Confucius Queue Management: Be Fair But Not Too Fast

When many users and unique applications share a congested edge link (e.g., a home network), everyone wants their own application to continue to perform well despite contention over network resources. Traditionally, network engineers have focused on fairness as the key objective to ensure that competing applications are equitably and led by the switch, and hence have deployed fair queueing mechanisms. However, for many network workloads today, strict fairness is directly at odds with equitable application performance. Real-time streaming applications, such as videoconferencing, suffer the most when network performance is volatile (with delay spikes or sudden and dramatic drops in throughput). Unfortunately, "fair" queueing mechanisms lead to extremely volatile network behavior in the presence of bursty and multi-flow applications such as Web traffic. When a sudden burst of new data arrives, fair queueing algorithms rapidly shift resources away from incumbent flows, leading to severe stalls in real-time applications. In this paper, we present Confucius, the first practical queue management scheme to effectively balance fairness against volatility, providing performance outcomes that benefit all applications sharing the contended link. Confucius outperforms realistic queueing schemes by protecting the real-time streaming flows from stalls in competing with more than 95% of websites. Importantly, Confucius does not assume the collaboration of end-hosts, nor does it require manual parameter tuning to achieve good performance

E-EON : Energy-Efficient and Optimized Networks for Hadoop

Energy efficiency and performance improvements have been two of the major concerns of current Data Centers. With the advent of Big Data, more information is generated year after year, and even the most aggressive predictions of the largest network equipment manufacturer have been surpassed due to the non-stop growing network traffic generated by current Big Data frameworks. As, currently, one of the most famous and discussed frameworks designed to store, retrieve and process the information that is being consistently generated by users and machines, Hadoop has gained a lot of attention from the industry in recent years and presently its name describes a whole ecosystem designed to tackle the most varied requirements of today’s cloud applications. This thesis relates to Hadoop clusters, mainly focused on their interconnects, which is commonly considered to be the bottleneck of such ecosystem. We conducted research focusing on energy efficiency and also on performance optimizations as improvements on cluster throughput and network latency. Regarding the energy consumption, a significant proportion of a data center's energy consumption is caused by the network, which stands for 12% of the total system power at full load. With the non-stop growing network traffic, it is desired by industry and academic community that network energy consumption should be proportional to its utilization. Considering cluster performance, although Hadoop is a network throughput-sensitive workload with less stringent requirements for network latency, there is an increasing interest in running batch and interactive workloads concurrently on the same cluster. Doing so maximizes system utilization, to obtain the greatest benefits from the capital and operational expenditures. For this to happen, cluster throughput should not be impacted when network latency is minimized. The two biggest challenges faced during the development of this thesis were related to achieving near proportional energy consumption for the interconnects and also improving the network latency found on Hadoop clusters, while having virtually no loss on cluster throughput. Such challenges led to comparable sized opportunity: proposing new techniques that must solve such problems from the current generation of Hadoop clusters. We named E-EON the set of techniques presented in this work, which stands for Energy Efficient and Optimized Networks for Hadoop. E-EON can be used to reduce the network energy consumption and yet, to reduce network latency while cluster throughput is improved at the same time. Furthermore, such techniques are not exclusive to Hadoop and they are also expected to have similar