Cloud computing, which is among the trendiest computing paradigms in recent years, is believed to be most suitable for supporting network-centric applications by providing elastic amounts of bandwidth for accessing a wide range of resources on the y. In particular, geo-distributed cloud systems are widely in construction nowadays. They span multiple data centers at different geographical locations, thus offering many advantages to large-scale multimedia applications because of the abundance of on-demand storage/bandwidth capacities and their geographical proximity to different groups of users. In this thesis, we investigate the common fundamental challenges in how to efficiently leverage the power of cloud resources to facilitate multimedia content delivery in various modern real world applications, from different perspectives. First, from the perspective of application providers, we propose tractable procedures for both model analysis and system designs of supporting representative large scale multimedia applications in a cloud system, i.e., VoD streaming applications and social media applications, respectively. We further verify the effectiveness of these algorithms and the feasibility of their deployment under dynamic realistic settings in real-life cloud systems. Second, from the perspective of end users, we target our focus at mobile users. The rapidly increasing power of personal mobile devices, dwarfing even high-end devices, is providing much richer contents and social interactions to users on the move, and many more challenging applications are on the horizon. We explore the tough challenges of how to effectively exploit cloud resources to facilitate mobile services by introducing two cloud-assisted mobile systems (i.e., CloudMoV and vSky-Conf), and explain in details their design philosophies and implementation. Finally, from the perspective of the cloud providers, we realize existing data center networks lack the flexibility to support many core services, given our hands-on experiences from working with public cloud systems. One of the specific problem is, “bulk data transfers across geo-distributed datacenters". After formulating a novel and well-formed optimization model for treating the data migration problem, we design and implement a Delay Tolerant Migration (DTM) system based on the Beacon platform and standard OpenFlow APIs. The system realizes a reliable Datacenter to Datacenter (D2D) network by applying the software defined networking (SDN) paradigm. Real-world experiments under realistic network traffic demonstrate the efficiency of the design. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

On mobile platforms such as iOS and Android, Web browsers such as Google Chrome, and even smart televisions such as Google TV or Roku, hundreds of thousands of software apps provide services to users. Their functionality often requires access to potentially sensitive user data (e.g., contact lists, passwords, photos), sensor inputs (e.g., camera, microphone, GPS), and/or information about user behavior. Most apps use this data responsibly, but there has also been evidence of privacy violations. As a result, individuals must carefully consider what apps to install and corporations often restrict what apps employees can install on their devices, to prevent an untrusted app—or a cloud provider that an app communicates with—from leaking personal data and proprietary information. There is an inherent trade-off between users’ privacy and apps’ functionality. An app with no access to user data cannot leak anything sensitive, but many apps cannot function without such data. A password management app needs access to passwords, an audio transcription app needs access to the recordings of users’ speech, and a navigation app needs users’ location. In this dissertation, we present two app platform designs, πBox and CleanRoom, that strike a useful balance between users’ privacy and apps’ functional needs, thus shifting much of the responsibility for protecting privacy from the app and its users to the platform itself. πBox is a new app platform that prevents apps from misusing information about their users. To achieve this, πBox deploys (1) a sandbox that spans the user’s device and the cloud, (2) specialized storage and communication channels that enable common app functionality, and (3) an adaptation of recent theoretical algorithms for differential privacy under continual observation. We describe a prototype implementation of πBox and show how it enables a wide range of useful apps with minimal performance overhead and without sacrificing user privacy. In particular, πBox develops the aforementioned three techniques under the assumption of limited sharing of personal data. CleanRoom extends πBox and is designed to protect confidentiality in a "Bring Your Own Apps" (BYOA) world in which employees use their own untrusted third-party apps to create, edit, and share corporate data. CleanRoom’s core guarantee is privacy-preserving collaboration: CleanRoom enables employees to work together on shared documents while ensuring that the documents’ owners—not the app accessing the document—control who can access and collaborate on the document. To achieve this guarantee, CleanRoom partitions an app into three parts, each of which implements a different function of the app (data navigation, data manipulation, and app settings), and controls communication between these parts. We show that CleanRoom accommodates a broad range of apps, preserves the confidentiality of the data that these apps access, and incurs insignificant overhead (e.g., 0.11 ms of overhead per client-server request). Both πBox and CleanRoom use differential privacy for apps to provide feedback to their publisher. This dissertation explores how to adapt differential privacy to be useful for app platforms. In particular, we investigate an adaptation of re- cent theoretical algorithms for differential privacy under continual observation and several techniques to leverage it for useful features in an app environment including advertising, app performance feedback, and error reporting. / text

