Latency-Aware aedia delivery through software-defined networks

Latency-Aware Media Delivery through Software-Defined Networks. NEM SUMMIT 2016 Conference Proceedings

Dynamic adaptive video streaming with minimal buffer sizes

Recently, adaptive streaming has been widely adopted in video streaming services to improve the Quality-of-Experience (QoE) of video delivery over the Internet. However, state-of-the-art bitrate adaptation achieves satisfactory performance only with extensive buffering of several tens of seconds. This leads to high playback latency in video delivery, which is undesirable especially in the context of live content with a low upper bound on the latency. Therefore, this thesis aims at pushing the application of adaptive streaming to its limit with respect to the buffer size, which is the dominant factor of the streaming latency. In this work, we first address the minimum buffering size required in adaptive streaming, which provides us with guidelines to determine a reasonable low latency for streaming systems. Then, we tackle the fundamental challenge of achieving such a low-latency streaming by developing a novel adaptation algorithm that stabilizes buffer dynamics despite a small buffer size. We also present advanced improvements by designing a novel adaptation architecture with low-delay feedback for the bitrate selection and optimizing the underlying transport layer to offer efficient realtime streaming. Experimental evaluations demonstrate that our approach achieves superior QoE in adaptive video streaming, especially in the particularly challenging case of low-latency streaming.In letzter Zeit setzen immer mehr Anbieter von Video-Streaming im Internet auf adaptives Streaming um die Nutzererfahrung (QoE) zu verbessern. Allerdings erreichen aktuelle Bitrate-Adaption-Algorithmen nur dann eine zufriedenstellende Leistung, wenn sehr große Puffer in der Größenordnung von mehreren zehn Sekunden eingesetzt werden. Dies führt zu großen Latenzen bei der Wiedergabe, was vor allem bei Live-Übertragungen mit einer niedrigen Obergrenze für Verzögerungen unerwünscht ist. Aus diesem Grund zielt die vorliegende Dissertation darauf ab adaptive Streaming-Anwendung im Bezug auf die Puffer-Größe zu optimieren da dies den Hauptfaktor für die Streaming-Latenz darstellt. In dieser Arbeit untersuchen wir zuerst die minimale benötigte Puffer-Größe für adaptives Streaming, was uns ermöglicht eine sinnvolle Untergrenze für die erreichbare Latenz festzulegen. Im nächsten Schritt gehen wir die grundlegende Herausforderung an dieses Optimum zu erreichen. Hierfür entwickeln wir einen neuartigen Adaptionsalgorithmus, der es ermöglicht den Füllstand des Puffers trotz der geringen Größe zu stabilisieren. Danach präsentieren wir weitere Verbesserungen indem wir eine neue Adaptions-Architektur für die Datenraten-Anpassung mit geringer Feedback-Verzögerung designen und das darunter liegende Transportprotokoll optimieren um effizientes Echtzeit-Streaming zu ermöglichen. Durch experimentelle Prüfung zeigen wir, dass unser Ansatz eine verbesserte Nutzererfahrung für adaptives Streaming erreicht, vor allem in besonders herausfordernden Fällen, wenn Streaming mit geringer Latenz gefordert ist

