Exploring interconnect energy savings under East-West traffic pattern of MapReduce clusters

An important challenge of modern data centers is to reduce energy consumption, of which a substantial proportion is due to the network. Energy Efficient Ethernet (EEE) is a recent standard that aims to reduce network power consumption, but current practice is to disable it in production use, since it has a poorly understood impact on real world application performance. An important application framework commonly used in modern data centers is Apache Hadoop, which implements the MapReduce programming model. This paper is the first to analyse the impact of EEE on MapReduce workloads, in terms of performance overheads and energy savings. We find that optimum energy savings are possible if the links use packet coalescing. Packet coalescing must, however, be carefully configured in order to avoid excessive performance degradation.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contract TIN2012-34557, HiPEAC-3 Network of Excellence (ICT-287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Postprint (author's final draft

Energy Efficient Ethernet on MapReduce Clusters: Packet Coalescing To Improve 10GbE Links

An important challenge of modern data centers is to reduce energy consumption, of which a substantial proportion is due to the network. Switches and NICs supporting the recent energy efficient Ethernet (EEE) standard are now available, but current practice is to disable EEE in production use, since its effect on real world application performance is poorly understood. This paper contributes to this discussion by analyzing the impact of EEE on MapReduce workloads, in terms of performance overheads and energy savings. MapReduce is the central programming model of Apache Hadoop, one of the most widely used application frameworks in modern data centers. We find that, while 1GbE links (edge links) achieve good energy savings using the standard EEE implementation, optimum energy savings in the 10 GbE links (aggregation and core links) are only possible, if these links employ packet coalescing. Packet coalescing must, however, be carefully configured in order to avoid excessive performance degradation. With our new analysis of how the static parameters of packet coalescing perform under different cluster loads, we were able to cover both idle and heavy load periods that can exist on this type of environment. Finally, we evaluate our recommendation for packet coalescing for 10 GbE links using the energy-delay metric. This paper is an extension of our previous work [1], which was published in the Proceedings of the 40th Annual IEEE Conference on Local Computer Networks (LCN 2015).This work was supported in part by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant 610456 (EUROSERVER), in part by the Spanish Government through the Severo Ochoa programme (SEV-2011-00067 and SEV-2015-0493), in part by the Spanish Ministry of Economy a nd Competitiveness under Contract TIN2012-34557 and Contract TIN2015-65316-P, and in part by the Generalitat de Catalunya under Contract 2014-SGR-1051 and Contract 2014-SGR-1272.Peer ReviewedPostprint (author's final draft

Optimizing dual-mode EEE interfaces: Deep-Sleep is healthy

The IEEE 802.3bj standard defines two potential low power operating modes for high speed energy efficient ethernet (EEE) physical interfaces working at 40 and 100 Gb/s: a not-so-efficient low power mode that requires very short transition times to restore normal operation (Fast-Wake) and a highly efficient low power mode with longer transition times (Deep-Sleep). In this paper, we present a new frame coalescing mechanism that dynamically adjusts the coalescing queue threshold in order to minimize the energy consumption of dual-mode EEE interfaces and maintains, at the same time, the average frame delay close to a target value. The proposed mechanism has been validated through simulation under different types of traffic (Poisson, self-similar, and real Internet traffic). In addition, we show that, with the current transition times and efficiency profiles of the standardized low power modes, our proposal renders the Fast-Wake mode unnecessary in most practical scenarios.Xunta de Galici

Traffic models for data networks and energy efficient control techniques

Questo lavoro tratta lo studio di modelli di traffico per reti di dati concentrandosi sul modello self-similar, e analizza tecniche di controllo Energy Efficient, con particolare attenzione rivolta allo standard IEEE 802.3az. Inoltre viene presentato un nuovo algoritmo che utilizza come idea quella dello standard IEEE 802.3az e la combina con le proprietà statistiche del traffico al fine di ottenere ulteriori risparmi energetici. Nella parte finale sono riportati i risultati delle simulazion

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

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