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Log based Automated SMI Parameter Identification and Resource Recommendations in Cloud


  • Department of Computer Science and Engineering, TRP Engineering College, NH 45, Irungalur, Mannachanallur Taluk, Tiruchirappalli - 621105, Tamil Nadu, India
  • Department of Computer Applications, Pondicherry University, Kalapet, Puducherry – 605014, Pondicherry, India


Objectives: Resource provisioning is the major requirement in cloud provisioning. The major objective is to provide effective resource provisioning can improve the utility of a cloud service at reduced costs. Methods/Analysis: This paper presents an effective method to identify the quality parameters for effective provisioning of cloud resources. User log files are used to identify the quality parameters. It is assumed that the user migrates from a web service, cluster based service or another cloud based service. The log files from these architectures are used to map the SMI parameters and the quality values are obtained by analyzing them. Findings: Experiments were conducted on an access log data with 4.4 million entries and 3 million independent users. The required QoS and provided QoS were plotted and it was observed that most of the points are situated either on the diagonal or in the top left. This exhibits the efficiency of our approach to appropriately identify the user requirements and provide appropriate allocations. The ratio between the time taken for the entire process to complete and the data size was also analyzed for identifying the scalability of the system. It could be observed that as the size of the data increases, the time taken also increases. Hence the time taken is observed to be linear. Applications/Improvement: Identification of quality parameters were never performed with such granularity. Hence the results obtained exhibits effective quality assignments appropriate to user’s needs.


Cloud Provisioning, Resource Recommendations, SMI Parameter Mapping, Log File Based Mapping, Workload Identification.

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  • Kostoska M, Gusev M, Ristov S. A new cloud services portability platform. Procedia Engineering, 2014 Mar; 69:1268-75.
  • Huang J, Liu G, Duan Q. On modeling and optimization for composite network - Cloud service provisioning. Journal of Network and Computer Applications. 2014 Oct; 45:35-43.
  • Singh A, Juneja D, Malhotra M. A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. Journal of King Saud University-Computer and Information Sciences. 2015 Nov.
  • Ezugwu AE, Buhari SM, Junaidu SB. Virtual machine allocation in cloud computing environment. International Journal of Cloud Applications and Computing (IJCAC). 2013 Apr; 3(2):47-60.
  • Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I. Department Electrical Engineering and Computer Sciences, University of California, Berkeley, Rep. UCB/EECS: Above the clouds: A Berkeley view of cloud computing. 2009 Feb.
  • Wu CM, Chang RS, Chan HY. A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems. 2014 Jul; 37:141-47.
  • Quang-Hung N, Thoai N, Son NT. Epobf, energy efficient allocation of virtual machines in high performance computing cloud. 2013 Oct.
  • Buyya R, Abramson D, Giddy J, Stockinger H. Economic models for resource management and scheduling in grid computing. Concurrency and computation: practice and experience. 2002 Nov; 14(13-15):1507-42.
  • Buyya R, Branson K, Giddy J, Abramson D. The Virtual Laboratory: a toolset to enable distributed molecular modelling for drug design on the World‐Wide Grid. Concurrency and Computation: Practice and Experience. 2003 Jan; 15(1):1-25.
  • Li JZ, Chinneck J, Woodside M, Litoiu M. Fast scalable optimization to configure service systems having cost and quality of service constraints. Proceedings of the 6th international conference on Autonomic computing. ACM, 2009 Jul; p. 159-68.
  • Hu D, Chen N, Dong S, Wan Y. A user preference and service time mix-aware resource provisioning strategy for multi-tier cloud services. AASRI Procedia. 2013 Nov; 5:235-42.
  • Singh R, Sharma U, Cecchet E, Shenoy. Autonomic mix-aware provisioning for non-stationary data center workloads. Proceedings of the 7th International Conference on Autonomic computing. ACM 2010 Jun; p. 21-30.
  • Kim S, Kim JS, Hwang S, Kim Y. Towards effective science cloud provisioning for a large-scale high-throughput computing. Cluster Computing. 2014 Dec; 17(4):1157-69.
  • Wang XY, Lan D, Wang G, Fang X, Ye M, Chen Y, Wang Q. Appliance-based autonomic provisioning framework for virtualized outsourcing data center. ICAC’07, Fourth International Conference on Autonomic Computing. IEEE, 2007 Jun; p. 29.
  • Urgaonkar B, Shenoy P, Roscoe T. Resource overbooking and application profiling in a shared internet hosting platform. ACM Transactions on Internet Technology (TOIT). 2009 Feb; 9(1).
  • Li C. Optimal resource provisioning for cloud computing environment. The Journal of Supercomputing. 2012 Nov; 62(2):989-1022.
  • Madni SH, Latiff MS, Coulibaly Y. An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud. Indian Journal of Science and Technology. 2016 Jan; 9(4):1-14.
  • Shyamala K, Rani TS. An analysis on efficient resource allocation mechanisms in cloud computing. Indian Journal of Science and Technology. 2015 May; 8(9):814-21.
  • Mahmoud AA, Zarina M, Nik WN, Ahmad F. Multi-Criteria Strategy for Job Scheduling and Resource Load Balancing in Cloud Computing Environment. Indian Journal of Science and Technology. 2015 Nov; 8(30):1-5.
  • Sheshasaayee A, Margaret TS. The Challenges of Business Intelligence in Cloud Computing. Indian Journal of Science and Technology. 2015 Dec; 8(36):1-6.
  • Randles M, Lamb D, Taleb-Bendiab A. A comparative study into distributed load balancing algorithms for cloud computing. 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2010 Apr; p. 551-56.
  • AVouk M. Cloud computing–issues, research and implementations. CIT. Journal of Computing and Information Technology. 2008 Dec; 16(4):235-46.
  • Srikantaiah S, Kansal A, Zhao F. Energy aware consolidation for cloud computing. Proceedings of the 2008 conference on Power aware computing and systems. 2008 Dec; p. 10-10.
  • Berl A, Gelenbe E, Di Girolamo M, Giuliani G, De Meer H, Dang MQ, Pentikousis K. Energy-efficient cloud computing. The Computer Journal. 2010 Sep; 53(7):1045-51.
  • Kim KH, Beloglazov A, Buyya R. Power-aware provisioning of cloud resources for real-time services. Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science. ACM, 2009 Nov.
  • Singh S, Chana I. Q-aware: Quality of service based cloud resource provisioning. Computers & Electrical Engineering. 2015 Oct; 47:138-60.


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