Abstract
Nowadays, cloud storage has received widespread attention for sharing of resources to achieve coherence and economies of scale. Focus on maximizing the effectiveness of the shared resources, how to allocate tasks reasonably and enhance the load balance are critical challenges that enhancing the overall performance of cloud service platform. In this paper, we proposed a high-dynamic invocation load balancing algorithm (LY-Cluster) for distributed servers in the cloud. There are three main contents that automatically allocate services’ IDs, multi-level capacity manager, and dynamically reallocated per demand based on sudden tasks. The experimental results show that our method performs well in terms of load balancing across the service replicas and improves the system scalability and response time.
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References
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Zaharia, M.: A view of cloud computing. Communications of the ACM 53(4), 50–58 (2010)
Espadas, J., Molina, A., JimĂ©nez, G., Molina, M., RamĂrez, R., Concha, D.: A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures. Future Generation Computer Systems 29(1), 273–286 (2013)
Subramani, V., Kettimuthu, R., Srinivasan, S., Johnston, J., Sadayappan, P.: Selective buddy allocation for scheduling parallel jobs on clusters. In: 4th IEEE International Conference on Cluster Computing, pp. 107–116. IEEE Press, Illinois (2002)
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. Journal of Parallel and Distributed Computing 59(2), 107–131 (1999)
Overman, R.E., Prins, J.F., Miller, L.A., Minion, M.L.: Dynamic Load Balancing of the Adaptive Fast Multipole Method in Heterogeneous Systems. In: 2013 IEEE 27th International Conference on Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pp. 1126–1135. IEEE Press, Cambridge (2013)
Chen, L., Villa, O., Krishnamoorthy, S., Gao, G.R.: Dynamic load balancing on single-and multi-GPU systems. In: 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), pp. 1–12. IEEE Press, Atlanta (2010)
Ghemawat, S., Gobioff, H., Leung, S.T.: The Google file system. ACM SIGOPS Operating Systems Review 37(5), 29–43 (2003)
Ekanayake, J., Fox, G.: High performance parallel computing with clouds and cloud technologies. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) Cloudcomp 2009. LNICST, vol. 34, pp. 20–38. Springer, Heidelberg (2010)
Gufler, B., Augsten, N., Reiser, A., Kemper, A.: Load balancing in mapreduce based on scalable cardinality estimates. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 522–533. IEEE Press, Washington (2012)
Fan, K., Zhang, D., Li, H., Yang, Y.: An Adaptive Feedback Load Balancing Algorithm in HDFS. In: 5th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 23–29. IEEE Press, Xi’an (2013)
Fang, Y., Wang, F., Ge, J.: A task scheduling algorithm based on load balancing in cloud computing. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds.) WISM 2010. LNCS, vol. 6318, pp. 271–277. Springer, Heidelberg (2010)
Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing 13(5), 2292–2303 (2013)
Hu, J., Gu, J., Sun, G., Zhao, T.: A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In: 2010 Third International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp. 89–96. IEEE Press, Dalian (2010)
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Qu, Z., Zang, J., Wang, L., Sun, H., Wang, Y. (2014). A High-Dynamic Invocation Load Balancing Algorithm for Distributed Servers in the Cloud. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_62
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DOI: https://doi.org/10.1007/978-3-319-09333-8_62
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
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