Swarm Intelligence (SI) based profiling and scheduling of big data applications

Personalization targets a user's software, hardware, and QoS requirements at any given moment in the cloud environment for the big data applications. However, the individualization aims to target the daily needs of an individual user in a dynamic manner. The proposed research work aims to design a system which will be able to optimize user's applications towards a specified target goal. Furthermore, it is integrated with a Particle Swarm Optimization (PSO) based application profiling and resource selection mechanism which comes from the family of Swarm Intelligence (SI). The proposed algorithms create an application profile template and preferred resource list for each submitted big data applications and select the cloud resources from the preferred resource list which is based on the application preferences and availability of cloud resources in an optimal manner. From the experimental results, it is evident that the proposed research work maximizes the application success ratio, scheduling success rate, utilization of cloud resources, and user satisfaction.