Cloud Task Scheduling Based on Policy Gradient Algorithm in Heterogeneous Cloud Data Center for Energy Consumption Optimization

The appearance of cloud computing has provided an efficient and economical way for users to receive computing resources with a pay-as-you-go model from cloud service provider. Cloud computing has attracted wide attention in both academia and industrial application. However, with the widely deploying of large-scale data center, high energy consumption becomes one of the key challenges and difficulties that restricting the development of cloud computing. Due to the limitations and dynamics of resources, how to map the cloud tasks to the resources aiming at decreasing the energy consumption has remained an essential issue. In this paper, we have made some improvements to the DeepJS and present DeepEnergyJS to minimize energy consumption for data center that receives enormous numbers of tasks arrive dynamically. We firstly introduce a power model to estimate the power of data center of the moment, then design a reward function that reflects the goal of optimizing power consumption and implement the simulation experiment on independent workloads to verity its availability of reducing the energy consumption. Finally, DeepJS considered only the independent workloads, we model inter-task dependencies in jobs and then conduct experiments in a similar way with independent workloads to prove that DeepEnergyJS can be extended for workloads that with dependencies which make it more functional. What's more, our experiments are based on different configured servers while the severs used in DeepJS are all the same, which make our study closer to the real cloud computing environment. The experiments results show that DeepEnergyJS can significantly outperforms three baselines in both independent workloads and workloads with dependencies.