Look ahead to improve QoE in DASH streaming

[EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.This work is supported by the PAID-10-18 Program of the Universitat Politecnica de Valencia (Ayudas para contratos de acceso al sistema espanol de Ciencia, Tecnologia e Innovacion, en estructuras de investigacion de la Universitat Politecnica de Valencia) and by the Project 20180810 from the Universitat Politecnica de Valencia ("Tecnologias de distribucion y procesado de informacion multimedia y QoE").Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Guerri Cebollada, JC. (2020). Look ahead to improve QoE in DASH streaming. 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Universitat Politècnica de València. http://hdl.handle.net/10251/33359 .Belda R, de Fez I, Arce P, Guerri J C (2018) Look ahead: a DASH adaptation algorithm. Proc. of the IEEE Int. Symp. On broadband multimed. Syst. And broadcast., Valencia, Spain: article no. 158. https://doi.org/10.1109/BMSB.2018.8436718 .Blender Foundation webpage. Available online at: https://www.blender.org/foundation . Accessed: Jun. (2019).Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20-3:273–297. https://doi.org/10.1023/A:1022627411411DASH Industry forum webpage. Available online at: http://dashif.org . Accessed: Jun. (2019)Ghadiyaram D, Pan J, Bovik AC (2019) A subjective and objective study of stalling events in mobile streaming videos. IEEE Trans on Circuits and Syst for Video Technol 29(1):183–197. https://doi.org/10.1109/TCSVT.2017.2768542Ghent University. 4G/LTE bandwidth logs. Available online at: http://users.ugent.be/~jvdrhoof/dataset-4g . Accessed: Jun. (2019).Github webpage. A DASH segment size aware rate adaptation model for DASH. Available online at: https://github.com/pari685/AStream . Accessed: Jun. (2019)GitHub website. Dashgen, Multimedia Communications Group. Available online at: https://github.com/comm-iteam/dashgen . Accessed: Jun. (2019).van der Hooft J, Petrangeli S, Wauters T, Huysegems R, Alface PR, Bostoen T, De Turck F (2016) HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks. IEEE Commun Lett 20(1):2177–2180. https://doi.org/10.1109/LCOMM.2016.2601087Huang TY, Johari R, McKeown N, Trunnell M, Watson M (2014) A buffer-based approach to rate adaptation: evidence from a large video streaming service. Proc. of the 2014 ACM Conf. On SIGCOMM, Chicago, IL, USA: 187-198. https://doi.org/10.1145/2619239.2626296Institute of Telecommunications and Multimedia Applications website. Look Ahead Demo. Available online at: https://lookahead.iteam.upv.es . Accessed: Jun. (2019)ISO/IEC 23009–1:2014 (2014) Dynamic adaptive streaming over HTTP (DASH) - Part 1: media presentation description and segment formats.Juluri P, Tamarapalli V, Medhi D (2015) SARA: segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. Proc. of the IEEE Int. Conf. On Commun. Workshop (ICCW), London, UK: 1765-1770. https://doi.org/10.1109/ICCW.2015.7247436 .Juluri P, Tamarapalli V, Medhi D (2016) QoE management in DASH systems using the segment aware rate adaptation algorithm. Proc. of the IEEE/IFIP Netw. Oper. And Manag. Symp. (NOMS), Istanbul, Turkey: 129-136. https://doi.org/10.1109/NOMS.2016.7502805 .Kua J, Armitage G, Branch P (2017) A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun Surv & Tutor 19(3):1842–1866. https://doi.org/10.1109/COMST.2017.2685630Lee S, Youn K, Chung K (2015) Adaptive video quality control scheme to improve QoE of MPEG DASH. Proc. of IEEE Int. Conf. On Consum. Electron. (ICCE), Las Vegas, NV, USA: 126-127. https://doi.org/10.1109/ICCE.2015.7066348 .Li S, Zhang F, Ma L, Ngan K (2011) Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Trans. on Multimed. 13-5:935–949. https://doi.org/10.1109/TMM.2011.2152382Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. Proc. of the second annual ACM Conf. On multimed. Syst. (MMSys), San Jose, CA, USA: 169-174. https://doi.org/10.1145/1943552.1943575 .Medium webpage (2016) Toward a practical perceptual video quality metric. Available online at: https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652 . Accessed: Jun. 2019.Mobile Video Service Performance Study (2015) HUAWEI white paper. Available online at: http://www.ctiforum.com/uploadfile/2015/0701/20150701091255294.pdf .Mok RKP, Luo X, Chan EWW, Chang RKC (2012) QDASH: a QoE-aware DASH system. Proc. of multim. Syst. Conf. (MMSys), Chapel Hill, NC, USA: 11-22. https://doi.org/10.1145/2155555.2155558Moldovan C, Hagn K, Sieber C, Kellerer W, Hoßfeld T (2017) Keep calm and don’t switch: about the relationship between switches and quality in HAS. Proc. of the Int. Teletraffic Congr. (ITC), Genoa, Italy: pp. 1-6. https://doi.org/10.23919/ITC.2017.8065802Müller C, Lederer S, Timmerer C (2012) An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. Proc. of the 4th workshop on mob. Video (MoVid), Chapel Hill, NC, USA: 37-42. https://doi.org/10.1145/2151677.2151686Nguyen T, Vu T, Nguyen DV, Ngoc NP, and Thang TC (2015) QoE optimization for adaptive streaming with multiple VBR videos. Proc. of the Int. Conf. On comp., Manag. And Telecommun. (ComManTel), DaNang, Vietnam: 189-193. https://doi.org/10.1109/ComManTel.2015.7394285 .Qin Y, H. Shuai, Pattipati K R, Qian F, Sen S, Wang B, Yue C (2018) ABR Streaming of VBR-encoded videos: characterization, challenges, and solutions. Proc. of ACM CoNext 2018, Heraklion, Greece: 366–378. https://doi.org/10.1145/3281411.3281439 .Samain J, Carofiglio G, Muscariello L, Papalini M, Sardara M, Tortelli M, Rossi D (2017) Dynamic adaptive video streaming: towards a systematic comparison of ICN and TCP/IP. IEEE Trans on Multimed 19(10):2166–2181. https://doi.org/10.1109/TMM.2017.2733340Sheikh H, Bovik A (2006) Image information and visual quality. IEEE Trans on Image Process 15(2):430–444. https://doi.org/10.1109/TIP.2005.859378Shuai Y, Herfet T (2016). A buffer dynamic stabilizer for low-latency adaptive video streaming. Proc. of the Int. Conf. on Consum. Electron., Berlin: 1–5. https://doi.org/10.1109/ICCE-Berlin.2016.7684742 .Tavakoli S, Egger S, Seufert M, Schatz R, Brunnström K, García N (2016) Perceptual quality of HTTP adaptive streaming strategies: cross-experimental analysis of multi-laboratory and crowdsourced subjective studies. 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Published: May (2018)Yu L, Tillo T, Xiao J (2017) QoE-driven dynamic adaptive video streaming strategy with future information. IEEE Trans on Broadcast 63-3:523–534. https://doi.org/10.1109/TBC.2017.2687698Zhao S, Li Z, Medhi D, Lai P, Liu S (2017) Study of user QoE improvement for dynamic adaptive streaming over HTTP (MPEG-DASH). Proc. of the Int. Conf. On Comput., network. And Commun. (ICNC): multimed. Comput. And Commun., Santa Clara, CA, USA: 566-570. https://doi.org/10.1109/ICCNC.2017.7876191 .Zhou Y, Duan Y, Sun J, Guo Z (2014) Towards a simple and smooth rate adaption for VBR video in DASH. Proc. of the IEEE Vis. Commun. and Image Process. Conf, Valletta, pp 9–12. https://doi.org/10.1109/VCIP.2014.7051491Zhou C, Lin C-W, Guo Z (2016) mDASH: a Markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. on Multimed 18(4):738–751. https://doi.org/10.1109/TMM.2016.252265