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Measurements made by the AVHRR (Advanced Very High Resolution Radiometer) on board of five NOAA polar orbiting satellites were used to retrieve cloud optical depth (τ) and cloud droplet effective radius (r[sub eff]) for marine boundary layer clouds over the Pacific Ocean west of California and over the Southern Ocean near Tasmania. Retrievals were obtained for 21 days of data acquired between 1987 and 1995 from which over 300 subscenes ~ 256 km x 256 km in size were extracted. On this spatial scale cloud fields were found to have mean τ between 8 and 32 and mean r[sub eff] between 6 and 17 μm. The frequency distribution of τ is well approximated by a two parameter gamma distribution. The gamma distribution also provides a good fit to the observed r[sub eff] distribution if the distribution is symmetric or positively skewed but fails for negatively skewed or bi-modal distributions of r[sub eff] which were also observed. The retrievals show a relationship between τ and r[sub eff] which is consistent with a simple "reference" cloud model with reff ~ r[sup 1 / 5]. The proportionality constant depends on cloud droplet number concentration N and cloud subadiabaticity β through the parameter N[sub sat] = N/ [sq rt. Β]. Departures from the reference behaviour occur in scenes with spatially coherent N[sub sat] regimes, separated by a sharp boundary. AVHRR imagery is able to separate two N[sub sat] regimes if they differ by at least 30% in most cases. Satellite retrievals of τ and r[sub eff] were compared with in situ aircraft measurement near Tasmania. The retrievals overestimated r[sub eff] by 0.7 to 3.6 μm on different flights, in agreement with results from earlier comparison studies. The r[sub eff] overestimation was found to be an offset independent of τ. The reference cloud model and the N[sub sat] retrieval were tested on aircraft data and yield results consistent with direct in situ measurements of N and 8. Spectral and multifractal analyses of the spatial structure of cloud visible radiance, τ and r[sub eff] fields in 34 satellite scenes revealed scale breaks at 3 to 2 km in all analysed scenes in agreement with some earlier observations (Davis et al. (1996a)) but in contrast with other work (Lovejoy et al. (1993)). The nonstationarity H(1) and intermittency C(1) parameters were computed for the 34 scenes, stratified using the reference cloud model and according to mean τ and r[sub eff]. Similar values of H(1) and C(1) were found in all these categories. These measurements of the frequency distribution and spatial variability of τ, r[sub eff], liquid water path (Iwp), and N[sub sat] can be used to place constraints on mesoscale models of layer clouds.

The studies in this dissertation aim at advancing our scientific understandings about physical processes involved in the aerosol-cloud-precipitation interaction and quantitatively assessing the impacts of aerosols on the cloud systems with diverse scales over the globe on the basis of the observational data analysis and various modeling studies. Long-term impacts of aerosols on precipitation and lightning over the Pearl River Delta megacity area in China are identified through the analysis of seven-year measurements of precipitation, lightning flashes, and visibility from 2000 to 2006. The cloud resolving - Weather Research and Forecasting (CR-WRF) model with a two- moment bulk microphysical scheme is employed to simulate a mesoscale convective system in the Guangzhou megacity area and to elucidate the effects of aerosols on cloud processes, precipitation, and lightning activity. The responses of hydrometeors and latent heat release to different aerosol loadings reveal the physical mechanism for the precipitation and lightning enhancement in the Guangzhou megacity area, showing more efficient mixed phase processes and intensified convection under the polluted aerosol condition. Sensitivity modeling experiments are performed for maritime warm stratocumulus clouds over the southeast Pacific Ocean to evaluate the microphysical parameterizations for simulations of the aerosol effects in regional and global climate models. The Morrison double-moment bulk microphysical scheme presently implemented in the WRF model is modified by replacing the fixed aerosols in the original bulk scheme with a prognostic double-moment aerosol representation to predict both aerosol number concentration and mass mixing ratio. The impacts of the parameterizations of diffusional growth and autoconversion of cloud droplets and the selection of the embryonic raindrop radius on the performance of the bulk microphysical scheme are also evaluated. The impacts of Asian pollution outflows on the Pacific storm track are assessed utilizing reanalysis data, a hierarchical modeling approach and the multi-scale aerosol- climate modeling frame. Statistical analysis of two sets of reanalysis data suggests a strengthened trend of the storm track intensity over the North Pacific since 1979. The two-month seasonal simulations using a CR-WRF model with a two-moment bulk microphysics are performed to examine the aerosol effects on the Pacific storm track intensity. Subsequently, the anomalies of the diabatic heating rate by the Asian pollution outflow derived from the CR-WRF simulations have been prescribed in the NACR Community Atmosphere Model (CAM5) to provide the aerosol forcing terms. The forced GCM well reproduces an enhancement in the intensity of storm track, compared to the unforced model simulations. Similarly, under the multi-scale aerosol-climate modeling frame, the comparisons of the simulated present day versus pre-industrial climate corresponding to two different aerosol scenarios indicate the increased precipitation and poleward heat transport for the present-day climate reveal invigorated mid-latitude cyclones. The current work illustrates the complexity of the aerosol effects on the cloud systems at the diverse scales with different meteorological conditions. This study also stresses the importance of accurate representation of aerosol forcings in the different types of atmospheric numerical models for future climate projections.

