Cloud computing is a model of acquiring and using preconfigured IT resources on demand. Cloud providers build and maintain large data centres and lease their resources to customers in a pay-as-you-go manner. This enables organisations to focus on their core lines of business instead of building and managing in-house infrastructure. Such in-house IT facilities can often be either under or over utilised given dynamic and unpredictable workloads. The cloud model resolves this problem by allowing organisations to flexibly resize/scale their rented infrastructure in response to the demand. The confluence of these incentives has caused the recent widespread adoption of cloud services.
However, cloud adoption has introduced challenges in terms of service unavailability, regulatory compliance, low network latency to end users, and vendor lock-in. These factors are of special importance for large scale interactive web-facing applications, which observe unpredictable workload spikes and need to serve users worldwide with low latency. The utilisation of multiple cloud sites (i.e. a Multi-Cloud) has emerged as a promising solution. Multi-Cloud approaches also facilitate cost reduction by taking advantage of the diverging prices in different cloud sites.
The 3-Tier architectural model is the de-facto standard approach to build interactive web systems. It divides the application in three tiers: (i) presentation tier which implements the user interfaces, (ii) domain tier implementing the core business logic, and (iii) data tier managing the persistent storage. This logical division most often leads to deployment separation as well.
This thesis investigates dynamic approaches for workload distribution and resource provisioning (a.k.a. brokering) of 3-Tier applications in a Multi-Cloud environment. It advances the field by making the following key contributions:
1. A performance model and a simulator for 3-Tier applications in one and multiple clouds.
2. A system architecture for brokering 3-Tier applications across clouds, which considers latency, availability, and regulatory requirements and minimises the overall operational costs.
3. An approach for Virtual Machine (VM) type selection that reduces the total cost within a cloud site. It uses online machine learning techniques to address the variability of both the application requirements and the capacity of the underlying resources.
4. A rule-based domain specific model for regulatory requirements, which can be interpreted by a rule inference engine.
5. Design and implementation of a workload redirection system that directs end users to the individual cloud sites in a Multi-Cloud environment.
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