High Performance Queueing and Scheduling in Support of Multicasting in Input-Queued Switches

Due to its mild requirement on the bandwidth of switching fabric and internal memory, the input-queued architecture is a practical solution for today\u27s very high-speed switches. One of the notoriously difficult problems in the design of input-queued switches with very high link rates is the high performance queueing and scheduling of multicast traffic. This dissertation focuses on proposing novel solutions for this problem. The design challenge stems from the nature of multicast traffic, i.e., a multicast packet typically has multiple destinations. On the one hand, this nature makes queueing and scheduling of multicast traffic much more difficult than that of unicast traffic. For example, virtual output queueing is widely used to completely avoid the head-of-line blocking and achieve 100% throughput for unicast traffic. Nevertheless, the exhaustive, multicast virtual output queueing is impractical and results in out-of-order delivery. On the other hand, in spite of extensive studies in the context of either pure unicast traffic or pure multicast traffic, the results from a study in one context are not applicable to the other context due to the difference between the natures of unicast and multicast traffic. The design of integrated scheduling for both types of traffic remains an open issue. The main contribution of this dissertation is twofold: firstly, the performance of an interesting approach to efficiently mitigate head-of-line blocking for multicast traffic is theoretically analyzed; secondly, two novel algorithms are proposed to efficiently integrate unicast and multicast scheduling within one switching fabric. The research work presented in this dissertation concludes that (1) a small number of queues are sufficient to maximize the saturation throughput and delay performances of a large multicast switch with multiple first-in-first-out queues per input port; (2) the theoretical analysis results are indeed valid for practical large-sized switches; (3) for a large M × N multicast switch, the final achievable saturation throughput decreases as the ratio of M/N decreases; (4) and the two proposed integration algorithms exhibit promising performances in terms of saturation throughput, delay, and packet loss ratio under both uniform Bernoulli and uniform bursty traffic

Building Internet caching systems for streaming media delivery

The proxy has been widely and successfully used to cache the static Web objects fetched by a client so that the subsequent clients requesting the same Web objects can be served directly from the proxy instead of other sources faraway, thus reducing the server\u27s load, the network traffic and the client response time. However, with the dramatic increase of streaming media objects emerging on the Internet, the existing proxy cannot efficiently deliver them due to their large sizes and client real time requirements.;In this dissertation, we design, implement, and evaluate cost-effective and high performance proxy-based Internet caching systems for streaming media delivery. Addressing the conflicting performance objectives for streaming media delivery, we first propose an efficient segment-based streaming media proxy system model. This model has guided us to design a practical streaming proxy, called Hyper-Proxy, aiming at delivering the streaming media data to clients with minimum playback jitter and a small startup latency, while achieving high caching performance. Second, we have implemented Hyper-Proxy by leveraging the existing Internet infrastructure. Hyper-Proxy enables the streaming service on the common Web servers. The evaluation of Hyper-Proxy on the global Internet environment and the local network environment shows it can provide satisfying streaming performance to clients while maintaining a good cache performance. Finally, to further improve the streaming delivery efficiency, we propose a group of the Shared Running Buffers (SRB) based proxy caching techniques to effectively utilize proxy\u27s memory. SRB algorithms can significantly reduce the media server/proxy\u27s load and network traffic and relieve the bottlenecks of the disk bandwidth and the network bandwidth.;The contributions of this dissertation are threefold: (1) we have studied several critical performance trade-offs and provided insights into Internet media content caching and delivery. Our understanding further leads us to establish an effective streaming system optimization model; (2) we have designed and evaluated several efficient algorithms to support Internet streaming content delivery, including segment caching, segment prefetching, and memory locality exploitation for streaming; (3) having addressed several system challenges, we have successfully implemented a real streaming proxy system and deployed it in a large industrial enterprise

Optimization inWeb Caching: Cache Management, Capacity Planning, and Content Naming

