A hierarchical approach for resource allocation in hybrid cloud environments
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Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.
KeywordsStackelberg game Resource allocation Hybrid cloud
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61271176, 61401334, 61571350 and 61402287, the Fundamental Research Funds for the Central Universities (BDY021403), the 111 Project (B08038) and Shanghai Yangfan Project (No. 14YF1401900).
- 1.How Alibaba catered to USD 3 billion sales in a day. http://www.infoq.com/news/2012/12/interview-taobao-tmall.
- 3.Panigrahy, R., Talwar, K., Uyeda, L., & Wieder, U. (2011). Heuristics for vector bin packing. Microsoft: Technical Report.Google Scholar
- 4.Osborne, M. J. (2004). An introduction to game theory. Oxford: Oxford University Press.Google Scholar
- 6.Jünger, M., Liebling, T. M., Naddef, D., et al. (2009). 50 Years of integer programming 1958–2008. NewYork: Springer.Google Scholar
- 7.Karp, R. M. (1972). Reducibility among combinatorial problems. In Complexity of computer computations series. The IBM research symposia series (pp. 85–103). Springer.Google Scholar
- 9.Puchinger, J., Raidl, G. R., & Pferschy, U. (2006). The core concept for the multidimensional knapsack problem. In Evolutionary computation in combinatorial optimization, series. Lecture Notes in Computer Science (vol. 3906, pp. 195–208).Google Scholar
- 11.Singh, A., Korupolu, M., & Mohapatra, D. (November 2008). Server-storage virtualization: integration and load balancing in data centers. In Proceedings of the ACM/IEEE conference on supercomputing (pp. 1–12).Google Scholar
- 12.Jameson, A., Schmidt, W., & Turkel, E. (June 1981). Numerical solution of the Euler equations by finite volume methods using Runge Kutta time stepping schemes. In Fluid and plasma dynamics conference (pp. 1–15).Google Scholar
- 13.Paruchuri, P., Pearce, J. P., Marecki, J., Tambe, M., Ordonez, F., & Kraus, S. (2008). Efficient algorithms to solve Bayesian Stackelberg games for security applications. In ACM AAMAS (pp. 895–902).Google Scholar
- 14.Montgomery, D. C., Runger, G. C., & Hubele, N. F. (2009). Engineering statistics (5th ed.). Hoboken: Wiley.Google Scholar
- 16.Whitt, W. (2006). Staffing a calling center with uncertain arrival rate and absenteeism. Production and Operations Management, 15(1), 88–102.Google Scholar
- 19.Alicherry, M., & Lakshman, T. (2012). Network aware resource allocation in distributed clouds. In IEEE INFOCOM (pp. 963–971).Google Scholar
- 21.Dán, G., & Carlsson, N. (2014). Dynamic content allocation for cloud-assisted service of periodic workloads. In IEEE INFOCOM (pp. 853–861).Google Scholar
- 22.Hao, F., Kodialam, M., Lakshman, T. V., & Mukherjee, S. (April 2014). Online allocation of virtual machines in a distributed cloud. In IEEE INFOCOM (pp. 10–18).Google Scholar
- 23.Shi, W., Zhang, L., Wu, C., Li, Z., & Lau, F. C. (June 2014). An online auction framework for dynamic resource provisioning in cloud computing. In ACM SIGMETRICS (pp. 71–83).Google Scholar
- 27.Lee, G., Chun, B., & Katz, R. H. (2011). Heterogeneity-aware resource allocation and scheduling in the cloud. In Proceedings of HotCloud (pp. 1–5).Google Scholar
- 28.den Bossche, R. V., Vanmechelen, K., & Broeckhove, J. (2010). Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In International conference on cloud computing (CLOUD) (pp. 228–235).Google Scholar
- 29.Zhou, Z., Zhang, H., Du, X., Li, P., & Yu, X. (2013). Prometheus: Privacy-aware data retrieval on hybrid cloud. In IEEE INFOCOM (pp. 2643–2651).Google Scholar