Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers’ data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Use of cloud resources has increased with the increasing trend of organizations and governments towards cloud adaptation. This increase in cloud resource usage, leads to enormous amounts of energy consumption by cloud data center servers. Energy can be conserved in a cloud server by demand-based scaling of resources. But reactive scaling may lead to excessive scaling. That, in turn, results in enormous energy consumption by useless scale up and scale down. The scaling granularity can also result in excessive scaling of the resource. Without a proper mechanism for estimating cloud resource usage may lead to significant scaling overheads. To overcome, such inefficiencies, we present Cartesian genetic programming based neural network for resource estimation and a rule-based scaling system for IaaS cloud server. Our system consists of a resource monitor, a resource estimator and a scaling mechanism. The resource monitor takes resource utilizations and feeds to the estimator for efficient estimation of resources. The scaling system uses the resource estimator’s output for scaling the resource with the granularity of a CPU core. The proposed method has been trained and tested with real traces of Bitbrains data center, producing promising results in real-time. It has shown better prediction accuracy and energy efficiency than predictive scaling systems from literature.

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy.

The mounting customer demands for bandwidth-desirous services are deriving for a cost effective, robust, and high capacity wireless access network. The end users expect a satisfactory and economical delivery of “Quad-play” applications (voice, video, data, and mobility) and rich-media applications (multimedia, interactive gaming, and meta-verse) over the wireless network. In last two decades, a remarkable evolution of wireless networks is observed. Moreover, after the advent of software defined radio enabled wireless sets, the selection of the optimum wireless access network for different applications (live video streaming, online gaming, voice calling and browsing) is gaining vital importance. In this paper, a ranking algorithm based on Markov chain optimized learning approach is formulated for the heterogeneous environment. The algorithm is designed on the basis of most important Quality of Service (QoS) parameters like throughput, delay/error and cost. The proposed technique is robust against the change in number of available networks where, previously proposed techniques TOPSIS, VIKOR and RafoQ are unable to handle the change in available networks adequately. The Simulation results verify the selection of optimal access network for varying applications conforming to defined ranking algorithm.

KSII Transactions on Internet and Information Systems TIIS Paper Details