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One of the challenges of cloud computing is effective resource management due to its auto-scaling feature. Prediction techniques have been proposed for cloud computing to improve cloud resource management. This paper proposes an autonomic prediction suite to improve the prediction accuracy of the auto-scaling system in the cloud computing environment. Towards this end, this paper proposes that the prediction accuracy of the predictive auto-scaling systems will increase if an appropriate time-series prediction algorithm based on the incoming workload pattern is selected. To test the proposition, a comprehensive theoretical investigation is provided on different risk minimization principles and their effects on the accuracy of the time-series prediction techniques in the cloud environment. In addition, experiments are conducted to empirically validate the theoretical assessment of the hypothesis. Based on the theoretical and the experimental results, this paper designs a self-adaptive prediction suite. The proposed suite can automatically choose the most suitable prediction algorithm based on the incoming workload pattern.

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The growth in the number of mobile subscriptions has led to a substantial increase in the mobile network bandwidth demand. The mobile network operators need to provide enough resources to meet the huge network demand and provide a satisfactory level of Quality-of-Service (QoS) to their users. However, in order to reduce the cost, the network operators need an efficient network plan that helps them provide cost effective services with a high degree of QoS. To devise such a network plan, the network operators should have an in-depth insight into the characteristics of the network traffic. This paper applies the time-series analysis technique to decomposing the traffic of a commercial trial mobile network into components and identifying the significant factors that drive the traffic of the network. The analysis results are further used to enhance the accuracy of predicting the mobile traffic. In addition, this paper investigates the accuracy of machine learning techniques-Multi-Layer Perceptron (MLP), Multi-Layer Perceptron with Weight Decay (MLPWD), and Support Vector Machines (SVM)-to predict the components of the commercial trial mobile network traffic. The experimental results show that using different prediction models for different network traffic components increases the overall prediction accuracy up to 17%. The experimental results can help the network operators predict the future resource demands more accurately and facilitate provisioning and placement of the mobile network resources for effective resource management.

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