Hybrid Approach for Resource Provisioning in Cloud Computing

Moh'D Zuhier Freaj; Azzam Sleit

Moh'D Zuhier Freaj University of Jordan King Abdullah II School for IT Department of Computer Science Amman http://orcid.org/0000-0001-5863-9534
Azzam Sleit University of Jordan King Abdullah II School for IT Department of Computer Science Amman, Jordan

Keywords: Cloud Computing, Resource Provisioning, Energy Utilization, Resource Utilization, Power Aware, Cost Efficient, Sustainability, Elasticity of Resources.

Abstract

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Elasticity of resources is considered as a key characteristic of cloud computing using this key characteristic; internet services are allocated the only-needed resources. This allocation of resources however should not be at the expense of the services’ performance. Allocation of resources without degrading performance is called resource provisioning. Resource provisioning does not only support the elasticity of resources, but also enhances cost efficiency and sustainability.

The goal of this work is to investigate resource provisioning to increase the percentage of resources utilization without degrading the performance so that the power consumption of the cloud data centers is reduced. To achieve this goal, a hybrid-approach for resource provisioning is developed. In this approach, a list of virtual machines is requested, passed to a selection algorithm, sorting the machines according to their load, compute the threshold of the machines’ load, and combining the high load with low load from two different virtual machines on one super virtual machine. The approach was implemented in a simulator called CloudSim. It was used to run two sets of experiments. The first is to measure the power consumption of the data center as whole and hosts as well. And the second is concerned with the processing times and memory usage.

The results have shown that this approach outperforms traditional counterparts in resource provisioning. The results showed that the hybrid approach achieved reduction of (5.85 MW/s) in power consumption compared with the traditional counterparts for the whole data center, as well as reduction of (2.48 MW/s) in power consumption for the hosts.

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References

D. Agrawal, A. El Abbadi, S. Das, and A. J. Elmore, Database scalability, elasticity, and autonomy in the cloud, in Proceedings of the 16th Intl. conference on Database systems for advanced applications-Volume Part I, ser. DASFAA'11, 2011, pp. 2 - 15.

M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stocia, and M. Zahira, Above the clouds: A Berkeley view of cloud computing, U.C. Berkeley, EECS Department, 2009.

A. Beloglazov and R. Buyya, Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers, Concurrency and Computation: Practice and Experience (CCPE), pp. 1397-1420, 2012.

M. A. Bhat, R. M. Shah, B. Ahmad, and I. R. Bhat, Cloud Computing: A Solution to Information Support System (ISS), International Journal of Computer Applications, 2010.

S. J. Biggs and S. Vidalis, Cloud Computing and The Impact On Digital Forensic Investigations, International Conference for Internet Technology and Secured Transactions ICITST, 2009.

R. N. Calheiros, R. Ranjan, A. F. Beloglazov, C. A. De Rose, and R. Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms.

E. Caron, F. Desprez, and A. Muresan, Forecasting for Cloud computing on-demand resources based on pattern matching, INRIA, 2010.

M. Carroll, P. Kotzé, and A. van der Merwe, Securing Virtual and Cloud Environments. In I. Ivanov et al. Cloud Computing and Services Science, Service Science: Research and Innovations in the Service Economy, 2012.

T. Dillon, C. Wu, and E. Chang, Cloud Computing: Issues and Challenges, IEEE Computer Society, 2010, pp. 27-33.

D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper, Capacity management and demand prediction for next generation data centers, In IEEE Intl. Conference on Web Services, 2007, pp. 43–50.

S. Govindan, J. Choi, B. Urgaonkar, A. Sivasubramaniam, and A. Baldini, Statistical profiling-based techniques for effective power provisioning in data centers, In EuroSys ’09: Proceedings of the 4th ACM European conference on Computer systems, ACM, 2009, pp. 317–330.

Gupta and Nikhil, Fibonacci Retracements and Self-Fulfilling Prophecy, Honors Projects, Paper 41, http://digitalcommons.macalester.edu/economics_honors_projects/41, 2011.