Optimized traffic scheduling and routing in smart home networks

Home networks are evolving rapidly to include heterogeneous physical access and a large number of smart devices that generate different types of traffic with different distributions and different Quality of Service (QoS) requirements. Due to their particular architectures, which are very dense and very dynamic, the traditional one-pair-node shortest path solution is no longer efficient to handle inter-smart home networks (inter-SHNs) routing constraints such as delay, packet loss, and bandwidth in all-pair node heterogenous links. In addition, Current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decisions for bandwidth allocation. Such priority based scheduling methods are not optimal to provide both QoS and Quality of Experience (QoE), especially for smart home applications, since higher priority traffic does not necessarily require higher stringent delay than lower-priority traffic. Moreover, current QoS-aware scheduling methods in the intra-smart home network (intra-SHN) do not consider concurrent traffic caused by the fluctuation of intra-SH network traffic distributions. Thus, the goal of this dissertation is to build an efficient heterogenous multi-constrained routing mechanism and an optimized traffic scheduling tool in order to maintain a cost-effective communication between all wired-wireless connected devices in inter-SHNs and to effectively process concurrent and non-concurrent traffic in intra-SHN. This will help Internet service providers (ISPs) and home user to enhance the overall QoS and QoE of their applications while maintaining a relevant communication in both inter-SHNs and intra-SHN. In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to build a cost-effective routing mechanism in heterogonous inter-SHNs ? ii) how to efficiently schedule the multi-sourced intra-SHN traffic based on both QoS and QoE ? and iii) how to design an optimized queuing model for intra-SHN concurrent traffics while considering their QoS requirements? As part of our contributions to solve the first problem highlighted above, we present an analytical framework for dynamically optimizing data flows in inter-SHNs using Software-defined networking (SDN). We formulate a QoS-based routing optimization problem as a constrained shortest path problem and then propose an optimized solution (QASDN) to determine minimal cost between all pairs of nodes in the network taking into account the different types of physical accesses and the network utilization patterns. To address the second issue and to solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision meeting delay requirements of all traffic while preserving their degrees of criticality. A new metric combining the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined. Finally, as part of our contribution to address the third issue, we present an analytic model for a QoS-aware scheduling optimization of concurrent intra-SHN traffics with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in intra-SHN, propose an innovative queuing model (QC-SH) based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. The results of our work offer SHNs more effective data transfer between all heterogenous connected devices with optimal resource utilization, a dynamic QoS/QoE-aware traffic processing in SHN as well as an innovative model for optimizing concurrent SHN traffic scheduling with enhanced fairness strategy. Numerical results show an improvement up to 90% for network resource utilization, 77% for bandwidth, 40% for scheduling with QoS and QoE and 57% for concurrent traffic scheduling delay using our proposed solutions compared with Traditional methods

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Thermal and QoS-Aware Embedded Systems

While embedded systems such as smartphones and smart cars become essential parts of our lives, they face urgent thermal challenges. Extreme thermal conditions (i.e., both high and low temperatures) degrade system reliability, even risking safety; devices in the cold environments unexpectedly go offline, whereas extremely high device temperatures can cause device failures or battery explosions. These thermal limits become close to the norm because of ever-increasing chip power densities and application complexities. Embedded systems in the wild, however, lack adaptive and effective solutions to overcome such thermal challenges. An adaptive thermal management solution must cope with various runtime thermal scenarios under a changing ambient temperature. An effective solution requires the understanding of the dynamic thermal behaviors of underlying hardware and application workloads to ensure thermal and application quality-of-service (QoS) requirements. This thesis proposes a suite of adaptive and effective thermal management solutions to address different aspects of real-world thermal challenges faced by modern embedded systems. First, we present BPM, a battery-aware power management framework for mobile devices to address the unexpected device shutoffs in cold environments. We develop BPM as a background service that characterizes and controls real-time battery behaviors to maintain operable conditions even in cold environments. We then propose eTEC, building on the thermoelectric cooling solution, which adaptively controls cooling and computational power to avoid mobile devices overheating. For the real-time embedded systems such as cars, we present RT-TRM, a thermal-aware resource management framework that monitors changing ambient temperatures and allocates system resources to individual tasks. Next, we target in-vehicle vision systems running on CPUs–GPU system-on-chips and develop CPU–GPU co-scheduling to tackle thermal imbalance across CPUs caused by GPU heat. We evaluate all of these solutions using representative mobile/automotive platforms and workloads, demonstrating their effectiveness in meeting thermal and QoS requirements.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153350/1/ymoonlee_1.pd

Mejora del streaming de vídeo en DASH con codificación de bitrate variable mediante el algoritmo Look Ahead y mecanismos de coordinación para la reproducción, y propuesta de nuevas métricas para la evaluación de la QoE

[ES] Esta tesis presenta diversas propuestas encaminadas a mejorar la transmisión de vídeo a través del estándar DASH (Dynamic Adaptive Streaming over HTTP). Este trabajo de investigación estudia el protocolo de transmisión DASH y sus características. A la vez, plantea la codificación con calidad constante y bitrate variable como modo de codificación del contenido de vídeo más indicado para la transmisión de contenido bajo demanda mediante el estándar DASH. Derivado de la propuesta de utilización del modo de codificación de calidad constante, cobra mayor importancia el papel que juegan los algoritmos de adaptación en la experiencia de los usuarios al consumir el contenido multimedia. En este sentido, esta tesis presenta un algoritmo de adaptación denominado Look Ahead el cual, sin modificar el estándar, permite utilizar la información de los tamaños de los segmentos de vídeo incluida en los contenedores multimedia para evitar tomar decisiones de adaptación que desemboquen en paradas no deseadas en la reproducción de contenido multimedia. Con el objetivo de evaluar las posibles mejoras del algoritmo de adaptación presentado, se proponen tres modelos de evaluación objetiva de la QoE. Los modelos propuestos permiten predecir de forma sencilla la QoE que tendrían los usuarios de forma objetiva, utilizando parámetros conocidos como el bitrate medio, el PSNR (Peak Signal-to-Noise Ratio) y el valor de VMAF (Video Multimethod Assessment Fusion). Todos ellos aplicados a cada segmento. Finalmente, se estudia el comportamiento de DASH en entornos Wi-Fi con alta densidad de usuarios. En este contexto, se producen un número elevado de paradas en la reproducción por una mala estimación de la tasa de transferencia disponible debida al patrón ON/OFF de descarga de DASH y a la variabilidad del acceso al medio de Wi-Fi. Para paliar esta situación, se propone un servicio de coordinación basado en la tecnología SAND (MPEG's Server and Network Assisted DASH) que proporciona una estimación de la tasa de transferencia basada en la información del estado de los players de los clientes.[CA] Aquesta tesi presenta diverses propostes encaminades a millorar la transmissió de vídeo a través de l'estàndard DASH (Dynamic Adaptive Streaming over HTTP). Aquest treball de recerca estudia el protocol de transmissió DASH i les seves característiques. Alhora, planteja la codificació amb qualitat constant i bitrate variable com a manera de codificació del contingut de vídeo més indicada per a la transmissió de contingut sota demanda mitjançant l'estàndard DASH. Derivat de la proposta d'utilització de la manera de codificació de qualitat constant, cobra major importància el paper que juguen els algorismes d'adaptació en l'experiència dels usuaris en consumir el contingut. En aquest sentit, aquesta tesi presenta un algoritme d'adaptació denominat Look Ahead el qual, sense modificar l'estàndard, permet utilitzar la informació de les grandàries dels segments de vídeo inclosa en els contenidors multimèdia per a evitar prendre decisions d'adaptació que desemboquin en una parada indesitjada en la reproducció de contingut multimèdia. Amb l'objectiu d'avaluar les possibles millores de l'algoritme d'adaptació presentat, es proposen tres models d'avaluació objectiva de la QoE. Els models proposats permeten predir de manera senzilla la QoE que tindrien els usuaris de manera objectiva, utilitzant paràmetres coneguts com el bitrate mitjà, el PSNR (Peak Signal-to-Noise Ratio) i el valor de VMAF (Video Multimethod Assessment Fusion). Tots ells aplicats a cada segment. Finalment, s'estudia el comportament de DASH en entorns Wi-Fi amb alta densitat d'usuaris. En aquest context es produeixen un nombre elevat de parades en la reproducció per una mala estimació de la taxa de transferència disponible deguda al patró ON/OFF de descàrrega de DASH i a la variabilitat de l'accés al mitjà de Wi-Fi. Per a pal·liar aquesta situació, es proposa un servei de coordinació basat en la tecnologia SAND (MPEG's Server and Network Assisted DASH) que proporciona una estimació de la taxa de transferència basada en la informació de l'estat dels players dels clients.[EN] This thesis presents several proposals aimed at improving video transmission through the DASH (Dynamic Adaptive Streaming over HTTP) standard. This research work studies the DASH transmission protocol and its characteristics. At the same time, this work proposes the use of encoding with constant quality and variable bitrate as the most suitable video content encoding mode for on-demand content transmission through the DASH standard. Based on the proposal to use the constant quality encoding mode, the role played by adaptation algorithms in the user experience when consuming multimedia content becomes more important. In this sense, this thesis presents an adaptation algorithm called Look Ahead which, without modifying the standard, allows the use of the information on the sizes of the video segments included in the multimedia containers to avoid making adaptation decisions that lead to undesirable stalls during the playback of multimedia content. In order to evaluate the improvements of the presented adaptation algorithm, three models of objective QoE evaluation are proposed. These models allow to predict in a simple way the QoE that users would have in an objective way, using well-known parameters such as the average bitrate, the PSNR (Peak Signal-to-Noise Ratio) and the VMAF (Video Multimethod Assessment Fusion). All of them applied to each segment. Finally, the DASH behavior in Wi-Fi environments with high user density is analyzed. In this context, there could be a high number of stalls in the playback because of a bad estimation of the available transfer rate due to the ON/OFF pattern of DASH download and to the variability of the access to the Wi-Fi environment. To relieve this situation, a coordination service based on SAND (MPEG's Server and Network Assisted DASH) is proposed, which provides an estimation of the transfer rate based on the information of the state of the clients' players.Belda Ortega, R. (2021). Mejora del streaming de vídeo en DASH con codificación de bitrate variable mediante el algoritmo Look Ahead y mecanismos de coordinación para la reproducción, y propuesta de nuevas métricas para la evaluación de la QoE [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/169467TESI