This dissertation focuses on the global investigation of optically thin cirrus cloud optical thickness (tau) and microphysical properties, such as, effective particle size (D_(eff)) and ice crystal habits (shapes), based on the global satellite observations and fast radiative transfer models (RTMs). In the first part, we develop two computationally efficient RTMs simulating satellite observations under cloudy-sky conditions in the visible/shortwave infrared (VIS/SWIR) and thermal inferred (IR) spectral regions, respectively. To mitigate the computational burden associated with absorption, thermal emission and multiple scattering, we generate pre-computed lookup tables (LUTs) using two rigorous models, i.e., the line-by-line radiative transfer model (LBLRTM) and the discrete ordinates radiative transfer model (DISORT). The second part introduces two methods (i.e., VIS/SWIR- and IR-based methods) to retrieve tau and D_(eff) from satellite observations in corresponding spectral regions of the two RTMs. We discuss the advantages and weakness of the two methods by estimating the impacts from different error sources on the retrievals through sensitivity studies. Finally, we develop a new method to infer the scattering phase functions of optically thin cirrus clouds in a water vapor absorption channel (1.38-µm). We estimate the ice crystal habits and surface structures by comparing the inferred scattering phase functions and numerically simulated phase functions calculated using idealized habits.

The Oort Cloud (OC) is a roughly spherical cloud of comets surrounding the solar system, stretching from well beyond the orbit of Neptune, half way to the nearest star. This body of comets is interesting because it contains a record of the gravitational perturbations suffered by the solar system over its lifetime. Here, we investigate the effects of a particular class of perturbing objects - enormous complexes of molecular gas called giant molecular clouds (GMCs). Recent evidence has shown that the classical picture of Oort Cloud formation is inadequate to describe certain properties of the OC. To re-investigate the dynamical evolution of the Oort Cloud, we simulate the Sun's emergence from its natal molecular cloud, and its subsequent encounters with GMCs. While the role of giant molecular clouds in OC formation has been explored before, they have been implemented in a general way, not explicitly taking into account the 3D structure of the cloud. In this research, we draw on an extensive body of evidence which suggests that GMCs are not uniform, diffuse objects, but are instead organized into high density clumps, connected by a very diffuse inter-clump medium. Recent research has shown that GMCs are likely to be fractal in nature, and so we have modeled them as fractal distributions with dimension 1.6. We then perform N-body simulations of the passage of the Sun and its Oort Cloud through such a molecular cloud. We find that the fractal structure of the GMC is, in fact, an important parameter in the magnitude of the cometary energy change. The significant energy changes occur as a result of interactions with the GMC substructure, not simply as a result of its overall density distribution. We find that interactions with GMCs can be quite destructive to the OC, but can also serve to move comets from tightly bound orbits to less tightly bound orbits, thus partially replacing those lost to stripping. Simulations of the Sun's relatively slow exit from its birth GMC paint a picture of a potentially very destructive era, in which a large portion of the OC's evolution may have occured. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2009-09-21 13:05:17.527

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