PhD Oral Exam - Fadi Alzhouri, Electrical and Computer Engineering
When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
The unexpected growth, flexibility and dynamism of information technology (IT) over the last decade has radically altered the civilization lifestyle and this boom continues as yet. Many nations have been competing to be forefront of this technological revolution, quite embracing the opportunities created by the advancements in this field in order to boost economy growth and to increase the accomplishments of every day’s life. Cloud computing is one of the most promising achievement of these advancements. However, it faces many sophisticated challenges and barriers like any immature industry. Managing and maximizing such a very complex system business revenue is of paramount importance. The wealth of the cloud protfolio comes from the proceeds of three main services: Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS).
The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values. Therefore, many enterprises show frantic effort to capture the largest portion through introducing many different pricing models to satisfy not merely customers' demands but essentially providers' requirements. Indeed, one of the most challenging requirements is finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been presented as an elegant solution to overcome these situations aiming to gain more profits. However, previous studies on recent spot pricing schemes reveal an artificial pricing policy that does not comply with the dynamic nature of these phenomena.
In this thesis, we investigate dynamic pricing of stagnant resources so as to maximize cloud revenue. To achieve this task, we reveal the necessities and objectives that underlie behind the importance of adopting cloud providers to dynamic price model, analyze adopted dynamic pricing strategy for real cloud enterprises and create dynamic pricing model that could be a strategic pricing model for IaaS cloud providers to increase the marginal profit and also to overcome technical barriers simultaneously.
First, we formulate the maximum expected reward under discrete finite-horizon Markovian decisions and characterize model properties under optimum controlling conditions. The initial approach manages one class but multiple fares of virtual machines. For this purpose, the proposed approach leverages Markov decision processes, a number of properties under optimum controlling conditions that characterize a model's behaviour, and approximate stochastic dynamic programming using linear programming to create a practical model.
Second, our seminal work directs us to explore the most sensitive factors that drive price dynamism and to mitigate the high dimensionality of such a large-scale problem through conducting column generation. More specifically, we employ a decomposition approach.
Third, we observe that all previous work tackled one class of virtual machines merely. Therefore, we extend our study to cover multiple classes of virtual machines. Intuitively, dynamic price of multiple classes model is much more efficiently from one side but practically is more challenging from another side. Consequently, our approach of dynamic pricing can scale up or down the price efficiently and effectively according to stagnant resources and load threshold aims to maximize the IaaS cloud revenue.