Abstract
As we come to terms with various big data challenges, one vital issue remains largely untouched. That is service level agreement (SLA) management to deliver strong Quality of Service (QoS) guarantees for big data analytics applications (BDAA) sharing the same underlying infrastructure, for example, a public cloud platform. Although SLA and QoS are not new concepts as they originated much before the cloud computing and big data era, its importance is amplified and complexity is aggravated by the emergence of time-sensitive BDAAs such as social network-based stock recommendation and environmental monitoring. These applications require strong QoS guarantees and dependability from the underlying cloud computing platform to accommodate real-time responses while handling ever-increasing complexities and uncertainties. Hence, the over-reaching goal of this PhD research is to develop novel simulation, modelling and benchmarking tools and techniques that can aid researchers and practitioners in studying the impact of uncertainties (contention, failures, anomalies, etc.) on the final SLA and QoS of a cloud-hosted BDAA.
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