To achieve “provisioning elasticity”, the cloud needs to manage its available resources on demand. A-priori, static, VM provisioning introduces no runtime overhead but fails to handle unanticipated changes in resource demands. Dynamic provisioning addresses this problem but introduces runtime overhead. To avoid sub-optimal provisioning my PhD thesis adopts a hybrid approach that combines static and dynamic provisioning. The idea is to adapt an initial static placement of VMs in response to evolving load characteristics. My work is focused on broadening the applicability of clouds by looking at how the infrastructure can be more effectively used to support historically atypical applications (e.g. those that are interactive in nature with tighter QoS constraints). To accomplish this I have developed a family of related algorithms that collectively improve resource sharing on physical machines to permit load variation to be better addressed and to lessen the probability of VM interference due to resource contention. The family includes three core dynamic provisioning algorithms. The first algorithm provides for the short-term, controlled sharing of resources between co-hosted VMs, the second identifies pairs (and by extrapolation larger groups) of VMs that are predicted to be "compatible" in terms of the resources they need. This allows the cloud provider to do co-location to make the first algorithm more effective. The final, third, algorithm deals with under-utilized physical machines by re-packing the VMs on those machines while also considering their compatibility. This final algorithm both addresses the possibility of the second algorithm creating underutilized machines as a result of pairing and migration and also handles underutilization arising from “holes” left by the termination of short-duration VMs (another form of atypical VM application). I have also created a surprisingly simple static provisioning algorithm that considers compatibility to minimize VM interference that can be used before my dynamic algorithms. My evaluation is primarily simulation-based though I have also implemented the core algorithms on a small test-bed system to ensure correctness. The results obtained from my simulation experiments suggest that hybrid static and dynamic provisioning approaches are both feasible and should be effective supporting a broad range of applications in cloud environments. / February 2017

Zhang, Yilei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 160-174). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

As the power industry moves towards more active distribution networks there is an increased requirement for greater analysis and observability of the current state of the network. There are a number of challenges for utilities in realising this including the quality and accuracy of their network models; the lack of integration between network models and the large quantities of sensor data being collected; the security and communication challenges posed when installing large numbers of sophisticated sensors across distribution networks; and the exponential increase in computing power required to fully analyse modern network configurations. This thesis will look at these challenges and how cloud computing can be used to provide novel solutions by providing secure platforms on which to deploy complex data collection and network analysis applications. One of the main research contributions is the use of remote data collection from Micro Phasor Measurement Units (μPMUs), which collect synchronised information about the state of the distribution network. Impedance equations are applied to network data recorded from μPMUs and the results are compared to network models. This identifies areas of the distribution network as requiring resurveying or upgrading, potentially impacting planning for installation of generation or load. Triggers can be used to reduce the bandwidth of data being sent by a μPMU; these were tested with real world data to highlight how a combination of local intelligence and cloud-based analysis can be used to reduce bandwidth requirements while supporting the use of detailed measurement data for cloud-based analysis in a fault detection system. Power flow analysis is an important tool for both operations and planning engineers, and as computing power has increased the time required to run individual power flow analysis cases has decreased rapidly. However there has also been a corresponding increase in the complexity of the data as utilities seek to model and analyse distributed energy resources attached on the medium and low voltage networks. This has made network models more complex, exponentially increasing the number of contingencies that need to be analysed in an emergency situation. Another main research contribution is a demonstration of the challenges faced when using a commercial cloud platform to inexpensively solve computationally intensive power flow problems and the time, costs and feasibility of performing N-1 and N-2 analysis on a 21,000-bus network. It includes a full analysis and comparison of execution times and costs for different commercial cloud system configurations as well as the extrapolated costs required to run a full N-2 analysis of over 420 million contingencies in under 10 minutes. This includes a demonstration of a cloud client and server application developed as part of this research that leverages a commercial power flow engine. Finally, this thesis will summarise how each of these research outputs can be combined to provide utilities with a commercial, open, standards-based cloud platform for continuous, automated contingency analysis using real-time sensor data based on current network conditions. This would better inform control engineers about areas of vulnerability and help them identify and counter these in real-time.

Thesis (PhD) - Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Complex Software Systems and Services, 2009. / A thesis submitted to CS3 - Centre for Complex Software Systems and Services, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology for the degree of Doctor of Philosophy, 2009. Typescript. "June 2009". Bibliography: p. 122-135.

Cloud computing is here to stay and is the natural progression in the evolution of our computing and collaboration needs. The easy availability of computing infrastructures is motivating a new breed of entrepreneurs to realize their ideas and deliver innovations to masses. These innovations, however, have some serious security weaknesses. If not taken into account, these weaknesses could prove fatal for an organization’s reputation and existence. This thesis explains the potential risks associated with various types of cloud computing technologies and recommends methods to mitigate them. / text

Today, it is a major issue for mobile applications to maintain a replica state of the server on mobile devices. This creates the need to keep data on both the server and the mobile. In such cases, when the data changes on the server, the new state of the data has to be updated on the mobile in order to maintain a consistent view of the data flow. However, mobile devices communicate over wireless mediums (.e.g., Bluetooth, Wi-Fi, 3.5G/4G, etc.) which can experience intermittent connectivity. The volatility of the network is also influenced by low-bandwidth. The direct effects of these issues are high latency and inconsistency issues between the data on the mobile clients and the remote servers. In this work, I present a detail review on the topic of data synchronization in mobile networks. Then, a generic architecture called MobiQ is proposed which can keep working in an offline mode to record local modifications and can synchronize with the remote servers when connectivity is restored. This is achieved through the proposal of an efficient synchronization protocol which combines different synchronization and replication strategies. Moreover, the MobiQ framework provides a secured environment to work with data. The implemented architecture is designed and tested in mobile questionnaire system and the result is encouraging.

As a more efficient means of supplying computing resources in the form of a utility, cloud computing platforms have been increasingly used to meet the insatiable demand from mobile applications. The research problems we study in this thesis are in the general research area of mobile cloud computing, as we seek to design and implement new algorithms and protocols that straddle the boundary between mobile applications and cloud computing systems, so that their performance can be jointly optimized to provide the best possible user experience, yet operating within the constraints of available resources and operational costs. From the perspective of mobile applications, we show that interactive applications have the need to stream multi-touch gestures among multiple users. Tailored to the nature of multi-touch gesture streams, we propose a new protocol that uses inter-session network coding to reduce the gesture recognizing delays. Towards supporting mobile applications using the cloud resources, we believe that multi-party video conferencing service can benefit from the inter-datacenter networks in the cloud. We apply intra-session network coding to design a new protocol to maximize the total throughput of all conferencing sessions in the cloud, subject to a latency constraint imposed by the nature of video conferencing. Our real-world experiments have shown that the inter-datacenter networks help to achieve substantially improved throughput, with very similar delays compared to traditional peer-to-peer solutions. From the perspective of cloud service providers, we study the challenges involved when resource utilization is to be maximized and when operational costs are to be minimized. To maximize resource utilization, we propose a virtual machine (VM) migration algorithm based on Nash bargaining solutions. To minimize operational costs, we present optimal routing and flow assignment algorithms for the inter-datacenter traffic, with and without store-and-forward capabilities in intermediate datacenters. With efficient and cost-effective utilization of resources in the cloud, and by designing new protocols that are applicable to both mobile applications and cloud computing systems, achieving an optimized level of user experience with interactive mobile applications will become a reality.

As a more efficient means of supplying computing resources in the form of a utility, cloud computing platforms have been increasingly used to meet the insatiable demand from mobile applications. The research problems we study in this thesis are in the general research area of mobile cloud computing, as we seek to design and implement new algorithms and protocols that straddle the boundary between mobile applications and cloud computing systems, so that their performance can be jointly optimized to provide the best possible user experience, yet operating within the constraints of available resources and operational costs. From the perspective of mobile applications, we show that interactive applications have the need to stream multi-touch gestures among multiple users. Tailored to the nature of multi-touch gesture streams, we propose a new protocol that uses inter-session network coding to reduce the gesture recognizing delays. Towards supporting mobile applications using the cloud resources, we believe that multi-party video conferencing service can benefit from the inter-datacenter networks in the cloud. We apply intra-session network coding to design a new protocol to maximize the total throughput of all conferencing sessions in the cloud, subject to a latency constraint imposed by the nature of video conferencing. Our real-world experiments have shown that the inter-datacenter networks help to achieve substantially improved throughput, with very similar delays compared to traditional peer-to-peer solutions. From the perspective of cloud service providers, we study the challenges involved when resource utilization is to be maximized and when operational costs are to be minimized. To maximize resource utilization, we propose a virtual machine (VM) migration algorithm based on Nash bargaining solutions. To minimize operational costs, we present optimal routing and flow assignment algorithms for the inter-datacenter traffic, with and without store-and-forward capabilities in intermediate datacenters. With efficient and cost-effective utilization of resources in the cloud, and by designing new protocols that are applicable to both mobile applications and cloud computing systems, achieving an optimized level of user experience with interactive mobile applications will become a reality.

The thesis investigates, in a qualitative way, the vectors that contribute to cloud computing risks in the areas of security, business, and compliance. The focus of this research is on the identification of risk vectors that affect cloud computing and the creation of a framework that can help IT managers in their cloud adoption process. Economic pressures on businesses are creating a demand for an alternative delivery of the model that can provide flexible payments, dramatic cuts in capital investment, and reductions in operational cost. Cloud computing is positioned to take advantage of these economic pressures with low cost IT services and a flexible payment model, but at what risk to the business? Security concerns about cloud computing are heightened and fueled by misconceptions related to security and compliance risks. Unfortunately, these security concerns are seldom, expressed quantifiably. To bring clarity to cloud computing security, compliance, and business risks, this research focuses on a qualitative analysis of risk vectors drawn from one-on-one interviews with top IT experts selected. The qualitative aspect of this research separates facts from unfounded suspicions, and creates a framework that can help align perceived risks of cloud computing with actual risks. The qualitative research was done through interviews with experts and through the survey to measure risk perceptions about cloud computing using a Likert scale. The decision-making model and the framework created by this research help to rationalize the risk vectors on cloud environments and recommend reducing strategies to bring the IT industry one step closer to a clearer understanding of the risks-tradeoffs implications of cloud computing environments.

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