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ACCRS: autonomic based cloud computing resource scaling

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Abstract

A cloud computing model gives cloud service providers the ability to retain multiple workloads on a single physical system. However, efficient resource provisioning and possible system fault management in the cloud can be a challenge. Early fault detection can provide room to recover from potential faults before impacting QoS. Current static techniques of fault management in computing systems are not satisfactory enough to safeguard the QoS requested by cloud users. Thus, new smart techniques are needed. This paper presents the ACCRS framework for cloud computing infrastructures to advance system’s utilization level, reduce cost and power consumption and fulfil SLAs. The ACCRS framework employs Autonomic Computing basic components which includes state monitoring, planning, decision making, fault predication, detection, and root cause analysis for recovery actions to improve system’s reliability, availability, and utilization level by scaling resources in response to changes in the cloud system state.

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References

  1. Parashar, M., Hariri, S.: Autonomic Computing: Concepts, Infrastructure, and Applications. CRC press (2006)

  2. Al-Dahoud, A., Al-Sharif, Z., Alawneh, L., Jararweh, Y.: Autonomic cloud computing resource scaling. In: 4th International IBM Cloud Academy Conference (ICACON 2016), University of Alberta, Edmonton, Canada, IBM (2016)

  3. Jararweh, Y., Al-Ayyoub, M., Darabseh, A., Benkhelifa, E., Vouk, M., Rindos, A.: Software defined cloud: survey, system and evaluation. Future Gener. Comput. Syst. 58, 56–74 (2016)

    Article  Google Scholar 

  4. Darabseh, A., Al-Ayyoub, M., Jararweh, Y., Benkhelifa, E., Vouk, M., Rindos, A.: Sddc: a software defined datacenter experimental framework. In: Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, pp. 189–194 (2015)

  5. Jararweh, Y.: Autonomic Programming Paradigm for High Performance Computing. PhD thesis, University of Arizona, Tucson (2010). AAI3423763

  6. Dai, Y., Xiang, Y., Zhang, G.: Self-healing and hybrid diagnosis in cloud computing. In: Cloud Computing, pp. 45–56. Springer (2009)

  7. Bhaduri, K., Das, K., Matthews, B.L.: Detecting abnormal machine characteristics in cloud infrastructures. In: Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pp. 137–144, IEEE (2011)

  8. Alhosban, A., Hashmi, K., Malik, Z., Medjahed, B.: Self-healing framework for cloud-based services. In: Computer Systems and Applications (AICCSA), 2013 ACS International Conference on, pp. 1–7, IEEE (2013)

  9. Buyya, R., Ramamohanarao, K., Leckie, C., Calheiros, R.N., Dastjerdi, A.V., Versteeg, S.: Big data analytics-enhanced cloud computing: Challenges, architectural elements, and future directions. arXiv preprint, arXiv:1510.06486 (2015)

  10. Islam, S., Keung, J., Lee, K., Liu, A.: An empirical study into adaptive resource provisioning in the cloud. In: IEEE International Conference on Utility and Cloud Computing (UCC 2010), p. 8 (2010)

  11. Buyya, R., Garg, S.K., Calheiros, R.N.: Sla-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. In: Cloud and Service Computing (CSC), 2011 International Conference on, pp. 1–10, IEEE (2011)

  12. Vecchiola, C., Chu, X., Buyya, R.: Aneka: a software platform for.net-based cloud computing. High Speed Larg. Scale Sci. Comput. 18, 267–295 (2009)

    Google Scholar 

  13. Hategan, M., Wozniak, J., Maheshwari, K.: Coasters: uniform resource provisioning and access for clouds and grids. In: Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on, pp. 114–121, IEEE (2011)

  14. Chaisiri, S., Lee, B.-S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5(2), 164–177 (2012)

    Article  Google Scholar 

  15. Dejun, J., Pierre, G., Chi, C.-H.: Resource provisioning of web applications in heterogeneous clouds. In: Proceedings of the 2nd USENIX Conference on Web Application Development, pp. 5–5, USENIX Association (2011)

