The need for the cloud resources is increasing and with increase in demand the cost of these resources is also increasing. Cloud environment gives the flexibility of utilizing the resources as per the need and the customer would pay for his usage. The consumer need not invest on the resources and thereby the cost of investment is drastically reduced for the consumer. But since the demand for the cloud resource is increasing, the cost is rising high. This can be reduced with an approach as proposed in this paper. This paper mainly focusses on the optimal way of resource adaption and hence reduction of cost and power consumption. Based on the analysis, there are some open challenges for the optimal resource adaptation. The resource’s idle time is utilized by other consumer in need and hence reduces the cost and power consumption. This can be achieved by adopting k-means algorithm initially to segregate the different kinds of resources, then the idle time is calculated with time and at what time using some of the prediction algorithms. The idle time of the resources is then distributed using algorithms such as round robin, FCFS etc…

Cloud, Resource, adaptation, machine learning

[1]. Jassy A Amazon Web Services Summit. https://aws.amazon.com/ summits/san-francisco/. Accessed May 2016
[2]. Galante G, Bona LCEd “A survey on cloud computing elasticity” In: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing, UCC ’12. IEEE Computer Society, Washington, DC, USA. pp 263–270
[3]. Beloglazov A, Buyya R, Lee YC, Zomaya A, “A taxonomy and survey of energy-efficient data centers and cloud computing systems”. Adv Comput82:47–111, 2011
[4]. Botran TL, Miguel-Alonso J, Lozano JA, “Auto-scaling techniques for elastic applications in cloud environments”. J Grid Comput12(4):559–592,2014
[5]. Najjar A, Serpaggi X, Gravier C, Boissier O, “Survey of Elasticity”, Management Solutions in Cloud Computing. In: Computer Communications and Networks. Springer, 236 Gray’s Inn Road, Floor 6, London WC1X 8HB, UK. pp 235–263, 2014
[6]. Jennings B, Stadler R, “Resource management in clouds: Survey and research challenges”. J Netw Syst Manag23(3):567–619
[7]. Coutinho EF, Carvalho Sousa FR, Rego PAL, Gomes DG, Souza JN, “Elasticity in cloud computing: a survey. Ann Telecommunannales des télécommunications” 70(7):289–309. doi:10.1007/s12243-014-0450-7,2014
[8]. Mann ZA, “Allocation of virtual machines in cloud data centers —a survey of problem models and optimization algorithms”. ACM Computing Survey 48(1):11–11134. doi:10.1145/2797211,2015
[9]. Singh S, Chana I, “Qos-aware autonomic resource management in cloud computing: A systematic review. ACM Computing Survey” 48(3):42–14246. doi:10.1145/2843889,2015
[10]. Faniyi F, Bahsoon R, “A systematic review of service level management in the cloud”. ACM Computing Survey 48(3):43–14327. doi:10.1145/2843890,2015
[11]. Naskos A, Gounaris A, Sioutas S, “Cloud Elasticity: A Survey”. In: Karydis I, Sioutas S, Triantafillou P, Tsoumakos D (eds). “Algorithmic Aspects of Cloud Computing: First International Workshop”, ALGOCLOUD 2015, Patras, Greece, September 14-15, 2015. Revised Selected Papers. Springer, Cham. pp 151–167,2016
[12]. Mohamaddiah MH, Abdullah A, Subramaniam S, Hussin M, “A survey on resource allocation and monitoring in cloud computing”. Int J Mach Learn Comput4(1):31–38,2014
[13]. Singh S, Chana I, “A survey on resource scheduling in cloud computing: Issues and challenges”. J Grid Comput14(2):1–48,2016
[14]. NIST Sp800-145: “Definition of cloud computing. Technical report”, NIST, 100 Bureau Drive, Gaithersburg, USA (Sep 2011). NIST. http://csrc.nist.gov/ publications/PubsSPs.html. Accessed May 2016
[15]. Herbst NR, Kounev S, Reussner R, “Elasticity in cloud computing: What it is, and what it is not”. In: 10th International Conference on Autonomic Computing. pp 23–27,2013
