A Synergistic Cloud Service Approach for Cold Start Problems
The authors wish to thank Natural Science Foundation of China under Grant No. 61662054, 61262082, Natural Science Foundation of Inner Mongolia under Grand No.2015MS0608, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating”, Inner Mongolia Engineering Lab of Cloud Computing and Service Software and Inner Mongolia Engineering Lab of Big Data Analysis Technology, and the Enhancing Comprehensive Strength Foundation of Inner Mongolia University (No.10000-16010109-25).
2018 Ieee Smartworld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet Of People And Smart City Innovation (Smartworld/Scalcom/Uic/Atc/Cbdcom/Iop/Sci)
More and more new cloud users use the personalised cloud service combination strategy (CSCS). Solving the Cold Start problem of the cloud environment becomes intractable. The paper represents a novel method of choosing the most optimal combinatorial features based on the attenuation function to cluster, and integrating multi level sampling method to cope with the pure Cold Start for cloud users. By means of every clustering process with different combinatorial features, then using the relatively stable number of clusters for every clustering obtains the optimal combinatorial features, which presents the tendency of the whole society of cloud users who use the CSCS. Meanwhile, we propose the function of periodic attenuation that enhances the degree of recommendation for CSCSs which have been issued recently. We harness the vectors of preference and disfavour to calculate the similarity of cloud users. An improved cluster algorithm of CFSFDP is employed. Moreover, it is worth selecting the most representative features to cluster which demonstrates effectively. In addition, the attenuation function can increase the probability of recommendation of recent CSCS, and the multi level sampling method has been used to heighten the diversity of recommendations. The method of ours can enhance the effectiveness and intelligence of recommendation for the pure Cold Start problem.
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R. Qi, J. Zhou and X. Song, "A Synergistic Cloud Service Approach for Cold Start Problems," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, 2018, pp. 472-479.