Abstract
Cloud computing has emerged as an optimal option for almost all computational problems today. Using cloud services, customers and providers come to terms of usage conditions defined in Service Agreement Layer (SLA), which specifies acceptable Quality of Service (QoS) metric levels. From the view of cloud-based software developers, their application-level SLA must be mapped to provided virtual resource-level SLA. Hence, one of the important challenges in clouds today is to improve QoS of computing resources. In this paper, we focus on developing a comprehensive autoscaling solution for clouds based on forecasting resource consumption in advance and validating prediction-based scaling decisions. Our prediction model takes all advantages of fuzzy approach, genetic algorithm and neural network to process historical monitoring time series data. After that the scaling decisions are validated and adapted through evaluating SLA violations. Our solution is tested on real workload data generated from Google data center. The achieved results show significant efficiency and feasibility of our model.
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