LAUSR

Cloud computing has developed as a high-performance computing environment with a huge set of virtualized, abstracted, and flexible resource. It provides service to the user with high-performance. In a large-scale cloud computing environment, the cloud data centers and users are distributed physically across the globe. In a distributed environment, the arrangement of scientific workflow is considered as a popular NP-complete problem and they prevails to be intractable. An extra-ordinary issue in the distributed environment is scientific workflow scheduling and it is difficult to track the exact solution. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The biggest challenge for cloud data centers is how to handle and service the millions of requests that are arriving very frequently from end users efficiently and correctly. The aim of this study is to obtain an efficient load-balancing in the large-scale platform of cloud computing based on the proposed Meta-heuristic based multi objective optimisation. The main contributions of this paper are related to the scheduling of tasks to the resource groups using multi-objective memetic algorithm (MOMA), it uses a local search technique to reduce the likelihood of the premature convergence. To reschedule the failed workload to achieve fault tolerance an adaptive plant intelligent behavior optimization (APIBO) is proposed. The experiments using different scientific workflow applications highlight the effectiveness, usefulness, and better performance of the proposed approach and the Performances are evaluated in terms of resource contention, response time, execution time, throughput, and resource utilization.