Abstract
Infrastructure-as-a-service cloud environment provides infrastructure services per the user's workflow requirements using the pay-you-go model on demand. Workflow applications are highly used in IT industries, project management, supply chain management, large distributed management, and many more. Load-balanced workflow allocation to solve all the requirements of the cloud users is already proven NP-hard. In this scenario, meta-heuristic algorithms are one of the best candidates to solve the load balancing problem in the cloud to achieve an optimal allocation schedule. In this paper, we proposed a load-balanced workflow allocation method using stochastic fractal search (SFS) algorithms to maximize the resource utilization of the virtualized cloud resources. In this way, the load imbalance on virtual machines can be minimized. SFS is an evolutionary optimization method that uses the natural growth behavior in the search process. The performance analysis is conducted by implementing the workflow load balancer in MATLAB. The study shows that the solutions achieved by the proposed SFS strategy are far better than those obtained by particle swarm optimization (PSO).
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