Special Issue Article
Improving energy and cost-effective workflow scheduling in cloud computing
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Cloud computing offers utility-oriented IT services to users worldwide applications. However, datacenters hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs to the environment. The solution for this problem is that we need Green Cloud computing. This process may not only minimize operational costs but also reduce the environmental impact. The two heuristic strategies used in existing system for minimizing the cost. First strategy dynamically maps tasks to the most cost-efficient VMs based on the concept of Pareto dominance. Second strategy describes that it reduces the monetary costs of non-critical tasks and it complement of first one.In the previous system we have high energy consumption problem.To overcome this drawbacks we propose a energyaware allocation heuristics provision datacenters resource to client applications in a way that improves energy efficiency of the datacenter, while delivering the negotiated Quality of Service (QoS). Through simulation-based studies, we shows our algorithm reduce monetary costs while producing makespan as good as the best known task-scheduling algorithm.