The demand of cloud-based services is growing rapidly due to the high scalability and cost-effective nature of cloud infrastructure. As a result, the size of the data center is increasing drastically, so is the cost of maintenance in terms of resource management and energy consumption. Hence, it is important to develop a proper resource management plan to maximize the profit by reducing the overhead of operational cost. In this paper, we propose a multi-step-ahead workload prediction approach using Machine learning techniques and allocate the resources based on this prediction in a way that allows the resources to be utilized more efficiently and thereby, reducing the data center’s overall energy consumption. We evaluate the effectiveness of our framework based on real workload trace of Bitbrains. Experimental results show that our framework outperforms other state-of-the-art approaches for predicting workload over a long-run and significantly improves resource utilization while enabling substantial energy savings.

由于云基础架构的高度可扩展性和成本效益，对基于云的服务的需求正在迅速增长。结果，数据中心的规模急剧增加，就资源管理和能源消耗而言，维护成本也在增加。因此，重要的是要制定适当的资源管理计划，以通过减少运营成本的开销最大化利润。在本文中，我们提出了一种使用机器学习技术的多步提前工作量预测方法，并基于此预测分配资源，从而可以更有效地利用资源，从而减少数据中心的总体能耗。我们根据Bitbrains的实际工作量跟踪评估我们框架的有效性。