Latency-based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacentres

Cloud datacentres are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviours at the datacentres are being the focus of recent research in the context of sustainable datacentres. But prediction analytics of Cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-RVLBPNN (K-means based Rand Variable Learning Rate Back Propagation Neural Network) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and BPNN (Back Propagation Neural Network) algorithm. Experiments conducted on real-world Cloud datasets exhibit that the proposed model exhibits better prediction accuracy, outperforming traditional Hidden Markov Model, Naive Bayes Classifier and our earlier RVLBPNN model respectively.