Cloud computing system is a huge cluster of interconnected servers residing in a datacenter\nand dynamically provisioned to clients on-demand via a front-end interface. Scientific applications\nscheduling in the cloud computing environment is identified as NP-hard problem\ndue to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics\noptimization schemes have been applied to address the challenges of applications\nscheduling in the cloud system, without much emphasis on the issue of secure global\nscheduling. In this paper, scientific applications scheduling techniques using the Global\nLeague Championship Algorithm (GBLCA) optimization technique is first presented for\nglobal task scheduling in the cloud environment. The experiment is carried out using Cloud-\nSim simulator. The experimental results show that, the proposed GBLCA technique produced\nremarkable performance improvement rate on the makespan that ranges between\n14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule\napplications as parametrically measured in terms of the response time. In view of the experimental\nresults, the proposed technique provides better-quality scheduling solution that is\nsuitable for scientific applications task execution in the Cloud Computing environment than\nthe MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling\ntechniques.
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