A Cost-Effective Cloud Leasing Policy for Live Streaming Applications
ZHANG Qiongbing1, DING Lixin1†, YANG Li21. School of Computer, Wuhan University, Wuhan 430072, Hubei, China; 2. College of Computer, Hubei University of Education, Wuhan 430205, Hubei, China
The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service (QoS) guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm (GA) to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.
 Chen F, Zhang C, Wang F, et al. Cloud-Assisted live streaming for crowdsourced multimedia content [J]. IEEE Transactions on Multimedia, 2015, 17(9): 1-6.
 Hajjat M, Sun X, Sung Y W E, et al. Cloudward bound: Planning for beneficial migration of enterprise applications to the cloud[J]. ACM Sigcomm Computer Communication Review, 2010, 40(4): 243-254.
 Sharma U, Shenoy P, Sahu S, et al. A cost-aware elasticity provisioning system for the cloud[C]// Proceedings of the International Conference on Distributed Computing Systems. Washington D C: IEEE, 2011: 559-570.
 Zhang H, Jiang G, Yoshihira K, et al. Intelligent workload factoring for a hybrid cloud computing model [C]// Pro-ceedings of the Congress on Services-I. Washington D C: IEEE, 2009: 701-708.
 Adhikari V K, Guo Y, Hao F, et al. Unreeling netflix: Un-derstanding and improving multi-CDN movie delivery[C]// Proceedings of the INFOCOM, 2012. Washington D C: IEEE, 2012: 1620-1628.
 Aggarwal V, Gopalakrishnan V, Jana R, et al. Optimizing cloud resources for delivering IPTV services through virtualization [C] // Fourth International Conference on Communication Systems and Networks. Washington D C: IEEE, 2012: 1-10.
 Wu Y, Wu C, Li B, et al. Scaling social media applications into geo-distributed clouds [C]// IEEE INFOCOM. Wash-ington D C: IEEE, 2012: 684-692.
 Feng Y, Li B, Li B. Bargaining towards maximized resource utilization in video streaming datacenters[C]//INFOCOM, 2012 Proceedings IEEE. Washington D C: IEEE, 2012: 1134-1142.
 Amazon. Amazon Elastic Compute Cloud[EB/OL]. [2016- 12-20]. http://aws.amazon.com/ec2/.
 Amazon. Amazon Simple Storage Service[EB/OL]. [2016- 12-20]. http: //aws.amazon.com/s3/.
 Wang Z, Sun L, Wu C, et al. Joint online transcoding and geo-distributed delivery for dynamic adaptive stream-ing[C]// Proceedings of the INFOCOM. Washington D C: IEEE, 2014: 91-99.
 Holland J H. Erratum: Genetic algorithms and the optimal allocation of trials[J]. Siam Journal on Computing, 1973, 2(2):88-105.
 Wei H, Sung-Kwun O, Witold P. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs) [J]. Neural Net-works, 2014, 60(C): 166-181.
 Huang W, Wang J. The shortest path problem on a time-dependent network with mixed uncertainty of randomness and fuzziness [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11): 3194-3204.
 Huang W, Oh S K, Guo Z, et al. A space search optimization algorithm with accelerated convergence strategies[J]. Ap-plied Soft Computing, 2013, 13(12): 4659-4675.
 Mezmaz M, Melab N, Kessaci Y, et al. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems[J]. Journal of Parallel & Distributed Computing, 2011, 71(11): 1497-1508.
 Wu Z, Liu X, Ni Z, et al. A market-oriented hierarchical scheduling strategy in cloud workflow systems[J]. The Journal of Supercomputing, 2013, 63(1): 256-293.
 Zhu Z, Zhang G, Li M, et al. Evolutionary multi-objective workflow scheduling in cloud[J]. IEEE Transactions on Parallel & Distributed Systems, 2016, 27(5): 1344-1357.
 Gao Y, Guan H, Qi Z, et al. A multi-objective ant colony system algorithm for virtual machine placement in cloud computing[J]. Journal of Computer & System Sciences, 2013, 79(8): 1230-1242.
 Zhang M, Liu L, Liu S. Genetic algorithm based QoS-aware service composition in multi-cloud[C]// Proceedings of the IEEE Conference on Collaboration and Internet Computing. Washington D C: IEEE, 2015: 113-118.
 Ye Z, Zhou X, Bouguettaya A. Genetic algorithm based QoS-aware service compositions in cloud computing[C]// Proceedings of the International Conference on Database Systems for Advanced Applications. Berlin: Springer-Verlag, 2011: 321-334.
 Gen M. Genetic Algorithms and Their Applications [M]. London: Springer-Verlag, 2006.
 Goldberg D E, Holland J H. Genetic algorithms and machine learning [J]. Machine Learning, 1988, 3(2): 95-99.