benefits if applied to any other Big Data framework infrastructure that fits the problem characterization we presented throughout this thesis. With E-EON we were able to reduce the energy consumption by up to 80% compared to the state-of-the art technique. We were also able to reduce network latency by up to 85% and in some cases, even improve cluster throughput by 10%. Although these were the two major accomplishment from this thesis, we also present minor benefits which translate to easier configuration compared to the stat-of-the-art techniques. Finally, we enrich the discussions found in this thesis with recommendations targeting network administrators and network equipment manufacturers.La eficiencia energética y las mejoras de rendimiento han sido dos de las principales preocupaciones de los Data Centers actuales. Con el arribo del Big Data, se genera más información año con año, incluso las predicciones más agresivas de parte del mayor fabricante de dispositivos de red se han superado debido al continuo tráfico de red generado por los sistemas de Big Data. Actualmente, uno de los más famosos y discutidos frameworks desarrollado para almacenar, recuperar y procesar la información generada consistentemente por usuarios y máquinas, Hadoop acaparó la atención de la industria en los últimos años y actualmente su nombre describe a todo un ecosistema diseñado para abordar los requisitos más variados de las aplicaciones actuales de Cloud Computing. Esta tesis profundiza sobre los clusters Hadoop, principalmente enfocada a sus interconexiones, que comúnmente se consideran el cuello de botella de dicho ecosistema. Realizamos investigaciones centradas en la eficiencia energética y también en optimizaciones de rendimiento como mejoras en el throughput de la infraestructura y de latencia de la red. En cuanto al consumo de energía, una porción significativa de un Data Center es causada por la red, representada por el 12 % de la potencia total del sistema a plena carga. Con el tráfico constantemente creciente de la red, la industria y la comunidad académica busca que el consumo energético sea proporcional a su uso. Considerando las prestaciones del cluster, a pesar de que Hadoop mantiene una carga de trabajo sensible al rendimiento de red aunque con requisitos menos estrictos sobre la latencia de la misma, existe un interés creciente en ejecutar aplicaciones interactivas y secuenciales de manera simultánea sobre dicha infraestructura. Al hacerlo, se maximiza la utilización del sistema para obtener los mayores beneficios al capital y gastos operativos. Para que esto suceda, el rendimiento del sistema no puede verse afectado cuando se minimiza la latencia de la red. Los dos mayores desafíos enfrentados durante el desarrollo de esta tesis estuvieron relacionados con lograr un consumo energético cercano a la cantidad de interconexiones y también a mejorar la latencia de red encontrada en los clusters Hadoop al tiempo que la perdida del rendimiento de la infraestructura es casi nula. Dichos desafíos llevaron a una oportunidad de tamaño semejante: proponer técnicas novedosas que resuelven dichos problemas a partir de la generación actual de clusters Hadoop. Llamamos a E-EON (Energy Efficient and Optimized Networks) al conjunto de técnicas presentadas en este trabajo. E-EON se puede utilizar para reducir el consumo de energía y la latencia de la red al mismo tiempo que el rendimiento del cluster se mejora. Además tales técnicas no son exclusivas de Hadoop y también se espera que tengan beneficios similares si se aplican a cualquier otra infraestructura de Big Data que se ajuste a la caracterización del problema que presentamos a lo largo de esta tesis. Con E-EON pudimos reducir el consumo de energía hasta en un 80% en comparación con las técnicas encontradas en la literatura actual. También pudimos reducir la latencia de la red hasta en un 85% y, en algunos casos, incluso mejorar el rendimiento del cluster en un 10%. Aunque estos fueron los dos principales logros de esta tesis, también presentamos beneficios menores que se traducen en una configuración más sencilla en comparación con las técnicas más avanzadas. Finalmente, enriquecimos las discusiones encontradas en esta tesis con recomendaciones dirigidas a los administradores de red y a los fabricantes de dispositivos de red

