Optimization of Cloud Migration Parameters Using Novel Linear Programming Technique

The work presents a linear programming-based transportation model approach known as improved modified distribution load balancing algorithm (IMDLBA) to enhance the migration parameters. IMDLBA is a part of reactive load balancing mechanism that relies on the process of migration to deal with workload imbalances across the virtual resources. The important migration parameters considered in this work are migration cost, degree of balance, number of task migrations, and number of machines required for migration. The model has been reviewed with respect to existing meta-heuristics—improved weighted round robin (IWRR), honey bee behavior load balancing (HBB-LB), dynamic load balancing (DLB), and HDLB algorithms, in terms of above cited parameters which come under the class of quality of service (QoS) metrics. Experimental analysis and evaluations from IMDLB algorithm revealed the significant reduction in migration cost and improvement in balance factor—a metric that define the degree of balance if VMs. A balance factor of around 31% has been enhanced compared to the existing methods. The IMDLB algorithm also works by performing one time migration on a given set of tasks. The IMDLB algorithm reduces the number of task migrations by 28.5, 51.25, 58.33, 75.16, and 75.19% with reference to IWRR (time-shared), IWRR (space-shared), HBB-LB, DLB, and HDLB respectively. Further the minimum number of machine combinations required for performing load balancing is also achieved. The research article takes into consideration five UN sustainable development goals namely SDG7, SDG 8, SDG 9, SDG 11, and SDG 12.