Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio-economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU-SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state-of-art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real-world Cloud trace logs demonstrate that the proposed IGRU-SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.

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.

Energy-aware datacenter operation is gaining attention in the recent years, having witnessed the increased deployments and usage of cloud datacenters resulting in significant amount of carbon footprints to directly affect environmental sustainability. Since providers tend to record user behaviors and cloud jobs profiles, analyzing cloud trace logs can provide insightful inferences to move towards datacenter sustainability. This paper investigates the trace logs of real datacenter workload arrival trend in order to characterize the resource request trend of users. Furthermore, anticipated resource request trends in terms of the requested level of CPU and memory resources are forecasted. A novel prediction model, named LSTMtsw, is proposed based on recurrent neural networks of deep learning, which exploits historical data in combination with the current job arrival trend to predict the future resource request trend of users. By the way of constructing sliding windows of historical data, the proposed model effectively reduces the number of iterations involved in the prediction process, and predicts the future trend with reduced error percentage. In addition, the proposed LSTMtsw model integrates an effective mechanism for controlling the prediction effect to achieve energy conservation without degrading the service quality. Experiments conducted on 600 hours of cloud trace logs demonstrate the efficiencies of the proposed model in outperforming the existing BPNN and LSTM models in terms of the achieved prediction accuracy whilst predicting the future resource demands of users.