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Modern Internet services are increasingly leveraging on cloud computing forflexible, elastic and on-demand provision. Typically, Quality of Service (QoS)of cloud-based services can be tuned using different underlying cloudconfigurations and resources, e.g., number of threads, CPU and memory etc.,which are shared, leased and priced as utilities. This benefit is fundamentallygrounded by autoscaling: an automatic and elastic process that adapts cloudconfigurations on-demand according to time-varying workloads. This thesisproposes a holistic cloud autoscaling framework to effectively and seamlesslyaddress existing challenges related to different logical aspects ofautoscaling, including architecting autoscaling system, modelling the QoS ofcloud-based service, determining the granularity of control and decidingtrade-off autoscaling decisions. The framework takes advantages of theprinciples of self-awareness and the related algorithms to adaptively handlethe dynamics, uncertainties, QoS interference and trade-offs on objectives thatare exhibited in the cloud. The major benefit is that, by leveraging theframework, cloud autoscaling can be effectively achieved without heavy humananalysis and design time knowledge. Through conducting various experimentsusing RUBiS benchmark and realistic workload on real cloud setting, this thesisevaluates the effectiveness of the framework based on various qualityindicators and compared with other state-of-the-art approaches.

Self-Aware and Self-Adaptive Autoscaling for Cloud Based Services