Computing and Information Systems - Theses
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ItemProfit optimization of resource management for big data analytics-as-a-service platforms in cloud computing environments( 2020)Discovering optimal resource management solutions to support data analytics to extract value from big data is an increasingly important research area. It is fair to say that the success of many organizations, companies, and individuals now relies heavily on data analytics solutions. Cloud computing greatly supports big data analytics by providing scalable resources based on user demand and supporting elastic resource provisioning in a pay-as-you-go model. Big data Analytics as a Service (AaaS) platforms provision AaaS to various domains as consumable services in an easy to use manner across cloud computing environments. AaaS platforms aim to deliver efficient data analytics solutions to benefit decision-making and problem solving in a wide range of application domains such as engineering, science, and government. However, big data analytics solutions face a range of challenges: the dynamic nature of query requests; the heterogeneity of cloud resources; the different Quality of Service (QoS) requirements; the potential for lengthy data processing times and associated expensive resource costs and dealing with big data processing demands under potentially limited/constrained budgets, deadlines and/or data accuracies. The above challenges need to be tackled by efficient resource management solutions to support AaaS platforms to deliver reliable, cost-effective and fast AaaS. Optimal resource management solutions are essential for AaaS platforms to maximize profits and minimize query times while guaranteeing Service Level Agreements (SLAs) during AaaS delivery. To tackle the above challenges, this thesis systematically studies profit optimization solutions to support AaaS platforms. Key contributions are made through a range of resource management solutions. These include admission control and resource scheduling algorithms that enable various problem scenarios where data needs to be processed under heterogeneous, constrained or limited budgets, deadlines, or accuracies with support of data splitting and/or data sampling-based methods to reduce data processing times and costs with potential accuracy trade-offs. These algorithms allow AaaS platforms to optimize profits and minimize query times through optimal resource management solutions, and thereby increase market share by maximizing query admissions and improve reputation by delivering SLA-supported AaaS solutions.