Speculation resource provisioning in high-performance computing
Keywords:
Cloud computing, dynamic resource provision, energy efficient resource allocation, speculation resource provision.Abstract
Distributed computing gives a backing to buyers to diminish their inner foundation, and for suppliers to expand incomes, utilizing their own particular framework. The proper load balancing and dynamic resource provisioning improves cloud performance and attracts the cloud users. In this paper, we propose an automated resource provisioning algorithm,
Speculation resource provisioning , prompting load balancing through speculative approach in resource provisioning. As an attempt to quantify resource allocation we use two level adaptive prediction mechanism to check the computational patterns of past resource allocation to the future requirement. The framework guarantees suitable resources required for
the application, by dodging over or under-provisioning of resource and supports energy-efficiency in resource allocation. We use estimation methodology to address the variability in the historical data to balance the speculation overhead. We have conveyed our proposed work in an open source cloud structure and contrasted our outcomes and other machine
learning approaches. Our Experimental results demonstrate adaptive resource allocation over customer-driven service management under the rapidly changing requirements of cloud computing.
References
Aljazzaf, Z.M. (2015). Modeling and measuring the quality of online
services. Kuwait Journal of Science, 42(3):134-157.
Bataineh, M.H. (2012). Artificial neural network for studying human
performance, MSc. Thesis, The University of IOWA, IOWA, USA.
Cao, J., Li, K. & Stojmenovic, I. (2014). Optimal power allocation
and load distribution for multiple heterogeneous multicore server
processors across clouds and data centers. IEEE Transactions on
Computers, 63(1):45-58.
Caron, E., Desprez, F. & Muresan, A. (2010). Forecasting for cloud
computing on-demand resources based on pattern matching (Doctoral Dissertation, INRIA).
Garca, D.F., Garca, J., Garca, M., Peteira, I., Garca, R. &
Valledor, P. (2006). Benchmarking of web services platforms. In
Proceedings of 2nd International Conference on Web Information
Systems and Technologies, WEBIST, Setubal, Portugal (pp.75-80).
Garg, S.K. & Buyya, R. (2011). Networkcloudsim: Modelling parallel
applications in cloud simulations. In Utility and Cloud Computing
(UCC), 2011 Fourth IEEE International Conference, pp. 105-113.
IEEE, Victoria, NSW.
Hussain, H., Malik, S.U.R., Hameed, A., Khan, S.U., Bickler, G.
et al. (2013). A survey on resource allocation in high performance
distributed computing systems. Parallel Computing, 39(11):709-736.
Hu, W., Hicks, A., Zhang, L., Dow, E.M., Soni, V., et al. (2013). A
quantitative study of virtual machine live migration. In Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, p. 11. ACM.
Huang, D., He, B. & Miao, C. (2014). A survey of resource
management in multi-tier web applications. IEEE Communications
Surveys & Tutorials, 16(3):1574-1590
Islam, S., Keung, J., Lee, K. & Liu, A. (2012). Empirical prediction
models for adaptive resource provisioning in the cloud. Future
Generation Computer Systems, 28(1):155-162.
Jamshidi, P., Ahmad, A. & Pahl, C. (2013). Cloud migration research: a systematic review. IEEE Transactions on Cloud Computing, 1(2):142- 157.
Li, A., Yang, X., Kandula, S. & Zhang, M., (2011). Comparing public cloud providers. IEEE Internet Computing, 15(2):50.
Samimi, P., Teimouri, Y., & Mukhtar, M. (2014). A combinatorial
double auction resource allocation model in cloud computing.
Information Sciences. DOI: 10.1016/j.ins.2014.02.008.
Tuah, N.J., Kumar, M. & Venkatesh, S. (2003). Resource-aware
speculative prefetching in wireless networks. Wireless Networks,
(1):61-72.
Urgaonkar, R., Kozat, U.C., Igarashi, K. & Neely, M.J. (2010).
Dynamic resource allocation and power management in virtualized data centers. 2010 IEEE Network Operations and Management Symposium-NOMS, pp. 479-486. IEEE.
Voorsluys, W., Broberg, J., Venugopal, S. & Buyya, R. (2009). Cost
of virtual machine live migration in clouds: A performance evaluation. IEEE International Conference on Cloud Computing, pp. 254-265.Springer Berlin Heidelberg.
Zhuang, H., Liu, X., Ou, Z. & Aberer, K. (2013). Impact of instance
seeking strategies on resource allocation in cloud data centers. IEEE
CLOUD, pp. 27-34.