S. Islam, J. Keung, K. Lee, and A. Liu, Empirical prediction models for adaptive resource provisioning in the cloud, Future Generation Computer Systems, 28(1), 2012, pp. 155-162.

H. C. Lim, S. Babu, J. S. Chase, and S. S. Parekh, Automated control in cloud computing: challenges and opportunities, ACDC '09: Proceedings of the 1st workshop on Automated control for datacenters and clouds, New York, NY, USA: ACM, 2009, pp. 13-18.

X. Meng, C. Isci, J. Kephart, L. Zhang, E. Bouillet, and D. Pendarakis, Efficient resource provisioning in compute clouds via VM multiplexing, Proceeding of the 7th international conference on Autonomic computing, Washington, DC, USA, 2010.

P. Padala, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, and K. Salem, Adaptive control of virtualized resources in utility computing environments, In ACM SIGOPS/EuroSys European Conference on Computer Systems, ACM, 2007.

KS. Park and VS. Pai, CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Operating Systems Review 2006, 2006, 40(1):74.

J. Rogers, O. Papaemmanouil, and U. Cetintemel, A generic auto-provisioning framework for cloud databases, Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on 1-6 March 2010, Long Beach, CA, 2010, pp. 63-68.

Robert Sedgewick, Algorithms in Java, 3rd Edition, Pearson Education, Inc, 2003.

J. a. Silva, L. Veiga, and P. Ferreira, Heuristic for resources allocation on utility computing infrastructures, MGC '08 Proceedings of the 6th international workshop on Middleware for grid computing, New York, NY, USA: ACM, 2008, pp. 1-6.

C. Weinhardt, A. Anandasivam, B. Blau, N. Borissov, T. Meinl, W. Michalk, and J. Stößer, Cloud Computing – A Classification, Business Models, and Research Directions. Business and Information Systems Engineering, 1 (5), 2009, pp. 391-399.

J. Weinman, Cloud Computing is NP-Complete. Retrieved March 21, 2013, from Joe Weinman: http://www.joeweinman.com/Resources/Joe_Weinman_Cloud_Computing_Is_NP-Complete.pdf, 2011.

K. Xiong and S. Suh, Resource provisioning in SLA-based cluster computing, Proceedings of the 15th international conference on Job scheduling strategies for parallel processing (JSSPP'10). Heidelberg: Springer-Verlag Berlin, 2010.

Q. Zhang, L. Cherkasova, and E. Smirni, A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications, Fourth International Conference on Autonomic Computing (ICAC'07), 2007, p. 27.

J. Zhu, Z. Jiang, and Z. Xiao, Twinkle: A fast resource provisioning mechanism for internet services, Proceedings of INFOCOM 2011, Shanghai, 2011, pp. 802-810.

D. Zissis, and D. Lekkas, Addressing cloud computing security issues, Future Generation Computer Systems 28, pp. 583–592, 2012.

A. Sleit, M. Al-Akhras, I. Juma, and M. Alian, “Applying ordinal association rules for cleansing data with missing values,” Journal of American Science, vol. 5, no. 3, pp. 52–62, 2009.

Al-Hasan, H., Qatawneh, M., Sleit, A., and Almobaideen, W. (2011) EAPHRN: Energy-Aware PEGASISBased Hierarchal Routing Protocol for Wireless Sensor Networks, Journal of American Science, 7(8), pp. 753-758.

Wesam Almobaideen, Dimah Al-Khateeb, Azzam Sleit, Mohammad Qatawneh, Khadejeh Qadadeh, Rasha Al-Khdour, Hadeel Abu Hafeeza, “Improved Stability Based Partially Disjoint AOMDV”, International Journal of Communications, Network and System Sciences, vol.6, pp.244-250, 2013.

Sleit, A., Qatawneh, M., Al-Sharief, M., Al-Jabaly, R., & Karajeh, O. (2011). Image Clustering using Color, Texture and Shape Features. KSII Transactions on Internet & Information Systems, 5(1).