Abstract
As is well-known Cloud is an Environment for sharing resources based on Anything as a Service (XaaS) pattern that includes software, platform, infrastructure, storage, etc. on demand. For allocating resources and managing it efficiently, the load has to be balanced on the cloud paradigm. Moreover, the reliable resource allocation with load balancing has become the significant resource focus in the current scenario. In the heterogeneous cloud environment, dispersion and uncertainty of cloud resources faces issues on the process of allocation that are not effectively handled and accessed by the existing approaches. With that concern, for providing proficient resource scheduling with apposite load balancing, an efficient load-balancing model based on modified particle swarm optimization with membrane computing has been proposed. Based on that, suitable resources are allocated for different jobs in accordance with the factors like completion time, scalability, makespan, utilization of resources, reliability, availability, etc. Moreover, in this paper, effective resource scheduling has been achieved with the modified particle swarm optimization that combined with membrane computing local and glob optimization of inter-membranes for providing an optimal solution. Spatial segmentation has also been performed for enhancing the membrane-based optimization.
Similar content being viewed by others
References
Aggarwal A, Jain S (2014) Efficient optimal algorithm and task scheduling in cloud computing environment. Int J Comput Trends Technol (IJCTT) 9:344–349
Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a berkeley view of cloud computing. University of California, Berkeley, Technical report. USB-EECS-2009-28
Bendraouche M, Boudhar M, Oulamara A (2015) Scheduling: agreement graph vs. resource constraints. Eur J Oper Res 240:585–586
Dong Z, Nan W, Xu L (2011) The bilateral resource integration service system. In: The proceedings of IEEE international conference on computational and information sciences
Dong WE, Nan WU, Xu L (2013) QoS-oriented monitoring model of cloud computing resources availability. In: Proceedings of IEEE international conference on computational and information sciences, pp 1537–1540
Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53–66
Ge-Xiang Z (2010) A survey of membrane computing as a new branch of natural computing. Chin J Comput 33(2):208–214
Guo-Ning G, Ting-Lei H (2010) Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: Proceedings of international conference on intelligent computing and integrated systems, pp 60–63
Herroelen W, Demeulemeester E, De Reyck B (1998) Resourceconstrained project scheduling—a survey of recent developments. Comput Oper Res 25(4):279–302
James J, Verma B (2012) Efficient VM load balancing algorithm for a cloud computing environment. Int J Comput Sci Eng 4(09):1658–1663
Kapur R (2015) Review on nature inspired algorithms in cloud computing. In: Proceedings of IEEE international conference on computing, communication and automation (ICCCA-2015), School of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India, May 15–16, 2015
Li G, Sun H, Gao H, Yu H, Cai Y (2009) A survey on wireless grids and clouds. In: 8th IEEE international conference on grid and cooperative computing, pp 261–267
Li K, Xu G, Zhao G, Dong Y, Wang D (2011) Cloud task scheduling on load balancing ant colony optimization. In: The proceedings of IEEE 6th annual ChinaGrid conference, pp 3–9
Lizheng G, Shuguang Z, Shigen S et al (2012) Task scheduling optimization in cloud computing based on heuristic algorithm. J Netw 7(3):547–553
Nan X, He Y, Guan L (2011) Optimal resource allocation for multimedia cloud based on queuing model. In: The proceedings of IEEE international workshop on multimedia signal processing (MMSP), pp 1–6
Nan X, He Y, Guan L (2012) Optimal resource allocation for multimedia cloud in priority service scheme. In: The proceedings of IEEE international symposium on circuits and systems (ISCAS), pp 1–4
Nan X, He Y, Guan L (2013) Optimization of workload scheduling for multimedia cloud computing. In: The proceedings of IEEE international symposium on circuits and systems (ISCAS), pp 1–4
Santhosh R, Ravichandran T (2012) Non-preemptive on-line scheduling of real-time services with task migration for cloud computing. Eur J Sci Res 89(1):163–169
Santhosh R, Ravichandran T (2013) Pre-emptive scheduling of on-line real time services with task migration for cloud computing. In: Proceedings of IEEE conference on pattern recognition, informatics and mobile engineering (PRIME), pp 1–6
Sun X, Su S, Xu P, Jiang L (2011) Optimizing multi-dimentional resource utilization in virtual data center. In: Proceedings of IEEE international conference on broadband network and multimedia technology (ICBNMT), pp 1–6
Wu Y, Wu C, Li B, Qiu X, Lau F (2011) Cloudmedia: when cloud on demand meets video on demand. In: Proceedings of IEEE conference on distributed computing systems (ICDCS), pp 268–277
Yadav AK, Dutta M (2014) A novel approach to provide broking service in cloud computing. Thesis, NITTTR, Panjab University, Chandigarh, India
Zhu W, Luo C, Wang J, Li S (2011) Multimedia cloud computing. IEEE Signal Process Mag 28(3):59–69
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sundaramoorthy, P., Selvam, M., Karthik, S. et al. Holding maximum customers in cloud business environment by efficient load balancing methods based on MPSO-MC. Inf Syst E-Bus Manage 18, 295–309 (2020). https://doi.org/10.1007/s10257-019-00413-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10257-019-00413-y