[16]. Maurer M, Brandic I, Sakellariou R, “Adaptive resource configuration for cloud infrastructure management”. Future General Computing System 29(2):472–487,2013
[17]. Magklis G, Semeraro G, Albonesi DH, Dropsho SG, Dwarkadas S, Scott ML, “Dynamic frequency and voltage scaling for a multiple-clock-domain microprocessor”. IEEE Micro 23:62–68,2003
[18]. Addis B, Ardagna D, Panicucci B, Zhang L, “Autonomic management of cloud service centers with availability guarantees”. In: 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, Washington, DC, USA. pp 220–227,2010
[19]. Sedaghat M, Hernández-Rodriguez F, Elmroth E, “Autonomic resource allocation for cloud data centers: A peer to peer approach”. In: IEEE International Conference on Cloud and Autonomic Computing. IEEE, Washington, DC, USA. pp 131–140,2014
[20]. Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA, “ Heterogeneity and dynamicity of clouds at scale: Google trace analysis”. In: Proceedings of the Third ACM Symposium on Cloud Computing, SoCC ’12. ACM, New York, NY, USA. pp 7–1713. doi:10.1145/2391229.2391236 http://doi.acm.org/10.1145/2391229.2391236,2012
[21]. Van HN, Tran FD, Menaud J-M, “Sla-aware virtual resource management for cloud infrastructures”. In: IEEE International Conference on Computer and Information Technology. IEEE, Washington, DC, USA 2:357-362,2009
[22]. Bodík P, Griffith R, Sutton C, Fox A, Jordan M, Patterson D, “Statistical machine learning makes automatic control practical for internet datacenters”. In: Proceedings of the 2009 Conference on Hot Topics in Cloud Computing, HotCloud’09. USENIX Association, Berkeley, CA, USA,2009
[23]. Lama P, Zhou X , “Aroma: Automated resource allocation and configuration of mapreduce environment in the cloud”. In: Proceedings of the 9th International Conference on Autonomic Computing, ICAC ’12. ACM, New York, NY, USA. pp 63–72,2012
[24]. Malkowski SJ, Hedwig M, Li J, Pu C, Neumann D, “Automated control for elastic n-tier workloads based on empirical modeling”. In: Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC ’11. ACM, New York, NY, USA. pp 131–140,2011
[25]. Ali-Eldin A, Tordsson J, ElmrothE, “An adaptive hybrid elasticity controller for cloud infrastructures”. In: 2012 IEEE Network Operations and Management Symposium. IEEE, Washington, DC, USA. pp 204–212,2012
[26]. Zhani MF, Cheriton DR, Zhang Q, Simon G, Boutaba R, “Vdc planner: Dynamic migration-aware virtual data center embedding for clouds”. In: IEEE International Symposium on Integrated Network Management. IEEE, Washington, DC, USA. pp 18–25,2013
[27]. Roy N, Dubey A, Gokhale A, “Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting”. In: IEEE International Conference on Cloud Computing. pp 500–507,2011 Computing. IEEE, Washington, DC, USA. pp 507–508,2014
[28]. Zheng S, Zhu G, Zhang J, Feng W, “Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm”. Multimedia Tools and Applications:1–18,2015
[29]. Zhang Q, Chen H, Shen Y, Ma S, Lu H, “Optimization of virtual resource management for cloud applications to cope with traffic burst”. FuturGenerComput Syst 58:42–55. doi:10.1016/j.future.2015.12.011,2016
[30]. Padala P, Hou K-Y, Shin KG, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A, “Automated control of multiple virtualized resources”. In: Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys ’09. ACM, New York, NY, USA. pp 13–26,20109
[31]. Dawoud W, Takouna I, MeinelC,Elastic virtual machine for fine-grained cloud resource provisioning. Glob Trends ComputCommun Syst 269:11–25,2011
[32]. Fargo F, Tunc C, Al-Nashif Y, Akoglu A, Hariri S, “Autonomic workload and resource management of cloud computing services”. In: IEEE International Conference on Cloud and Autonomic Computing. IEEE, Washington, DC, USA. pp 101–110,2014
[33]. Beloglazov A, Abawajyb J, Buyya R, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing”. Future General Computing Syst 28:755–768,2012