Steganographic Timing Channels

This paper describes steganographic timing channels that use cryptographic primitives to hide the presence of covert channels in the timing of network traffic. We have identified two key properties for steganographic timing channels: (1) the parameters of the scheme should be cryptographically keyed, and (2) the distribution of input timings should be indistinguishable from output timings. These properties are necessary (although we make no claim they are sufficient) for the undetectability of a steganographic timing channel. Without them, the contents of the channel can be read and observed by unauthorized persons, and the presence of the channel is trivially exposed by noticing large changes in timing distributions – a previously proposed methodology for covert channel detection. Our steganographic timing scheme meets the secrecy requirement by employing cryptographic keys, and we achieve a restricted form of input/output distribution parity. Under certain distributions, our schemes conforms to a uniformness property; input timings that are uniformly distributed modulo a timing window are indistinguishable from output timings, measured under the same modulo. We also demonstrate that our scheme is practical under real network conditions, and finally present an empirical study of its covertness using the firstorder entropy metric, as suggested by Gianvecchio and Wang [8], which is currently the best published practical detection heuristic for timing channels

TCP RAPID: FROM THEORY TO PRACTICE

Delay and bandwidth-based alternatives to TCP congestion-control have been around for nearly three decades and have seen a recent surge in interest. However, such designs have faced significant resistance in being deployed on a wide-scale across the Internet—this has been mostly due to serious concerns about noise in delay measurements, pacing inter-packet gaps, and required changes to the standard TCP stack. With the advent of high-speed networking, some of these concerns become even more significant. This thesis considers Rapid, a recent proposal for ultra-high speed congestion control, which perhaps stretches each of these challenges to the greatest extent. Rapid adopts a framework of continuous fine-scale bandwidth probing and rate adapting. It requires finely-controlled inter-packet gaps, high-precision timestamping of received packets, and reliance on fine-scale changes in interpacket gaps. While simulation-based evaluations of Rapid show that it has outstanding performance gains along several important dimensions, these will not translate to the real-world unless the above challenges are addressed. This thesis identifies the key challenges TCP Rapid faces on real high-speed networks, including deployability in standard protocol stacks, precise inter-packet gap creation, achieving robust bandwidth estimation in the presence of noise, and a stability/adaptability trade-off. A Linux implementation of Rapid is designed and developed after carefully considering each of these challenges. The evaluations on a 10Gbps testbed confirm that the implementation can indeed achieve the claimed performance gains, and that it would not have been possible unless each of the above challenges was addressed.Doctor of Philosoph

Reducing short flows' latency in the internet

Short flows are highly valuable in the modern Internet and are widely used by applications in the form of web requests or with user interactions. These kinds of applications are extremely sensitive to latency. A small additional delay, like one or two round trip times (RTTs), may easily cause user frustration and lose usability of services. In the most desirable scenario, we want to finish these kinds of flows in one network RTT. Furthermore, we would like the network's RTT to be as close as possible to the speed of light. Unfortunately, in the current Internet, there are many unnecessary delays caused by different kinds of policies--in particular, transmission protocol and routing policies--driving us far away from this goal. This thesis aims at answering the following two questions: How can we optimize the transmission protocol to reduce short flows' latency as close as possible to one RTT and why are network RTTs still significantly larger than the speed-of-light latency? To reduce the transmission latency, we focused on the two main components of short flows, connection establishment and data transmission. ASAP, a new naming and transport protocol, is introduced to reduce the time spent on initial TCP connections. It merges functionality of DNS and TCP's connection establishment functions by piggybacking the connection establishment procedure atop the DNS lookup process. With the help of ASAP, the host is able to save up to two-thirds of the time spent on initial connection without exposing significant DoS vulnerabilities. For data transmission, we designed a new control rate mechanism, Halfback, which achieves low latency with limited bandwidth overhead and only requires sender-side changes. Halfback has an aggressive startup phase, finishing transmission for most short flows in one RTT, together with a Reverse-Ordering Proactively Retransmission phase which helps the host to recovery quickly from packet loss caused by the aggressive startup phase. Halfback is able to achieve 56% smaller flow completion time on average and three times smaller in the 99th percentile. RTT between two hosts is able to be more than 6 times the speed-of-light latency for Directed Optical Fiber. To understand the composition of RTT inflation, we break down the path inflation on the end-to-end path into its contribution factors. Based on our result, 7.2% is caused by network topology, 18.8% is contributed by inter-domain routing policies, 54.9% is caused by peering policies, and 25.6% is caused by intra-domain routing policies. This result shows that the main component of the path inflation is caused by peering policies which may require more attention for future research. Besides this, we also analyze the changes of the inflation caused by each contributing factor across five years. According to our analysis, the total inflation has been reduced by around 6% each year since 2010

SWIFT: A Narrowband-Friendly Cognitive Wideband Network

Wideband technologies in the unlicensed spectrum can satisfy the ever-increasing demands for wireless bandwidth created by emerging rich media applications. The key challenge for such systems, however, is to allow narrowband technologies that share these bands (say, 802.11 a/b/g/n, Zigbee) to achieve their normal performance, without compromising the throughput or range of the wideband network.This paper presents SWIFT, the first system where high-throughput wideband nodes are shown in a working deployment to coexist with unknown narrowband devices, while forming a network of their own. Prior work avoids narrowband devices by operating below the noise level and limiting itself to a single contiguous unused band. While this achieves coexistence, it sacrifices the throughput and operating distance of the wideband device. In contrast, SWIFT creates high throughput wireless links by weaving together non-contiguous unused frequency bands that change as narrowband devices enter or leave the environment. This design principle of cognitive aggregation allows SWIFT to achieve coexistence, while operating at normal power, and thereby obtaining higher throughput and greater operating range. We implement SWIFT on a wideband hardware platform, and evaluate it in the presence of 802.11 devices. In comparison to a baseline that coexists with narrowband devices by operating below their noise level, SWIFT is equally narrowband-friendly but achieves 3.6x-10.5x higher throughput and 6x greater range

