A New Balanced Particle Swarm Optimisation for Load Scheduling in Cloud Computing

The cloud computing is an augmentative and progressive paradigm that supports a huge amount of characteristics. It demands the optimal allocation of resources to the tasks present in the virtual machines (VMs) system using load scheduling algorithms. The basic objective of load scheduling is to avoid system overloading and thereby achieve higher throughput by maximising VM utilisation along with cost stabilisation. The first come first serve and min–min approaches allocate the load in a static manner and resources are left underutilised. The particle swarm optimisation obtains the motivation from the social behaviour of the flock of birds. It analyses various approaches for load scheduling. The paper proposes an improved balanced load scheduling approach based on particle swarm optimisation (BPSO) to minimise total transfer time and total cost stabilisation. The proposed BPSO approach is compared with the existing approaches used for load scheduling in cloudlets. The efficiency in terms of the transfer time and cost of the proposed algorithm is showcased with the help of simulation results. As evident from the results, the proposed algorithm reduces transfer time and cost than the prevalent algorithms thereby making a system with stable cost.