The increasingly diverse nature of modern applications makes it critical for future systems to have dynamic resource scaling capabilities which enable them to adapt their resource usage to meet user requirements. Such mechanisms should be both fine-grained in nature for resource-efficient operation and also provide a high scaling range to support a variety of applications with diverse needs. To this end, heterogeneous platforms, consisting of components with varying characteristics, have been proposed to provide improved performance/efficiency than homogeneous configurations, by making it possible to execute applications on the most suitable component. However, introduction of such heterogeneous architectural components requires system software to embrace complexity associated with heterogeneity for managing them efficiently. Diversity across vendors and rapidly changing hardware make it difficult to incorporate heterogeneity-aware resource management mechanisms into mainstream systems, affecting the widespread adoption of these platforms. Addressing these issues, this dissertation presents novel abstractions and mechanisms for heterogeneous platforms which decouple heterogeneity from management operations by masking the differences due to heterogeneity from applications. By exporting a homogeneous interface over heterogeneous components, it proposes the scalable 'resource state' abstraction, allowing applications to express their resource requirements which then are dynamically and transparently mapped to heterogeneous resources underneath. The proposed approach is explored for both modern mobile devices where power is a key resource and for cloud computing environments where platform resource usage has monetary implications, resulting in HeteroMates and HeteroVisor solutions. In addition, it also highlights the need for hardware and system software to consider multiple resources together to obtain desirable gains from such scaling mechanisms. The solutions presented in this dissertation open ways for utilizing future heterogeneous platforms to provide on-demand performance, as well as resource-efficient operation, without disrupting the existing software stack.

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches.