Design And Analysis Of Scalable Video Streaming Systems

Despite the advancement in multimedia streaming technology, many multimedia applications are still face major challenges, including provision of Quality-of-Service (QoS), system scalability, limited resources, and cost. In this dissertation, we develop and analyze a new set of metrics based on two particular video streaming systems, namely: (1) Video-on-Demand (VOD) with video advertisements system and (2) Automated Video Surveillance System (AVS). We address the main issues in the design of commercial VOD systems: scalability and support of video advertisements. We develop a scalable delivery framework for streaming media content with video advertisements. The delivery framework combines the benefits of stream merging and periodic broadcasting. In addition, we propose new scheduling policies that are well-suited for the proposed delivery framework. We also propose a new prediction scheme of the ad viewing times, called Assign Closest Ad Completion Time (ACA). Moreover, we propose an enhanced business model, in which the revenue generated from advertisements is used to subsidize the price. Additionally, we investigate the support of targeted advertisements, whereby clients receive ads that are well-suited for their interests and needs. Furthermore, we provide the clients with the ability to select from multiple price options, each with an associate expected number of viewed ads. We provide detailed analysis of the proposed VOD system, considering realistic workload and a wide range of design parameters. In the second system, Automated Video Surveillance (AVS), we consider the system design for optimizing the subjects recognition probabilities. We focus on the management and the control of various Pan, Tilt, Zoom (PTZ) video cameras. In particular, we develop a camera management solution that provides the best tradeoff between the subject recognition probability and time complexity. We consider both subject grouping and clustering mechanisms. In subject grouping, we propose the Grid Based Grouping (GBG) and the Elevator Based P lanning (EBP) algorithms. In the clustering approach, we propose the (GBG) with Clustering (GBGC) and the EBP with Clustering (EBPC) algorithms. We characterize the impact of various factors on recognition probability. These factors include resolution, pose and zoom-distance noise. We provide detailed analysis of the camera management solution, considering realistic workload and system design parameters

Estimated Time of Restoration (ETR) Guidance for Electric Distribution Networks

Electric distribution utilities have an obligation to inform the public and government regulators about when they expect to complete service restoration after a major storm. In this study, we explore methods for calculating the estimated time of restoration (ETR) from weather impacts, defined as the time it will take for 99.5% of customers to be restored. Actual data from Storm Irene (2011), the October Nor’easter (2011) and Hurricane Sandy (2012) within the Eversource Energy-Connecticut service territory were used to calibrate and test the methods; data used included predicted outages, the peak number of customers affected, a ratio of how many outages a restoration crew can repair per day, and the count of crews working per day. Data known before a storm strikes (such as predicted outages and available crews) can be used to calculate ETR and support pre-storm allocation of crews and resources, while data available immediately after the storm passes (such as customers affected) can be used as motivation for securing or releasing crews to complete the restoration in a timely manner. Used together, the methods presented in this paper will help utilities provide a reasonable, data-driven ETR without relying solely on qualitative past experiences or instinct

Dependence-driven techniques in system design

Burstiness in workloads is often found in multi-tier architectures, storage systems, and communication networks. This feature is extremely important in system design because it can significantly degrade system performance and availability. This dissertation focuses on how to use knowledge of burstiness to develop new techniques and tools for performance prediction, scheduling, and resource allocation under bursty workload conditions.;For multi-tier enterprise systems, burstiness in the service times is catastrophic for performance. Via detailed experimentation, we identify the cause of performance degradation on the persistent bottleneck switch among various servers. This results in an unstable behavior that cannot be captured by existing capacity planning models. In this dissertation, beyond identifying the cause and effects of bottleneck switch in multi-tier systems, we also propose modifications to the classic TPC-W benchmark to emulate bursty arrivals in multi-tier systems.;This dissertation also demonstrates how burstiness can be used to improve system performance. Two dependence-driven scheduling policies, SWAP and ALoC, are developed. These general scheduling policies counteract burstiness in workloads and maintain high availability by delaying selected requests that contribute to burstiness. Extensive experiments show that both SWAP and ALoC achieve good estimates of service times based on the knowledge of burstiness in the service process. as a result, SWAP successfully approximates the shortest job first (SJF) scheduling without requiring a priori information of job service times. ALoC adaptively controls system load by infinitely delaying only a small fraction of the incoming requests.;The knowledge of burstiness can also be used to forecast the length of idle intervals in storage systems. In practice, background activities are scheduled during system idle times. The scheduling of background jobs is crucial in terms of the performance degradation of foreground jobs and the utilization of idle times. In this dissertation, new background scheduling schemes are designed to determine when and for how long idle times can be used for serving background jobs, without violating predefined performance targets of foreground jobs. Extensive trace-driven simulation results illustrate that the proposed schemes are effective and robust in a wide range of system conditions. Furthermore, if there is burstiness within idle times, then maintenance features like disk scrubbing and intra-disk data redundancy can be successfully scheduled as background activities during idle times

