Energy-aware application scheduling based on genetic algorithm

As cloud computing is expected to expand rapidly in the coming years, the large-scale computing and data centers are becoming more and more widespread in the world. Energy consumption of these distributed systems has become a urgent problem and received much attention. Application Scheduling can alleviate this problem by reducing the number of running nodes and effectively maximizing total system efficiency. This paper focuses on scheduling applications in large-scale data centers using genetic algorithm. Specifically, we present the design and implementation of the cost function, the modification of the genetic operators and the choice of the data transition weight. The algorithm is studied via simulation and implementation in a large-scale data center. Test results and performance discussion justify the feasibility of the scheduling algorithm. From the results, we know that the proposed application scheduling method can be useful in practice, which can reduce the running nodes and minimize the cost of data transferred among the nodes efficiently.