Caching is fundamental to performance in distributed information retrieval systems such as the World Wide Web. This thesis introduces novel techniques for optimizing performance and cost-effectiveness in Web cache hierarchies. When requests are served by nearby caches rather than distant servers, server loads and network traffic decrease and transactions are faster. Cache system design and management, however, face extraordinary challenges in loosely-organized environments like the Web, where the many components involved in content creation, transport, and consumption are owned and administered by different entities. Such environments call for decentralized algorithms in which stakeholders act on local information and private preferences. In this thesis I consider problems of optimally designing new Web cache hierarchies and optimizing existing ones. The methods I introduce span the Web from point of content creation to point of consumption: I quantify the impact of content-naming practices on cache performance; present techniques for variable-quality-of-service cache management; describe how a decentralized algorithm can compute economically-optimal cache sizes in a branching two-level cache hierarchy; and introduce a new protocol extension that eliminates redundant data transfers and allows “dynamic” content to be cached consistently. To evaluate several of my new methods, I conducted trace-driven simulations on an unprecedented scale. This in turn required novel workload measurement methods and efficient new characterization and simulation techniques. The performance benefits of my proposed protocol extension are evaluated using two extraordinarily large and detailed workload traces collected in a traditional corporate network environment and an unconventional thin-client system. My empirical research follows a simple but powerful paradigm: measure on a large scale an important production environment’s exogenous workload; identify performance bounds inherent in the workload, independent of the system currently serving it; identify gaps between actual and potential performance in the environment under study; and finally devise ways to close these gaps through component modifications or through improved inter-component integration. This approach may be applicable to a wide range of Web services as they mature.Ph.D.Computer Science and EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/90029/1/kelly-optimization_web_caching.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90029/2/kelly-optimization_web_caching.ps.bz

User modeling servers - requirements, design, and evaluation

Softwaresysteme, die ihre Services an Charakteristika individueller Benutzer anpassen haben sich bereits als effektiver und/oder benutzerfreundlicher als statische Systeme in mehreren Anwendungsdomänen erwiesen. Um solche Anpassungsleistungen anbieten zu können, greifen benutzeradaptive Systeme auf Modelle von Benutzercharakteristika zurück. Der Aufbau und die Verwaltung dieser Modelle wird durch dezidierte Benutzermodellierungskomponenten vorgenommen. Ein wichtiger Zweig der Benutzermodellierungsforschung beschäftigt sich mit der Entwicklung sogenannter ?Benutzermodellierungs-Shells?, d.h. generischen Benutzermodellierungssystemen, die die Entwicklung anwendungsspezifischer Benutzermodellierungskomponenten erleichtern. Die Bestimmung des Leistungsumfangs dieser generischen Benutzermodellierungssysteme und deren Dienste bzw. Funktionalitäten wurde bisher in den meisten Fällen intuitiv vorgenommen und/oder aus Beschreibungen weniger benutzeradaptiver Systeme in der Literatur abgeleitet. In der jüngeren Vergangenheit führte der Trend zur Personalisierung im World Wide Web zur Entwicklung mehrerer kommerzieller Benutzermodellierungsserver. Die für diese Systeme als wichtig erachteten Eigenschaften stehen im krassen Gegensatz zu denen, die bei der Entwicklung der Benutzermodellierungs-Shells im Vordergrund standen und umgekehrt. Vor diesem Hintergrund ist das Ziel dieser Dissertation (i) Anforderungen an Benutzermodellierungsserver aus einer multi-disziplinären wissenschaftlichen und einer einsatzorientierten (kommerziellen) Perspektive zu analysieren, (ii) einen Server zu entwerfen und zu implementieren, der diesen Anforderungen genügt, und (iii) die Performanz und Skalierbarkeit dieses Servers unter der Arbeitslast kleinerer und mittlerer Einsatzumgebungen gegen die diesbezüglichen Anforderungen zu überprüfen. Um dieses Ziel zu erreichen, verfolgen wir einen anforderungszentrierten Ansatz, der auf Erfahrungen aus verschiedenen Forschungsbereichen aufbaut. Wir entwickeln eine generische Architektur für einen Benutzermodellierungsserver, die aus einem Serverkern für das Datenmanagement und modular hinzufügbaren Benutzermodellierungskomponenten besteht, von denen jede eine wichtige Benutzermodellierungstechnik implementiert. Wir zeigen, dass wir durch die Integration dieser Benutzermodellierungskomponenten in einem Server Synergieeffekte zwischen den eingesetzten Lerntechniken erzielen und bekannte Defizite einzelner Verfahren kompensieren können, beispielsweise bezüglich Performanz, Skalierbarkeit, Integration von Domänenwissen, Datenmangel und Kaltstart. Abschließend präsentieren wir die wichtigsten Ergebnisse der Experimente, die wir durchgeführt haben um empirisch nachzuweisen, dass der von uns entwickelte Benutzermodellierungsserver zentralen Performanz- und Skalierbarkeitskriterien genügt. Wir zeigen, dass unser Benutzermodellierungsserver die vorbesagten Kriterien in Anwendungsumgebungen mit kleiner und mittlerer Arbeitslast in vollem Umfang erfüllt. Ein Test in einer Anwendungsumgebung mit mehreren Millionen Benutzerprofilen und einer Arbeitslast, die als repräsentativ für größere Web Sites angesehen werden kann bestätigte, dass die Performanz der Benutzermodellierung unseres Servers keine signifikante Mehrbelastung für eine personalisierte Web Site darstellt. Gleichzeitig können die Anforderungen an die verfügbare Hardware als moderat eingestuft werden