Multi-objective cloud load-balancing with hybrid optimization

AbstractIn this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence.ABBREVIATION: CDC, cloud data center; CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing; CSP, Cloud service providers; CSSA, Chaotic Squirrel Search Algorithm; DA, Dragonfly Algorithm; ED, Euclidean Distance; EDA-GA, Estimation Of Distribution Algorithm And GA; FF, FireFly algorithm; GA, Genetic Algorithm; HHO, Harris Hawk Optimization; IaaS, Infrastructure-as-a-Service; MGWO, Modified Mean Grey Wolf Optimization Algorithm; MMHHO, Mantaray modified multi-objective Harris Hawk optimization; MRFO, Manta Ray Forging Optimization; PaaS, Platform-as-a-Service; PM, Physical Machine; PSO, Particle Swarm Optimization; SaaS, Software-as-a-Service; SAW, Sample additive weighting; SLA-LB, Service Level Agreement-Based Load Balancing; TBTS, Threshold-Based Task Scheduling Algorithm; TS, Task SchedulingKEYWORDS: Cloud computingload balancingDCCOpower consumptionmemory utilizationmigration cost Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKoppula GeetaKoppula Geeta, Currently working as Assistant Professor of Computer Science & Engineering at Rajiv Gandhi University of Knowledge Technologies Basar, She is having 18 years of teaching experience. Received her B.Tech, M.Tech from JNTUH. Currently she is pursuing PhD in JNTUH, Hyderabad. Her main research interests includes Cloud computing, Data mining.V. Kamakshi PrasadProfessor V. Kamakshi Prasad currently serving as a Senior Professor of Computer Science & Engineering at JNTUH College of Engineering Science & Technology in Hyderabad, has 31 years of teaching and research experience. He obtained his B.Tech., M.Tech., and Ph.D. degrees from KLCE, Andhra University College of Engineering, and IIT Madras, respectively. He joined JNTU as an Assistant Professor in 1992 and was subsequently promoted to the positions of Associate Professor, Professor, and Senior Professor in 2003, 2006, and 2016, respectively. Throughout his tenure, he has held various administrative roles within the University, including Additional Controller of Exams, Coordinator of TEQIP-II, Head of the Department of CSE, Controller of Exams, Director of Innovative Technologies, Director of Evaluation, and currently serves as the Chairperson of the Board of Studies for CSE and CSE allied branches. Additionally, he is actively involved as a member of the Board of Studies, Academic Councils, and Governing Bodies of several autonomous and non-autonomous colleges affiliated with JNTUH and other Universities. He has also served as the Visitor's (President of India) nominee for the Executive Council of MANUU, Hyderabad and as a board member of the School of Computer and Information Sciences (SCIS) at Hyderabad Central University. In recognition of his contributions, he received the Telangana Government's state teacher award for the year 2020.His research interests encompass a wide range of areas, including Quantum Computing, Machine Learning, Data Mining, Speech & Image Processing, and Theoretical Computer Science. He has successfully supervised 29 Ph.D. candidates and 3 MS degree holders, while currently guiding 8 more Ph.D. research scholars.