Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure. User requests are submitted through an interface to these that provide service them in on-demand basis. The scientific applications get executed at by making use of heterogeneous resources being allocated a dynamic manner grouped under NP hard problem category. Task scheduling poses numerous challenges impacting performance. If not handled properly, user satisfaction becomes questionable. More recently researchers had come up meta-heuristic type solutions for enriching task activity environment. prime aim is utilize available optimal and reduce time span execution. An improvised seagull optimization algorithm which combines features Cuckoo search (CS) (SOA) been proposed this work enhance performance inside aims minimize cost parameters spent during Performance evaluation performed using Cloudsim 3.0 toolkit comparing it Multi objective-Ant Colony Optimization (MO-ACO), ACO Min-Min algorithms. SOA-CS technique produced improvement 1.06%, 4.2%, 2.4% makespan reduced overall extent 1.74%, 3.93% 2.77% when compared PSO, ACO, IDEA algorithms respectively 300 vms considered. comparative simulation results obtained shown fares better than other contemporaries.