E-EON : Energy-Efficient and Optimized Networks for Hadoop

Energy efficiency and performance improvements have been two of the major concerns of current Data Centers. With the advent of Big Data, more information is generated year after year, and even the most aggressive predictions of the largest network equipment manufacturer have been surpassed due to the non-stop growing network traffic generated by current Big Data frameworks. As, currently, one of the most famous and discussed frameworks designed to store, retrieve and process the information that is being consistently generated by users and machines, Hadoop has gained a lot of attention from the industry in recent years and presently its name describes a whole ecosystem designed to tackle the most varied requirements of today’s cloud applications. This thesis relates to Hadoop clusters, mainly focused on their interconnects, which is commonly considered to be the bottleneck of such ecosystem. We conducted research focusing on energy efficiency and also on performance optimizations as improvements on cluster throughput and network latency. Regarding the energy consumption, a significant proportion of a data center's energy consumption is caused by the network, which stands for 12% of the total system power at full load. With the non-stop growing network traffic, it is desired by industry and academic community that network energy consumption should be proportional to its utilization. Considering cluster performance, although Hadoop is a network throughput-sensitive workload with less stringent requirements for network latency, there is an increasing interest in running batch and interactive workloads concurrently on the same cluster. Doing so maximizes system utilization, to obtain the greatest benefits from the capital and operational expenditures. For this to happen, cluster throughput should not be impacted when network latency is minimized. The two biggest challenges faced during the development of this thesis were related to achieving near proportional energy consumption for the interconnects and also improving the network latency found on Hadoop clusters, while having virtually no loss on cluster throughput. Such challenges led to comparable sized opportunity: proposing new techniques that must solve such problems from the current generation of Hadoop clusters. We named E-EON the set of techniques presented in this work, which stands for Energy Efficient and Optimized Networks for Hadoop. E-EON can be used to reduce the network energy consumption and yet, to reduce network latency while cluster throughput is improved at the same time. Furthermore, such techniques are not exclusive to Hadoop and they are also expected to have similar benefits if applied to any other Big Data framework infrastructure that fits the problem characterization we presented throughout this thesis. With E-EON we were able to reduce the energy consumption by up to 80% compared to the state-of-the art technique. We were also able to reduce network latency by up to 85% and in some cases, even improve cluster throughput by 10%. Although these were the two major accomplishment from this thesis, we also present minor benefits which translate to easier configuration compared to the stat-of-the-art techniques. Finally, we enrich the discussions found in this thesis with recommendations targeting network administrators and network equipment manufacturers.La eficiencia energética y las mejoras de rendimiento han sido dos de las principales preocupaciones de los Data Centers actuales. Con el arribo del Big Data, se genera más información año con año, incluso las predicciones más agresivas de parte del mayor fabricante de dispositivos de red se han superado debido al continuo tráfico de red generado por los sistemas de Big Data. Actualmente, uno de los más famosos y discutidos frameworks desarrollado para almacenar, recuperar y procesar la información generada consistentemente por usuarios y máquinas, Hadoop acaparó la atención de la industria en los últimos años y actualmente su nombre describe a todo un ecosistema diseñado para abordar los requisitos más variados de las aplicaciones actuales de Cloud Computing. Esta tesis profundiza sobre los clusters Hadoop, principalmente enfocada a sus interconexiones, que comúnmente se consideran el cuello de botella de dicho ecosistema. Realizamos investigaciones centradas en la eficiencia energética y también en optimizaciones de rendimiento como mejoras en el throughput de la infraestructura y de latencia de la red. En cuanto al consumo de energía, una porción significativa de un Data Center es causada por la red, representada por el 12 % de la potencia total del sistema a plena carga. Con el tráfico constantemente creciente de la red, la industria y la comunidad académica busca que el consumo energético sea proporcional a su uso. Considerando las prestaciones del cluster, a pesar de que Hadoop mantiene una carga de trabajo sensible al rendimiento de red aunque con requisitos menos estrictos sobre la latencia de la misma, existe un interés creciente en ejecutar aplicaciones interactivas y secuenciales de manera simultánea sobre dicha infraestructura. Al hacerlo, se maximiza la utilización del sistema para obtener los mayores beneficios al capital y gastos operativos. Para que esto suceda, el rendimiento del sistema no puede verse afectado cuando se minimiza la latencia de la red. Los dos mayores desafíos enfrentados durante el desarrollo de esta tesis estuvieron relacionados con lograr un consumo energético cercano a la cantidad de interconexiones y también a mejorar la latencia de red encontrada en los clusters Hadoop al tiempo que la perdida del rendimiento de la infraestructura es casi nula. Dichos desafíos llevaron a una oportunidad de tamaño semejante: proponer técnicas novedosas que resuelven dichos problemas a partir de la generación actual de clusters Hadoop. Llamamos a E-EON (Energy Efficient and Optimized Networks) al conjunto de técnicas presentadas en este trabajo. E-EON se puede utilizar para reducir el consumo de energía y la latencia de la red al mismo tiempo que el rendimiento del cluster se mejora. Además tales técnicas no son exclusivas de Hadoop y también se espera que tengan beneficios similares si se aplican a cualquier otra infraestructura de Big Data que se ajuste a la caracterización del problema que presentamos a lo largo de esta tesis. Con E-EON pudimos reducir el consumo de energía hasta en un 80% en comparación con las técnicas encontradas en la literatura actual. También pudimos reducir la latencia de la red hasta en un 85% y, en algunos casos, incluso mejorar el rendimiento del cluster en un 10%. Aunque estos fueron los dos principales logros de esta tesis, también presentamos beneficios menores que se traducen en una configuración más sencilla en comparación con las técnicas más avanzadas. Finalmente, enriquecimos las discusiones encontradas en esta tesis con recomendaciones dirigidas a los administradores de red y a los fabricantes de dispositivos de red.Postprint (published version

