A cost constrained resource scheduling optimization algorithm for reduction of energy consumption in cloud computing

In order to solve the serious energy consumption problem in cloud computing, a cost constrained resource scheduling optimization algorithm for reduction of the energy consumption in cloud computing was proposed based on the intensive study of the resource utilization of cloud data centers, the network workloads and the real-time power. Then the electricity cost was considered according to the world time zones, and the tasks were scheduled and performed by using the load-balancing method. Based on the above researches, the task slicing algorithm (TSA) was designed to reduce the idle probability of data centers and the energy consumption of data transmission between the centers through increasing the parallelism degree and dependency of tasks. When the cost constraint was not satisfied, tasks were iteratively calculated according to the algorithm. The results of the simulating experiments show that the algorithm can significantly save the service cost while optimizing the energy consumption.