Software Approaches to Manage Resource Tradeoffs of Power and Energy Constrained Applications

Power and energy efficiency have become an increasingly important design metric for a wide spectrum of computing devices. Battery efficiency, which requires a mixture of energy and power efficiency, is exceedingly important especially since there have been no groundbreaking advances in battery capacity recently. The need for energy and power efficiency stretches from small embedded devices to portable computers to large scale data centers. The projected future of computing demand, referred to as exascale computing, demands that researchers find ways to perform exaFLOPs of computation at a power bound much lower than would be required by simply scaling today's standards. There is a large body of work on power and energy efficiency for a wide range of applications and at different levels of abstraction. However, there is a lack of work studying the nuances of different tradeoffs that arise when operating under a power/energy budget. Moreover, there is no work on constructing a generalized model of applications running under power/energy constraints, which allows the designer to optimize their resource consumption, be it power, energy, time, bandwidth, or space. There is need for an efficient model that can provide bounds on the optimality of an application's resource consumption, becoming a basis against which online resource management heuristics can be measured. In this thesis, we tackle the problem of managing resource tradeoffs of power/energy constrained applications. We begin by studying the nuances of power/energy tradeoffs with the response time and throughput of stream processing applications. We then study the power performance tradeoff of batch processing applications to identify a power configuration that maximizes performance under a power bound. Next, we study the tradeoff of power/energy with network bandwidth and precision. Finally, we study how to combine tradeoffs into a generalized model of applications running under resource constraints. The work in this thesis presents detailed studies of the power/energy tradeoff with response time, throughput, performance, network bandwidth, and precision of stream and batch processing applications. To that end, we present an adaptive algorithm that manages stream processing tradeoffs of response time and throughput at the CPU level. At the task-level, we present an online heuristic that adaptively distributes bounded power in a cluster to improve performance, as well as an offline approach to optimally bound performance. We demonstrate how power can be used to reduce bandwidth bottlenecks and extend our offline approach to model bandwidth tradeoffs. Moreover, we present a tool that identifies parts of a program that can be downgraded in precision with minimal impact on accuracy, and maximal impact on energy consumption. Finally, we combine all the above tradeoffs into a flexible model that is efficient to solve and allows for bounding and/or optimizing the consumption of different resources

Novel performance evaluation of information and communication technologies to enable wide area monitoring systems for enhanced transmission network operation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The penetration of renewable energy sources has increased significantly in recent years due to the ongoing depletion of conventional resources and the transition to a low carbon energy system. Renewable energy sources such as wind energy are highly intermittent and unpredictable in nature, which makes the operation of the power grid more dynamic and therefore more complex. In order to operate the power system reliably under such conditions, Phasor Measurement Units (PMUs) through the use of satellite technology can offer a state-of-the-art Wide Area Monitoring System (WAMS) for improving power system monitoring, control and protection. They can improve the operation by providing highly precise and synchronised measurements near to real-time with higher frequency and accuracy. In order to achieve such objectives, a high-speed and reliable communications infrastructure is required to transfer time-critical PMU data from remote locations to the control centre. The signals measured by PMUs are transmitted across Local and Wide Area Networks, where they may encounter excessive delays. Signal delays can have a disruptive effect and make applications at best inefficient and at worse ineffective. The main research contribution of this thesis is the performance evaluation of communication infrastructures for WAMS. The evaluation begins from inside substations and continues over wide areas from substations to control centre. Through laboratory-based investigations and simulations, the performance of communications infrastructure in a typical power system substation has been analysed. In addition, the performance evaluation of WAMS communications infrastructure has been presented. In the modelling and analysis, an existing WAMS as installed on the GB transmission system has been considered. The actual PMU packets as received at the Phasor Data Concentrator (PDC) were captured for latency analysis. A novel algorithmic procedure has been developed and implemented to automate the large-scale latency calculations. Furthermore, the internal delays of PMUs have been investigated, determined and analysed. Subsequently, the WAMS has been simulated and detailed comparisons have been performed between the simulated model results and WAMS performance data captured from the actual WAMS. The validated WAMS model has been used for analysing possible future developments as well as to test newly proposed mechanisms, protocols, etc. in order to improve the communications infrastructure performance