<div>The demand for global video has been burgeoning across industries. With the expansion and improvement of video-streaming services, cloud-based video is evolving into a necessary feature of any successful business for reaching internal and external audiences. Over-the-top (OTT) video streaming, e.g., Netfix and YouTube, has been dominating the global IP traffic in recent years. More than 50% of OTT video traffic are now delivered through content distribution networks (CDNs). Even though multiple solutions have been proposed for improving congestion in the CDN system, managing the ever-increasing traffic requires a fundamental understanding of the system and the different design fexibilities (control knobs) to make the best use of the hardware limitations. In Addition, there is no analytical understanding for the key quality of experience (QoE) attributes (stall duration, average quality, etc.) for video streaming when transmitted using CDN-based multi-tier infrastructure, which is the focus of this thesis. The key contribution of this thesis is to provide a white-box analytical understanding of the key QoE attributes of the enduser in cloud storage systems, which can be used to systematically address the choppy user experience and have optimized system designs. The rst key design involves the scheduling strategy, that chooses the subset of CDN servers to obtain the content. The second key design involves the quality of each video chunk. The third key design involves deciding which contents to cache at the edge routers and which content needs to be stored at the CDN. Towards solving these challenges, this dissertation is divided into three parts. Part 1 considers video streaming over distributed systems where the video segments are encoded using an erasure code for better reliability. Part 2 looks at the problem of optimizing the tradeoff between quality and stall of the streamed videos. In Part 3, we consider caching partial contents of the videos at the CDN as well as at the edge-routers to further optimize video streaming services. We present a model for describing a today's representative multi-tier system architecture</div><div>for video streaming applications, typically composed of a centralized origin server, several CDN sites and edge-caches. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites and edge-routers, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With this model, we optimize different quality of experience (QoE) measures and present novel, yet efficient, algorithms to solve the formulated optimization problems. Our extensive simulation results demonstrate that the proposed algorithms signicantly outperform the state-of-the-art strategies. We take one step further and implement a small-scale video streaming system in a real cloud environment, managed by Openstack, and validate our results</div>

Achieving and maintaining sustainability in irrigated agriculture production in the era of rapidly increasing stress on our natural resources require, among other essential actions, optimum control and management of the applied water. Thus, a significant upgrade of the currently available soil water monitoring technologies is needed. The primary goal of this work was to reduce the uncertainties of spatially variable soil water in the field. Two approaches are suggested: 1) The Bayesian decision model that implicitly accounts for spatial variability at minimal cost based on limited field data, and 2) The Actively Heated Fiber Optic (AHFO) method that explicitly accounts for spatial variability with high sampling density at relatively low cost per measurement point. The Bayesian decision model uses an algorithm to integrate information embodied in independent estimates of soil water depletion to derive a posterior estimation of soil water status that has the potential to reduce the risk of costly errors in irrigation scheduling decisions. The sources of information are obtained from an ET based water balance model, soil water measurements, and expert opinion. The algorithm was tested in a numerical example based on a field experiment where soil water depletion measurements were made at 43 sites in an agricultural field under center pivot irrigation. The results showed that the estimates of the average soil water depletion in the field obtained from the posterior distributions of soil water depletion proved to outperform simple averaging of n soil water depletion measurements, up to n = 35 measurements. For n< 3, the model also provided a 39% average reduction in risk of error derived from non-representative measurements. The AHFO method observes the heating and cooling of a buried fiber optic (FO) cable through the course of a pulse application of energy as monitored by a distributed temperature sensing (DTS) system to reveal soil water content simultaneously at sub-meter scale along the FO cable that can potentially exceeds kilometers in length. A new and simple interpretation of heat data that takes advantage of the characteristics of FO temperature measurements is presented. The results demonstrate the feasibility of AHFO method application to obtain <0.05 m³m⁻³ error distributed measurements of soil water content under laboratory controlled conditions. The AHFO method was then tested under field conditions using 750 m of FO cables buried at 30, 60, and 90 cm depths in agricultural field. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse was developed in the lab. It was successively applied to the 30 and 60 cm depths cables, while the 90 cm depth cable illustrated the challenges of soil heterogeneity for this technique. The method was used to map with high spatial (1m) and temporal (1hr) resolution the spatial variability of soil water content and fluxes induced by the non-uniformity of water application at the surface. / Graduation date: 2012

With the growing business impact of distributed big data analytics jobs, it has become crucial to optimize their execution and resource consumption. In most cases, such jobs consist of multiple sub-entities called tasks and are executed online in a large shared distributed computing system. The ability to accurately estimate runtime properties and coordinate execution of sub-entities of a job allows a scheduler to efficiently schedule jobs for optimal scheduling. This thesis presents the first study that highlights spatial dimension, an inherent property of distributed jobs, and underscores its importance in efficient cluster job scheduling. We develop two new classes of spatial dimension based algorithms to<br>address the two primary challenges of cluster scheduling. First, we propose, validate, and design two complete systems that employ learning algorithms exploiting spatial dimension. We demonstrate high similarity in runtime properties between sub-entities of the same job by detailed trace analysis on four different industrial cluster traces. We identify design challenges and propose principles for a sampling based learning system for two examples, first for a coflow scheduler, and second for a cluster job scheduler.<br>We also propose, design, and demonstrate the effectiveness of new multi-task scheduling algorithms based on effective synchronization across the spatial dimension. We underline and validate by experimental analysis the importance of synchronization between sub-entities (flows, tasks) of a distributed entity (coflow, data analytics jobs) for its efficient execution. We also highlight that by not considering sibling sub-entities when scheduling something it may also lead to sub-optimal overall cluster performance. We propose, design, and implement a full coflow scheduler based on these assertions.