Resource Allocation for Improved Performance and Resource Efficiency in Cloud Computing

Allocating resources for applications is attributed to cost-efficiency measures only in the light of two other factors of paramount importance, namely application performance achieved and resource efficiency associated. Achieving satisfactory performance within QoS requirements is indeed the foremost objective to attain for any given application. However, the efficiency obtained for the relevant deployed resources is equally critical as it determines to what extent a resource allocation decision was comparable to optimality, and hence may be perceived cost-efficient in that regard. Achieving high application performance comes in conflict with maintaining high resource efficiency. The compromise between the two to seek a feasible trade-off at which a cost-efficient allocation can be claimed is without doubt a complex multi-dimensional problem as it directly has to deal with an adamant prime issue that is known as performance unpredictability. It is particularly raised in resource provisioning from large-scale clouds (whose infrastructure is virtualised and shared) due to factors mainly include: heterogeneity of cloud resources, workload uncertainty, and performance interference. This thesis attempts to optimise allocation decisions made for cloud-hosted applications against the challenge of performance unpredictability by improving their resource efficiency while ensuring that each application can satisfy its performance objectives in a cost-efficient deployment. To this end, we follow a holistic approach to present our contributions through which we address each factor of the aforementioned challenge when designing allocation mechanisms to achieve optimal allocations that are efficient performance-, utilisation-, and cost-wise. Firstly, we devise a resource allocation framework that exploits and thus benefits from heterogeneity of cloud resources such that application performance is predictable despite of being running on heterogeneous resources with varying computing capacities. Then, we address the long-standing problem of resource over-provisioning in cloud datacenters in response to workload uncertainty. Finally, we design and built a QoS-aware resource controlling system that enables coordinated execution amongst multiple applications on shared resources, with which a potential performance interference can be mitigated