[34]. Shen Z, Subbiah S, Gu X, Wilkes J, “Cloudscale: Elastic resource scaling for multi-tenant cloud systems”. In: Proceedings of the 2Nd ACM Symposium on Cloud Computing, SOCC ’11. ACM, New York, NY, USA. pp 5–1514,2011
[35]. Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G, “Power and performance management of virtualized computing environments via lookahead control”. In: Autonomic Computing ICAC. IEEE, Washington, DC, USA. pp 3–23,2008
[36]. Cardosa M, Korupolu MR, Singh A, “Shares and utilities based power consolidation in virtualized server environments”. In: 11th IFIP/IEEE International Conference on Symposium on Integrated Network Management. pp 327–334,2009
[37]. Koehler M, “An adaptive framework for utility-based optimization of scientific applications in the cloud”. J Cloud Comput Adv Syst App 3:4,2014
[38]. Nathuji R, Kansal A, Ghaffarkhah A, “Q-clouds: Managing performance interference effects for qos-aware clouds”. In: Proceedings of the 5th European Conference on Computer Systems, EuroSys ’10. ACM, New York, NY, USA. pp 237–250,2010
[39]. Han R, Guo L, Ghanem MM, Guo Y, “Lightweight resource scaling for cloud applications”. In: International Symposium on Cluster, Cloud and Grid Computing. IEEE, Washington, DC, USA. pp 644–651,2012
[40]. Zhu X, Wang Z, Singhal S, “Utility-Driven Workload Management Using Nested Control Design”. In: American Control Conference. IEEE, Washington, DC, USA,2006
[41]. Xu J, Zhao M, Fortes J, Carpenter R, Yousif M, “Autonomic resource management in virtualized data centers using fuzzy logic-based approaches”. ClustComput11:213–227,2008
[42]. Jamshidi P, Ahmad A, Pahl C, “Autonomic resource provisioning for cloud-based software”. In: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and SelfManaging Systems, SEAMS 2014. ACM, New York, NY, USA. pp 95–104,2014
[43]. Han R, Ghanem MM, Guo L, Guo Y, Osmond M, “Enabling cost-aware and adaptive elasticity of multi-tier cloud applications”. FuturGenerComput Syst 32:82–98,2014
[44]. Hasan MZ, Magana E, Clemm A, Tucker L, Gudreddi SLD, “Integrated and autonomic cloud resource scaling”. In: Network
Operations and Management Symposium. IEEE, Washington, DC, USA. pp 1327–1334,2012
[45]. Gmach D, Rolia J, Cherkasova L, Kemper A “Resource pool management: Reactive versus proactive or lets be friends”. Computer Networks: The International Journal of Computer and Telecommunications Networking 53:2905–2922,2009
[46]. Iqbal W, Dailey MN, Carrera D, Janecek P, “Adaptive resource provisioning for read intensive multi-tier applications in the cloud”. FuturGenerComput Syst 26:871–879,2011
[47]. Gulati A, Shanmuganathan G, Holler A, Ahmad I, “Cloudscale resource management: Challenges and techniques”. In: Proceedings of the 3rd USENIX Conference on Hot Topics in Cloud Computing, HotCloud’11. USENIX Association, Berkeley, CA, USA. pp 3–3,2011
[48]. Tchana A, Palma ND, Safieddine I, Hagimont D, Diot B, Vuillerme N, “Euro-par 2015: Parallel processing: 21st international conference on parallel and distributed computing”, Vienna, Austria, August 24-28, 2015, proceedings: 305–316,2015
[49]. Beloglazov A, Buyya R, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers”. ConcurrComputPract Experience 24:1397–1420,2012
[50]. Choi HW, Kwak H, Sohn A, Chung K, “Autonomous learning for efficient resource utilization of dynamic vm migration”. In: Proceedings of the 22Nd Annual International Conference on Supercomputing, ICS ’08. ACM, New York, NY, USA. pp 185–194,2008
[51]. Zuo L, Shu L, Dong S, Zhu C, Zhou Z, “Dynamically weigh0ted load evaluation method based on self-adaptive threshold in cloud computing”. Mob Networks Appl :1–15. doi:10.1007/s11036-016- 0679-7,2016
[52]. Lim HC, Babu S, Chase JS, “Automated control for elastic storage”. In: Proceedings of the 7th International Conference on Autonomic Computing, ICAC ’10. ACM, New York, NY, USA. pp 1–10,2010