MULTICRITERIA OPTIMIZATION METHOD FOR LOAD BALANCING IN CLOUD COMPUTING
Optimizing of the task scheduling process in the cloud environment is a multicriteria NP-hard problem. In this paper, weighted load balancing method (αPSO – TBLB) based on PSO algorithm is proposed. The method provides optimal migration of tasks from the loaded virtual machines to the less loaded virtual machine to prevent the excessive load in virtual machines of the cloud infrastructure. In the proposed optimization method, the minimization of the processing time of tasks and the transfer time of tasks were selected as the target functions. Experimental testing of the proposed approach was carried out in the Jswarm and Cloudsim programs. As a result of the simulation on the basis of the proposed method, an optimal solution for task scheduling was found, uniform distribution of tasks in virtual machines (VMs) was provided. Moreover, in the process of assigning tasks to virtual machines, a minimal time consumption was achieved (pp.3-13).
- Metri G., Srinivasaraghavan S., ShiW., Brockmeyer M. Experimental analysis of application specific energy efficiency of datacenters with heterogeneous servers / Proc. of the IEEE 5th International Conference on Cloud Computing, 2012, pp.786−793.
- Vaquero L.M., Rodero-Merino L., Caceres J., Lindner M. A break in the clouds: towards a cloud definition // ACM SIGCOMM Computer Communication Review, 2008, vol.39, no.1, pp.50−55.
- Ramezani F., Lu J., Hussain F.K. Task-based system load balancing in cloud computing using Particle Swarm Optimization // International Journal of Parallel Programming, 2013, vol.42, no.5, pp.739−754.
- Guo L., Zhao S., Shen S., Jiang C. Task scheduling optimization in cloud computing based on heuristic algorithm //Journal of Networks, 2012. vol.7, no3, pp.547−553.
- Alguliev R.M., Alyguliev R.M., Alekperov R.K. An approach to optimal task assignment in a distributed system // Journal of Automation and Information Sciences, 2004, vol.36, no.10, pp.51–55.
- Tanaev V.S., Gordon V.S., Shafransky Ya.M. Theory of schedules. One-stage systems. M.: Science. The main edition of physical and mathematical literature, 1984, p.384.
- Wu Z.,Liu X., Ni Z.,Yuan D.,Yang Y. A market-oriented hierarchical scheduling strategy incloud workflow systems // The Journal of Supercomputing, 2013, vol.63, no.1, pp.256−293.
- AjitM.,Vidya G. VM level load balancing in cloud environment / Proc. of the fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013, pp.87−95.
- Chawla Y., Bhonsle M. A study on scheduling methods in cloud computing // International Journal of Emerging Trends &Technology in Computer Science (IJETTCS), 2012, vol.1, no.3, pp.12−17.
- MohammadM., ValiKardan S., Shahi Z., Azar S.I. Towards workflow scheduling in cloud computing: A comprehensive analysis // Journal of Network and Computer Applications, 2016, vol.66, pp. 64−82.
- Milani A.S., Navimipour N.J. Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends // Journal of Network and Computer Applications, 2016, vol.71, pp.86−98.
- Ajit M., Vidya G. VM level load balancing in cloud environment / Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013, pp.1−5.
- Madni S.H., Latiff M.S., Coulibaly Y., Abdulhamid S.M. Resource scheduling for Infrastructure as a Service (IaaS) in cloud computing: Challenges and opportunities // Journal of Network and Computer Applications, 2016, vol.68, pp.173−200.
- Ramezani F., Lu J., Hussain F. Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization // Service-Oriented Computing, 2013, vol.8274, pp.237−251.
- Ramezani F., Lu J., Hussain F.K. Task-based system load balancing in cloud computing using particle swarm optimization // Knowledge Engineering and Management, 2013, pp.31−42.
- Ramezani F. Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments // World Wide Web, 2015, vol.18, no.6, pp.1737−1757.
- Dhinesh B.D., Krishna P.V. Honey bee behavior inspired load balancing of tasks in cloud computing environments // Applied Soft Computing, 2013, vol.13, no.5, pp.2292−2303.
- Babu K.R., Samuel P. Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud // Innovations in Bio-inspired Computing and Applications, 2016, pp.67−78.
- Banerjee S., Adhikari M., Kar S.,Biswas U. Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud // Arabian Journal for Science and Engineering, 2015, vol.40, no.5, pp.1409−1425.
- Liu Y., Zhang C., Li B., Niu J. DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters // Journal of Network and Computer Applications, 2015, pp.1−8.
- Cho K.M., Tsai P.W., Tsai C.W., Yang C.S. A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing // Neural Computing and Applications, 2015,vol.26,no.6, pp.1297−1309.
- Alguliev R.M., Aliguliyev R.M., Mehdiyev C.A. An optimization approach to automatic generic document summarization // Computational Intelligence,2013, vol.29, no.1, pp.129−155.
- Aliguliyev R.M. Clustering techniques and discrete particle swarm optimization algorithm for multi-document summarization // Computational Intelligence, 2010, vol.26, no.4, pp.420−448.
- Cakar T., Koker R. Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach // Computational Intelligence and Neuroscience, 2015, vol.2015, pp.1−13.