  16. Bonvin, N., Papaioannou, T.G., Aberer, K.: Autonomic sla-driven provisioning for cloud applications. In: Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 434–443, IEEE Computer Society (2011)

  17. Toosi, A.N., Calheiros, R.N., Thulasiram, R.K., Buyya, R.: Resource provisioning policies to increase iaas provider’s profit in a federated cloud environment. In: High Performance Computing and Communications (HPCC), 2011 IEEE 13th International Conference on, pp. 279–287, IEEE (2011)

  18. Juve G., Deelman, E.: Resource provisioning options for large-scale scientific workflows. In: eScience, 2008. eScience’08. IEEE Fourth International Conference on, pp. 608–613, IEEE (2008)

  19. Kusic, D., Kephart, J.O., Hanson, J.E., Kandasamy, N., Jiang, G.: Power and performance management of virtualized computing environments via lookahead control. Clust. Comput. 12(1), 1–15 (2009)

    Article  Google Scholar 

  20. Kee,Y.-S., Kesselman, C.: Grid resource abstraction, virtualization, and provisioning for time-targeted applications. In: Cluster Computing and the Grid, 2008. CCGRID’08. 8th IEEE International Symposium on, pp. 324–331, IEEE (2008)

  21. Panda, S.K., Jana, P.K.: Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. 71(4), 1505–1533 (2015)

    Article  Google Scholar 

  22. Meng, X., Isci, C., Kephart, J., Zhang, L., Bouillet, E., Pendarakis, D.: Efficient resource provisioning in compute clouds via vm multiplexing. In: Proceedings of the 7th International Conference on Autonomic Computing, pp. 11–20, ACM (2010)

  23. Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., Althebyan, Q.: Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Clust. Comput. 18(2), 919–932 (2015)

    Article  Google Scholar 

  24. Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The eucalyptus open-source cloud-computing system. In: Cluster Computing and the Grid, 2009. CCGRID’09. 9th IEEE/ACM International Symposium on, pp. 124–131, IEEE (2009)

  25. Bhaumik, S.K.: Root cause analysis in engineering failures. Trans. Indian Inst. Metals 63(2), 297–299 (2010)

    Article  MathSciNet  Google Scholar 

  26. Zhu, Q., Tung, T., Xie, Q.: Automatic fault diagnosis in cloud infrastructure. In: Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, vol. 1, pp. 467–474, IEEE (2013)

  27. I.G.B.S. IBM, “Business strategy for cloud providers. http://www.itworldcanada.com/archive/Documents/whitepaper/ITW157B_BusinessStretegyForCloudProviders.pdf (2009). Accessed: 1 June 2016

  28. Jararweh, Y., Jarrah, M., Alshara, Z., Alsaleh, M., Al-Ayyoub, M.: Cloudexp: a comprehensive cloud computing experimental framework. Simul. Model. Pract. Theory 49, 180–192 (2014)

    Article  Google Scholar 

  29. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, vol. 41, no. 1, pp. 23–50 (2011)

  30. Beitch, A., Liu, B., Yung, T., Griffith, R., Fox, A., Patterson, D.A.: Rain: A Workload Generation Toolkit for Cloud Computing Applications. University of California, Tech. Rep. UCB/EECS-2010-14 (2010)

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Authors and Affiliations

  1. Software Engineering Department, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Ziad A. Al-Sharif & Luay M. Alawneh

  2. Computer Science Department, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Yaser Jararweh & Ahmad Al-Dahoud

Authors
  1. Ziad A. Al-Sharif
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  2. Yaser Jararweh
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  3. Ahmad Al-Dahoud
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  4. Luay M. Alawneh
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Correspondence to Ziad A. Al-Sharif.

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Al-Sharif, Z.A., Jararweh, Y., Al-Dahoud, A. et al. ACCRS: autonomic based cloud computing resource scaling. Cluster Comput 20, 2479–2488 (2017). https://doi.org/10.1007/s10586-016-0682-6

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  • DOI: https://doi.org/10.1007/s10586-016-0682-6

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