer, as well as the case when one cloudlet might have a better CPU, while the other has more memory.
To the best of our knowledge, there are numerous clustering methods. We only choose one typical clustering method in this work to better present the comparisons with existing works and to enlighten the new problem, the DBOCP problem and its solutions.
The heuristic algorithm proposed by [29], where each cloudlet should be placed to a location that can cover as many user requests as possible.
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This work was partially supported by the National Key R&D Program of China under Grant No. 2018YFB1003201. It was also supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61702115 and 61672171. Part of the work was funded by China Postdoctoral Science Foundation under Grant No. 2017M622632.
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Chen, L., Wu, J., Zhou, G. et al. QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks. J Supercomput 74, 4037–4059 (2018). https://doi.org/10.1007/s11227-018-2412-8
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DOI: https://doi.org/10.1007/s11227-018-2412-8