The evolution of cloud technologies which allows the provisioning of IT resources over the Internet promises many benefits for the individuals and enterprises alike. However, this new resource provisioning model comes with the security challenges which did not exist in the traditional resource procurement mechanisms. We focus on the possible security concerns of a cloud user (e.g. an organization, government department, etc.) to lease cloud services such as resources in the form of Virtual Machines (VM) from a public Infrastructure-as-a-Service (IaaS) provider. There are many security critical areas in the cloud systems, such as data confidentiality, resource integrity, service compliance, security audits etc. In this thesis, we focus on the security aspects which result in the trust deficit among the cloud stakeholders and hence hinder a security sensitive user to benefit from the opportunities offered by the cloud computing. Based upon our findings from the security requirements analysis,we propose solutions that enable user trust in the public IaaS clouds. Our solutions mainly deal with the secure life cycle management of the user VM which include mechanisms for VM launch and migration. The VM launch and migration solutions ensure that the user VM is always protected in the cloud by only allowing it to run on the user trusted platforms. This is done by using trusted computing techniques that allow the users to remotely attest and hence rate the cloud platforms trusted or untrusted. We also provide a prototype implementation to prove the implementation feasibility of the proposed trust enabling principles used in the VM launch and migration solutions.

No description available.

This dissertation focuses on developing algorithms and systems to improve the efficiency of operating mega datacenters with hundreds of thousands of servers. In particular, it seeks to address two challenges: First, how to distribute the workload among the set of datacenters geographically deployed across the wide area? Second, how to manage the server resources of datacenters using virtualization technology? In the first part, we consider the workload management problem in geo-distributed datacenters. We first present a novel distributed workload management algorithm that jointly considers request mapping, which determines how to direct user requests to an appropriate datacenter for processing, and response routing, which decides how to select a path among the set of ISP links of a datacenter to route the response packets back to a user. In the next chapter, we study some key aspects of cost and workload in geo-distributed datacenters that have not been fully understood before. Through extensive empirical studies of climate data and cooling systems, we make a case for temperature aware workload management, where the geographical diversity of temperature and its impact on cooling energy efficiency can be used to reduce the overall cooling energy. Moreover, we advocate for holistic workload management for both interactive and batch jobs, where the delay-tolerant elastic nature of batch jobs can be exploited to further reduce the energy cost. A consistent 15% to 20% cooling energy reduction, and a 5% to 20% overall cost reduction are observed from extensive trace-driven simulations. In the second part of the thesis, we consider the resource management problem in virtualized datacenters. We design Anchor, a scalable and flexible architecture that efficiently supports a variety of resource management policies. We implement a prototype of Anchor on a small-scale in-house datacenter with 20 servers. Experimental results and trace-driven simulations show that Anchor is effective in realizing various resource management policies, and its simple algorithms are practical to solve virtual machine allocation with thousands of VMs and servers in just ten seconds.

This dissertation focuses on developing algorithms and systems to improve the efficiency of operating mega datacenters with hundreds of thousands of servers. In particular, it seeks to address two challenges: First, how to distribute the workload among the set of datacenters geographically deployed across the wide area? Second, how to manage the server resources of datacenters using virtualization technology? In the first part, we consider the workload management problem in geo-distributed datacenters. We first present a novel distributed workload management algorithm that jointly considers request mapping, which determines how to direct user requests to an appropriate datacenter for processing, and response routing, which decides how to select a path among the set of ISP links of a datacenter to route the response packets back to a user. In the next chapter, we study some key aspects of cost and workload in geo-distributed datacenters that have not been fully understood before. Through extensive empirical studies of climate data and cooling systems, we make a case for temperature aware workload management, where the geographical diversity of temperature and its impact on cooling energy efficiency can be used to reduce the overall cooling energy. Moreover, we advocate for holistic workload management for both interactive and batch jobs, where the delay-tolerant elastic nature of batch jobs can be exploited to further reduce the energy cost. A consistent 15% to 20% cooling energy reduction, and a 5% to 20% overall cost reduction are observed from extensive trace-driven simulations. In the second part of the thesis, we consider the resource management problem in virtualized datacenters. We design Anchor, a scalable and flexible architecture that efficiently supports a variety of resource management policies. We implement a prototype of Anchor on a small-scale in-house datacenter with 20 servers. Experimental results and trace-driven simulations show that Anchor is effective in realizing various resource management policies, and its simple algorithms are practical to solve virtual machine allocation with thousands of VMs and servers in just ten seconds.

University, Diss., 2004--Bonn.

Context: Cloud computing is providing services and resources to customers based on pay-per-use. As the services increasing, Cloud computing using a vast number of data centers like thousands of data centers which consumes high energy. The power consumption for cooling the data centers is very high. So, recent research going on to implement the best model to reduce the energy consumption by the data centers. This process of minimizing the energy consumption can be done using dynamic Virtual Machine Consolidation (VM Consolidation) in which there will be a migration of VMs from one host to another host so that energy can be saved. 70% of energy consumption will be reduced/ saved when the host idle mode is switched to sleep mode, and this is done by migration of VM from one host to another host. There are many energy adaptive heuristics algorithms for the VM Consolidation. Host overload detection, host underload detection and VM selection using VM placement are the heuristics algorithms of VM Consolidation which results in less consumption of the energy in the data centers while meeting Quality of Service (QoS). In this thesis, we proposed new heuristic algorithms to reduce energy consumption. Objectives: The objective of this research is to provide an energy efficient model to reduce energy consumption. And proposing a new heuristics algorithms of VM Consolidationtechnique in such a way that it consumes less energy. Presenting the advantages and disadvantages of the proposed heuristics algorithms is also considered as objectives of our experiment. Methods: Literature review was performed to gain knowledge about the working and performances of existing algorithms using VM Consolidation technique. Later, we have proposed a new host overload detection, host underload detection, VM selection, and VM placement heuristic algorithms. In our work, we got 32 combinations from the host overload detection and VM selection, and two VM placement heuristic algorithms. We proposed dynamic host underload detection algorithm which is used for all the 32 combinations. The other research method chosen is experimentation, to analyze the performances of both proposed and existing algorithms using workload traces of PlanetLab. This simulation is done usingCloudSim. Results: To compare and get the results, the following parameters had been considered: Energy consumption, No. of migrations, Performance Degradation due to VM Migrations (PDM),Service Level Agreement violation Time per Active Host (SLATAH), SLA Violation (SLAV),i.e. from a combination of the PDM, SLATAH, Energy consumption and SLA Violation (ESV).We have conducted T-test and Cohen’s d effect size to measure the significant difference and effect size between algorithms respectively. For analyzing the performance, the results obtained from proposed algorithms and existing algorithm were compared. From the 32 combinations of the host overload detection and VM Selection heuristic algorithms, MADmedian_MaxR (Mean Absolute Deviation around median (MADmedian) and Maximum Requested RAM (MaxR))using Modified Worst Fit Decreasing (MWFD) VM Placement algorithm, andMADmean_MaxR (Mean Absolute Deviation around mean (MADmean), and MaximumRequested RAM (MaxR)) using Modified Second Worst Fit Decreasing (MSWFD) VM placement algorithm respectively gives the best results which consume less energy and with minimum SLA Violation. Conclusion: By analyzing the comparisons, it is concluded that proposed algorithms perform better than the existing algorithm. As our aim is to propose the better energy- efficient model using the VM Consolidation techniques to minimize the power consumption while meeting the SLAs. Hence, we proposed the energy- efficient algorithms for VM Consolidation technique and compared with the existing algorithm and proved that our proposed algorithm performs better than the other algorithm. We proposed 32 combinations of heuristics algorithms (host overload detection and VM selection) with two adaptive heuristic VM placement algorithms. We have proposed a dynamic host underload detection algorithm, and it is used for all 32 combinations. When the proposed algorithms are compared with the existing algorithm, we got 22 combinations of host overload detection and VM Selection heuristic algorithms with MWFD(Modified Worst Fit Decreasing) VM placement and 20 combinations of host overload detection and VM Selection heuristic algorithms with MSWFD (Modified Second Worst FitDecreasing) VM placement algorithm which shows the better performance than existing algorithm. Thus, our proposed heuristic algorithms give better results with minimum energy consumption with less SLA violation.

The Royal Fleet Auxiliary (RFA) is a flotilla of ships, owned by the United Kingdom (UK) Ministry of Defence (MoD), which serves to resupply naval vessels during worldwide operations. Design Alterations and Additions (As&As) are implemented throughout their service lives in order to ‘Upgrade’ and ‘Update’ their capability. This research offers an original contribution to knowledge by applying formal decision making techniques to A&A reasoning in a way that, to the best knowledge of the researcher, has not previously been implemented as an integral part of the in-service design control process for RFA ships. In delivering this contribution, Multi Attribute Decision Making (MADM) techniques are investigated and applied. Three MADM techniques are applied: SAW (Simple Additive Weighting), AHP (Analytic Hierarchy Processes) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Application of these techniques defines the scope boundary and so rules out exhaustive investigation into the wider decision making approaches that could form the focus of future research. Literature reviews indicate that formal decision techniques have been extensively studied and applied – seemingly to the point of saturation. For this reason, the research does not claim to have developed new techniques. Rather, the contribution to knowledge lies in the systematic application of the techniques. In this respect, a niche topic is identified involving the implementation of As&As during Fleet Time (FT). Investigation results in the systematic identification and categorisation of the Risk Factors (RFs) constraining FT implementation. Two different techniques (SAW and AHP) are applied to FT As&As. The outcomes demonstrate a consistent trend and so offer mutual assurance. In addition, comparison of the techniques indicates that, whilst SAW offers a convenient and intuitive approach, the AHP imposes a higher cognitive burden. This is regarded as significant by the researcher since As&As are subject to schedule and cost constraints, whereby pragmatic and proportional approaches are more likely to find programme acceptance. Based upon an actual decision involving the selection of materials for a ship sea water system, TOPSIS is used to evaluate the options against key criteria. A sensitivity analysis indicates that selection will be influenced in the direction of the criteria weighting. Since ‘procurement cost’ is an important criterion for As&As, the thesis demonstrates a methodology for the delivery of robust cost estimates. This involves the treatment of cost uncertainty using risk analysis software based upon the Monte Carlo technique. The researcher consolidates studies into systematic decision methodologies for As&As. Credibility is claimed since methodologies are based upon established techniques and tested against A&A examples. Credibility is also claimed from the theme, running throughout the thesis, that the studies build upon the professional experience of the researcher and involve engagement with Suitably Qualified and Experienced Personnel (SQEP).

This thesis develops a mixed mode function – boundary element method (BEM) to analyze the dynamics of an integrated airplane – floating structure – water interaction system subject to airplane landing impacts. The airplane and the floating structure are treated as two solid substructures of which the motions are represented by their respective modal functions. The landing gear system of the airplane is modelled with a few linear spring – damper units connecting the airplane and the floating structure. The water is assumed to be inviscid and incompressible and the fluid motion is irrotational. Under a linear potential theory, the motion of the fluid is governed by the Laplace equation and the related boundary conditions. A linearised composite free surface boundary condition and an undisturbed far field (infinity) radiation condition are considered. The Green function, or kernel, of BEM formulation is a fundamental solution of the Laplace equation assuming an infinite fluid domain. The motion of the floating structure and the surrounding fluid are coupled through the wetted surface interface conditions. The coupled equations of the airplane, the floating structure and the surrounding fluid are solved using a step by step time integration procedure based on the Newmark assumptions. A FORTRAN program MMFBEP is written to implement the proposed numerical method. A few examples are completed to validate the mathematical model and the developed computer code. In comparing the available numerical and experimental results reported in the literature, sound agreements are reached. It is hoped that the developed method and computer code may be further improved and modified to provide an engineering tool for the dynamic design of Very Large Floating Structures (VLFS).

Steady ship motion in calm water is a classical problem in ship hydrodynamics. Potential flow modelling is a common method to predict the wave resistance of ships. In its conventional form the flow is assumed to be free from damping due to the inviscid assumption of potential flow. It has been argued by the founding fathers of ship resistance predictions that damping plays an important role in determining the wave resistance. Despite this viscosity is often omitted from present wave resistance prediction methods. It is known that damping plays an important role in the formation of the wave pattern and it is therefore of interest to determine the effect on the resistance prediction by including a damping factor in a previously undampened model. In this study, the problem is modelled using Kelvin sources with a translating speed. The fluid flow is modelled using a linearised free surface condition but an exact body condition on the hull. Rayleigh damping is introduced in the model to emulate viscous damping. To calculate the source influences, a new dissipative 3D Green function is derived. The image source part of Green function is separated into the near field and far field disturbance to achieve fast convergence of the integrals. The method is evaluated using thin ship theory to determine the wave pattern behind and the wave profile along a Wigley hull. A panel method is used to determine the wave and residual resistance for submerged ellipsoids and spheres. The results are validated and compared to existing numerical and experimental data from other sources. The results show that it may be possible to capture the residual resistance by including damping in a potential flow model but that more evaluations are needed.

Polymer composite materials are becoming increasingly popular in many engineering structures in the civil, aerospace, marine and automotive industries. The increased strength and stiffness to weight ratios which are possible with certain types of composites make them particularly attractive to many high performance applications such as military aircraft, offshore lifeboats and formula one racing cars. One aspect of composite materials which is preventing more widespread use is the perceived poor performance in fire. The perception is due to the fact that organic compounds used in polymer composites are combustible. The loss of the Norwegian Navy’s composite mine hunter vessel Orkla in 2002 to a fire did much to prevent further widespread use of such materials. The work presented here describes the research that has been conducted into assessing and predicting the performance of single skin and sandwich composite materials subjected to fire and mechanical load. The materials that were investigated were representative of the materials used in the construction of Royal National Lifeboat Institution (RNLI) lifeboats. A new method has been developed to assess the response both thermally and mechanically of single skin and sandwich panels subjected to combined fire and mechanical load. This has been done by the construction of a small scale fire and load testing apparatus. An empirical relationship was developed to predict the stiffness of single skin and sandwich panels during a fire and load test. Numerical models have also been generated to predict the thermo-mechanical response of single skin and sandwich panels to fire and load. Testing of single skin and sandwich panels on the newly developed apparatus has been conducted to verify the numerical models. The numerical models and the empirical relationship were used to predict the response of a full scale composite sandwich panel, representative of a lifeboat deck, to a standard cellulosic fire and mechanical load.