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With the increase in deployment of scientific application on public and private clouds, the allocation of workflow tasks to specific cloud instances to reduce runtime and cost has emerged as an important challenge. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters and has been proved to be NP-complete. This paper presents an optimization framework for task allocation on public clouds. We present a solution that considers important parameters such as workflow runtime, communication overhead, and overall execution cost. Our multiobjective optimization framework builds on a simple and extensible cost model and uses a heuristic to determine the optimal number of cloud instances to be used. Using the Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3) as an example, we show how our optimization heuristics lead to significantly better strategies than other state-of-theart approaches. Specifically, our single-objective optimization is slightly better than a simple heuristic and a particle swarm optimization approach for small workflows, and achieves significant improvements for larger workflows. In a similar manner, our multi-objective optimization obtains similar results to our singleobjective optimization for small-size workflows, and achieves up to 80% improvement for large-size workflows.

Team pursuit track cycling is a bicycle racing sport held on velodromes and it is part of the Summer Olympics. It involves the use of strategies to minimize the overall time that a team of cyclists needs to complete a race. We present an optimisation framework for team pursuit track cycling and show how to evolve strategies using metaheuristics for this interesting real-world problem. Our experimental results show that these heuristics lead to significantly better strategies than state-of-art strategies that are currently used by teams of cyclists.

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