Dynamic Scheduling in Cloud Computing using Particle Swarm Optimization

Objectives: Dynamic assignment of tasks to different VM in cloud datacenter and Load-Balancing by migration of tasks from an Overloaded VM to Candidate VM. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) algorithm is used finding each iteration the P_BEST (Personal Best Solution) i.e. current solution is compared with the G_BEST (Global Best Solution) i.e. previous best solution and the G_BEST value is updated at each iteration for calculating the minimum execution time. The parameters being calculated are Global_BEST Solution Function, Personal_BEST Solution Function and Average Utilization for each processor. Findings: A Dynamic Approach for Task-Scheduling using the load-balancing technique is implemented in this paper. Two algorithms namely - Particle Swarm Optimization (PSO) and Improved PSO are used and a comparison is made between them based on number of performance parameters like Scheduling Length (Make Span), Total Execution Time and Total Transfer or Migration Time. A Utilization Graph is plotted to show this comparison which compares these algorithms based on their Cloudlet Length (Scheduling Length) and Total Execution Time. Improved PSO algorithm has lesser or minimum execution time as compared to the PSO algorithm because in the Improved PSO Algorithm two parameters are being considered namely- Cloudlet Length and MIPS (Million Instructions Per Second) which leads to maximum utilization of available resources by all VM. Application/Improvements: This approach is used for dynamically assigning tasks to VM and checking maximum utilization of available resources through load balancing and minimizing the overall execution time and migration time.