A Dashboard-based Predictive Process Monitoring Engine

Protsesside jälgimine moodustab keskse osa äriprotsesside juhtimisest. See sisaldab tegevusi, milles kogutakse ja analüüsitakse protsessi täideviimise andmeid, et mõõta protsesside tulemuslikkust, võttes arvesse soorituse eesmärke. Tavaliselt on protsesside jälgimist sooritatud käitluse ajal, võimaldades reaalajalist ülevaadet protsessi sooritusest ja tuvastades protsessi vaidlusküsimused nende tekkimise hetkel. Viimasel ajal logimisvõimetega töövoo juhtimise süsteemide laialdane omaksvõtt on loonud aktiivse andmetest ajendatud ennustava protsesside jälgimise, mis kasutab varasemat protsesside jooksutamise andmestikku, et ennustada käimasolevate äriprotsesside tulevikusuunda. Seega potentsiaalselt hälbiva protsessi kulgu saab ette ennustada ja lahendada. Tüüpiliste protsesside jälgimise probleemidega tegelemiseks on välja pakutud erinevaid lähenemisi, nagu kas parasjagu käiva protsessi instants vastab selle soorituse eesmärkidele või millal instantsiga lõpule jõutakse. Need lähenemised on siiski seni jäänud akadeemilisse valdkonda ning neid pole rakendatud tööstuse sätetesse. Selles lõputöös me disainisime ja teostasime ennustava protsessi jälgimise mootori prototüübi. Arendatud lahendus on konfigureeritav täispinu veebiraamistik, mis võimaldab mitme soorituse indikaatori ennustamist ja mida saab kerge vaevaga laiendada teiste indikaatorite jaoks uute ennustavate mudelitega. Lisaks võimaldab see mitmest äriprotsessist pärinevate sündmusvoogude käsitlemist. Nii ennustuste tulemused kui protsesside täitmise reaalaja statistika kokkuvõtted kuvatakse esipaneelil, mis võimaldab mitut erinevat alternatiivset visualiseerimise valikut. Lahendus on kahte tõsielu äriprotsessi kasutades edukalt valideeritud, arvestades defineeritud funktsionaalseid ja mittefunktsionaalseid nõudeid.Process monitoring forms an integral part of business process management. It involves activities in which process execution data are collected and analyzed to measure the process performance with respect to the performance objectives. Traditionally, process monitoring has been performed at runtime, providing a real-time overview of the process performance and identifying performance issues as they arise. Recently, the rapid adop- tion of workflow management systems with logging capabilities has spawned the active development of data-driven, predictive process monitoring that exploits the historical process execution data to predict the future course of ongoing instances of a business process. Thus, potentially deviant process behavior can be anticipated and proactively addressed.To this end, various approaches have been proposed to tackle typical predictive monitoring problems, such as whether an ongoing process instance will fulfill its per- formance objectives, or when will an instance be completed. However, so far these approaches have largely remained in the academic domain and have not been widely applied in industry settings, mostly due to the lack of software support. In this the- sis, we have designed and implemented a prototype of a predictive process monitor- ing engine. The developed solution, named Nirdizati, is a configurable full-stack web framework that enables the prediction of several performance indicators and is easily extensible with new predictive models for other indicators. In addition, it allows han- dling event streams that originate from multiple business processes. The results of the predictions, as well as the real-time summary statistics about the process execution, are presented in a dashboard that offers multiple alternative visualization options. The dashboard updates periodically based on the arriving stream of events. The solution has been successfully validated with respect to the established functional and non-functional requirements using event streams corresponding to two real-life business processes