Techniques of data prefetching, replication, and consistency in the Internet

Internet has become a major infrastructure for information sharing in our daily life, and indispensable to critical and large applications in industry, government, business, and education. Internet bandwidth (or the network speed to transfer data) has been dramatically increased, however, the latency time (or the delay to physically access data) has been reduced in a much slower pace. The rich bandwidth and lagging latency can be effectively coped with in Internet systems by three data management techniques: caching, replication, and prefetching. The focus of this dissertation is to address the latency problem in Internet by utilizing the rich bandwidth and large storage capacity for efficiently prefetching data to significantly improve the Web content caching performance, by proposing and implementing scalable data consistency maintenance methods to handle Internet Web address caching in distributed name systems (DNS), and to handle massive data replications in peer-to-peer systems. While the DNS service is critical in Internet, peer-to-peer data sharing is being accepted as an important activity in Internet.;We have made three contributions in developing prefetching techniques. First, we have proposed an efficient data structure for maintaining Web access information, called popularity-based Prediction by Partial Matching (PB-PPM), where data are placed and replaced guided by popularity information of Web accesses, thus only important and useful information is stored. PB-PPM greatly reduces the required storage space, and improves the prediction accuracy. Second, a major weakness in existing Web servers is that prefetching activities are scheduled independently of dynamically changing server workloads. Without a proper control and coordination between the two kinds of activities, prefetching can negatively affect the Web services and degrade the Web access performance. to address this problem, we have developed a queuing model to characterize the interactions. Guided by the model, we have designed a coordination scheme that dynamically adjusts the prefetching aggressiveness in Web Servers. This scheme not only prevents the Web servers from being overloaded, but it can also minimize the average server response time. Finally, we have proposed a scheme that effectively coordinates the sharing of access information for both proxy and Web servers. With the support of this scheme, the accuracy of prefetching decisions is significantly improved.;Regarding data consistency support for Internet caching and data replications, we have conducted three significant studies. First, we have developed a consistency support technique to maintain the data consistency among the replicas in structured P2P networks. Based on Pastry, an existing and popular P2P system, we have implemented this scheme, and show that it can effectively maintain consistency while prevent hot-spot and node-failure problems. Second, we have designed and implemented a DNS cache update protocol, called DNScup, to provide strong consistency for domain/IP mappings. Finally, we have developed a dynamic lease scheme to timely update the replicas in Internet

Split-Domain TCP-Friendly Protocol For MPEG-4 Adaptive Rate Video Streaming Over 3G Networks

The imminent inception of third-generation (3G) mobile communication networks offers an unprecedented opportunity for the development of video streaming applications through wireless Internet access. Different design challenges exist in implementing video streaming connections spanning both wired and wireless domains. A split-domain TCP-friendly streaming video transmission protocol is presented based on adaptive rate encoding in the MPEG-4 video format. Network simulations are conducted to demonstrate the benefits and viability of such a video streaming scheme over existing options. Further feature enhancements and refinements are necessary for the proposed protocol to achieve its full potential