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

TL;DR

This paper introduces a comprehensive self-aware and self-adaptive autoscaling framework for cloud services, improving automatic resource management by addressing uncertainties, interference, and trade-offs without heavy manual intervention.

Contribution

It presents a holistic autoscaling framework leveraging self-awareness principles to handle cloud dynamics, uncertainties, and QoS trade-offs effectively and seamlessly.

Findings

Framework improves autoscaling efficiency and responsiveness.

Experimental results show better QoS and resource utilization.

Outperforms existing state-of-the-art approaches.

Abstract

Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations and resources, e.g., number of threads, CPU and memory etc., which are shared, leased and priced as utilities. This benefit is fundamentally grounded by autoscaling: an automatic and elastic process that adapts cloud configurations on-demand according to time-varying workloads. This thesis proposes a holistic cloud autoscaling framework to effectively and seamlessly address existing challenges related to different logical aspects of autoscaling, including architecting autoscaling system, modelling the QoS of cloud-based service, determining the granularity of control and deciding trade-off autoscaling decisions. The framework takes advantages of the principles of self-awareness and the related algorithms to adaptively handle the dynamics, uncertainties, QoS interference and trade-offs on objectives that are exhibited in the cloud. The major benefit is that, by leveraging the framework, cloud autoscaling can be effectively achieved without heavy human analysis and design time knowledge. Through conducting various experiments using RUBiS benchmark and realistic workload on real cloud setting, this thesis evaluates the effectiveness of the framework based on various quality indicators and compared with other state-of-the-art approaches.