Cloud Infrastructure Services Selection and Evaluation

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant increase of new cloud services almost every month by both large corporations (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g. Rackspace and FlexiScale), the selection scenarios become more and more complex. This is aggregated by confusing and ambiguous terminology and non-standardized interfaces. This is challenging for decision-makers such as application developers and chief information officers as they are overwhelmed by various choices available. In this thesis, I will address the above challenges by developing several techniques. Firstly, I define the Cloud Computing Ontology (CoCoOn). CoCoOn defines concepts, features, attributes and relations of Cloud infrastructure services. Secondly, I propose a service selection method that adopts an analytic hierarchy process (AHP)-based multi-criteria decision-making technique. It allows users to define multiple design-time constraints like renting costs, data centre locations, service features and real-time constraints, such as end-to-end message latency and throughput. These constraints are then matched against our model to compute the possible best-fit combinations of cloud Infrastructure, offered as a Service (IaaS). Pairwise comparisons are used to help users determine a relative preference among a pool of nonnumerical attributes. Criteria that are taken into consideration during comparison can be grouped into two categories: the benefit and the cost. Based on this, I define a cost-benefit-ratio-based evaluation function to calculate the ranking for Cloud service options. Thirdly, I suggest a theory-based queuing approach for estimating IaaS usage. Queuing theory is a widely studied method in QoS modelling and optimization. From the infrastructure system administrator perspective, I explore several ways to apply the queuing theory model to estimate the best-fit resource allocation for achieving the desired SLA. Finally, the thesis shows how an integrated system, CloudRecommender, can be built from our proposed approaches

Blur perception: An evaluation of focus measures

Since the middle of the 20th century the technological development of conventional photographic cameras has taken advantage of the advances in electronics and signal processing. One speci c area that has bene ted from these developments is that of auto-focus, the ability for a cameras optical arrangement to be altered so as to ensure the subject of the scene is in focus. However, whilst the precise focus point can be known for a single point in a scene, the method for selecting a best focus for the entire scene is an unsolved problem. Many focus algorithms have been proposed and compared, though no overall comparison between all algorithms has been made, nor have the results been compared with human observers. This work describes a methodology that was developed to benchmark focus algorithms against human results. Experiments that capture quantitative metrics about human observers were developed and conducted with a large set of observers on a diverse range of equipment. From these experiments, it was found that humans were highly consensual in their experimental responses. The human results were then used as a benchmark, against which equivalent experiments were performed by each of the candidate focus algorithms. A second set of experiments, conducted in a controlled environment, captured the underlying human psychophysical blur discrimination thresholds in natural scenes. The resultant thresholds were then characterised and compared against equivalent discrimination thresholds obtained by using the candidate focus algorithms as automated observers. The results of this comparison and how this should guide the selection of an auto-focus algorithm are discussed, with comment being passed on how focus algorithms may need to change to cope with future imaging techniques