A Review of Particle Swarm Optimization in Cloud Computing

AbstractTo meet the ever-growing demand for the online computational resources, it is mandatory to have the best resource allocation algorithm to allocate the resources to its end users. For most of the Internet of Things applications, the destination for the generated data is the cloud. The data may be processed instantaneously, or it may be done afterward depending on the type of data and the applications which generated that data or depending on applications that consume this data and produce some analysis result. Many algorithms have been proposed in this area, few of them are the linear method, few used heuristics, few using artificial intelligence and machine learning, and few used the meta-heuristic approach. Out of all the available methods, meta-heuristic algorithms stand out. Particle swarm optimization (PSO) is a meta-heuristic powerful technique of optimization technique that concerns the finding of maxima or minima of functions in the possible region. This chapter provides a review and discussion on different variations of the PSO algorithm and also compares different PSO optimization algorithms.KeywordsParticle swarm optimizationCloud computingInfrastructure as a serviceVirtual machineResource optimizationNature-inspired algorithms