Efficient resource utilization using multi-step-ahead workload prediction technique in cloud

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.

Automated summary

The demand for cloud-based services is rapidly increasing due to their scalability and cost-effectiveness. As a result, the size and maintenance cost of data centers are growing, making it crucial to develop a proper resource management plan. The proposed machine learning-based workload prediction approach in this paper improves resource utilization and reduces overall energy consumption in data centers.