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An Efficient Load Balancing Scheme for Cloud Computing


  • Department of Computer Science, College of Education for Pure Science, Thi_Qar University, Iraq


Objectives: The load balancing becomes an important point for performance and stability of the system. Therefore, it is needed an algorithm for enhancing the system performance by balancing workload among VMs. Methods: Task scheduling algorithms are used to achieve the load balancing and QoS. The proposed Load Balancing Decision Algorithm(LBDA) to manage and balance the load between the virtual machines in a datacenter along with reducing the completion time (Makespan) and Response time. Findings: The mechanism of LBDA is based on three stages, first calculates the VM capacity and VM load to categorize the VMs’ states (Under loaded VM, Balanced VM, High Balance VM, Overloaded). Second, calculate the time required to execute the task in each VM. Finally, makes a decision to distribute the tasks among the VMs based on VM state and task time required. Improvements: We compared the result of our proposed LBDA with Max- Min, Shortest Job Firstand Round Robin. The results showed that the proposed LBDA is more efficient than the existing algorithms.


Cloud Computing, LBDA, Load Balancing, Makespan, Response Time, Task Scheduling

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  • Chitra DD, Uthariaraj VR. Load balancing in cloud computing environment using Improved Weighted RoundRobin Algorithm for nonpreemptive dependent tasks. The Scientific World Journal. 2016; 2016.
  • Solmaz A, Motamedi S, Sharifian S. Task scheduling using Modified PSO Algorithm in cloud computing environment.International Conference on Machine Learning, Electrical and Mechanical Engineering; 2014. p. 37–41.
  • Imran MA, Pandey M, Rautaray SS. A proposal of resource allocation management for cloud computing. International Journal of Cloud Computing and Services Science. 2014; 3(2):79–86.
  • Jamuna RMR, Gouda KC, Nirmala N. Load balancing technique for climate data analysis in cloud computing environment. International Journal of Science, Engineering and Computer Technology. 2013; 3(5):183–85.
  • Namrata G, Garala K, Maheta P. Cloud load balancing based on ant colony optimization algorithm. IOSR Journal of Computer Engineering (IOSR-JCE); 2015. p. 11–18.
  • Danilo A. Quality-of-service in cloud computing: modeling techniques and their applications. Journal of Internet Services and Applications. 2014; 5(1):1–17.
  • Jia Z. A Heuristic clustering-based task deployment approach for load balancing using Bayes Theorem in cloud environment. IEEE Transactions on Parallel and Distributed Systems. 2016; 27(2):305–16. Crossref
  • Kunjal G, Goswami N, Maheta ND. A performance analysis of load Balancing algorithms in Cloud environment. 2015 International Conference on Computer Communication and Informatics (ICCCI), IEEE; 2015. p. 4–9.
  • Beghdad BK, Benhammadi F, Benaissa F. Balancing heuristic for independent task scheduling in cloud computing.2015 12th International Symposium on Programming and Systems (ISPS), IEEE; 2015.
  • Aditi S, Sharma S. Credit based scheduling using deadline in cloud computing environment. International Conference on Resent Innovation in Science Engineering and Management; 2016. p. 208–16.
  • Sukhjinder GS, Vivek T. Implementation of a hybrid load balancing algorithm for cloud computing. International Conference on Science, Technology and Management; 2016. p. 173–82.
  • Mohana PS, Subramani B. A new approach for load balancing in cloud computing. International Journal ofEngineering and Computer Science. 2013.
  • Shreya S, Kaur A. Load balancing in cloud computing using Shortest Job First and Round Robin Approach. International Journal of Science and Research. 2015; 9(4):1577–80.
  • Divya C, Chhillar RS. A new load balancing technique for virtual machine cloud computing environment.International Journal of Computer Applications. 2013; 69(23):37–40. Crossref
  • Yang X, HongTao L. Load balancing of virtual machines in cloud computing environment using improved ant colony algorithm. International Journal of Grid and Distributed Computing. 2015; 8(6):19–30. Crossref
  • Abbas RH, Katti CP, Saxena CP. A load balancing strategy for Cloud Computing environment. 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT), IEEE; 2014.
  • Babu DL, Venkata PK. Honey bee behavior inspired load balancing of tasks in cloud computing environments.Applied Soft Computing. 2013; 13(5):2292–303. Crossref
  • Elhossiny I, El-Bahnasawy N, Omara FA. Job scheduling based on harmonization between the requested and available processing power in the cloud computing environment.International Journal of Computer Applications.2015; 125(13):1–4.
  • Elrasheed I, Alamri F. Optimized load balancing based task scheduling in cloud environment. International Journal of Computer Applications; 2014.p. 35–8.
  • Ali A, Omara FA. Task scheduling using hybrid algorithm in cloud computing environments. IOSR Journal of Computer Engineering. 2015; 17(3):96–106.
  • Sourav B. Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud. Arabian Journal for Science and Engineering. 2015; 40(5):1409–25.Crossref
  • Nizomiddin BK, Choe TY. Dynamic task scheduling algorithm based on ant colony scheme. International Journal of Engineering and Technology (IJET). 2015; 7(4):1163–72.
  • Saleh A, Yussof S, Ezanee M, Almiani M. A review energy-efficient task scheduling algorithms in cloud computing.Long Island Systems, Applications and Technology Conference (LISAT); 2016.
  • Vinay D, Shah J, Mehta R. Dynamic load balancing for cloud computing using heuristic data and load on server.IOSR Journal of Computer Engineering (IOSR-JCE). 2014; 16(4):59–69. Crossref
  • Hussain MSH, Latiff MSA, Coulibaly Y. An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian Journal of Science and Technology. 2016; 9(4):1–14.
  • Kritika S, Maini R. Comparative analysis of host utilization thresholds in cloud datacenters. International Journal of Computer Applications. 2015; 120(2):9–13. Crossref
  • Lodhi V, Sarveshrai, Vishwakarma GK, Enhanced minimum utilization VM selection mechanism for clouds.International Journal of Computer Science and Information Technologies. 2015; 6(3):2975–77.
  • Dinesh K, Poornima G, Kiruthika K. Efficient resources allocation for different jobs in cloud. International Journal of Computer Applications. 2012; 56(10):30–5. Crossref


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