A Method Based on Data Fragmentation to Increase the Performance of ICTCP During Incast Congestion in Networks

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OFAR-CM: Efficient Dragonfly networks with simple congestion management

Dragonfly networks are appealing topologies for large-scale Data center and HPC networks, that provide high throughput with low diameter and moderate cost. However, they are prone to congestion under certain frequent traffic patterns that saturate specific network links. Adaptive non-minimal routing can be used to avoid such congestion. That kind of routing employs longer paths to circumvent local or global congested links. However, if a distance-based deadlock avoidance mechanism is employed, more Virtual Channels (VCs) are required, what increases design complexity and cost. OFAR (On-the-Fly Adaptive Routing) is a previously proposed routing that decouples VCs from deadlock avoidance, making local and global misrouting affordable. However, the severity of congestion with OFAR is higher, as it relies on an escape sub network with low bisection bandwidth. Additionally, OFAR allows for unlimited misroutings on the escape sub network, leading to unbounded paths in the network and long latencies. In this paper we propose and evaluate OFAR-CM, a variant of OFAR combined with a simple congestion management (CM) mechanism which only relies on local information, specifically the credit count of the output ports in the local router. With simple escape sub networks such as a Hamiltonian ring or a tree, OFAR outperforms former proposals with distance-based deadlock avoidance. Additionally, although long paths are allowed in theory, in practice packets arrive at their destination in a small number of hops. Altogether, OFAR-CM constitutes the first practicable mechanism to the date that supports both local and global misrouting in Dragonfly networks.The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. ERC-2012-Adg-321253- RoMoL, the Spanish Ministry of Science under contracts TIN2010-21291-C02-02, TIN2012-34557, and by the European HiPEAC Network of Excellence. M. García participated in this work while affiliated with the University of Cantabria.Peer ReviewedPostprint (author's final draft

Contention-based Nonminimal Adaptive Routing in High-radix Networks

Adaptive routing is an efficient congestion avoidance mechanism for modern Datacenter and HPC networks. Congestion detection traditionally relies on the occupancy of the router queues. However, this approach can hinder performance due to coarse-grain measurements with small buffers, and potential routing oscillations with large buffers. We introduce an alternative mechanism, labelled Contention-Based Adaptive Routing. Our mechanism adapts routing based on an estimation of “network contention”, the simultaneity of traffic flows contending for a network port. Our system employs a set of counters which track the demand for each output port. This exploits path diversity thanks to earlier detection of adversarial traffic patterns, and decouples buffer size and queue occupancy from contention detection. We evaluate our mechanism in a Dragonfly network. Our evaluations show this mechanism achieves optimal latency under uniform traffic and similar to best previous routing mechanisms under adversarial patterns, with immediate adaptation to traffic pattern changes

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Congestion control, energy efficiency and virtual machine placement for data centers

Data centers, facilities with communications network equipment and servers for data processing and/or storage, are prevalent and essential to provide a myriad of services and applications for various private, non-profit, and government systems, and they also form the foundation of cloud computing, which is transforming the technological landscape of the Internet. With rapid deployment of modern high-speed low-latency large-scale data centers, many issues have emerged in data centers, such as data center architecture design, congestion control, energy efficiency, virtual machine placement, and load balancing. The objective of this thesis is multi-fold. First, an enhanced Quantized Congestion Notification (QCN) congestion notification algorithm, called fair QCN (FQCN), is proposed to improve rate allocation fairness of multiple flows sharing one bottleneck link in data center networks. Detailed analysis on FQCN and simulation results is provided to validate the fair share rate allocation while maintaining the queue length stability. Furthermore, the effects of congestion notification algorithms, including QCN, AF-QCN and FQCN, are investigated with respect to TCP throughput collapse. The results show that FQCN can significantly enhance TCP throughput performance, and achieve better TCP throughput than QCN and AF-QCN in a TCP Incast setting. Second, a unified congestion detection, notification and control system for data center networks is designed to efficiently resolve network congestion in a uniform solution and to ensure convergence to statistical fairness with “no state” switches simultaneously. The architecture of the proposed system is described in detail and the FQCN algorithm is implemented in the proposed framework. The simulation results of the FQCN algorithm implemented in the proposed framework validate the robustness and efficiency of the proposed congestion control system. Third, a two-level power optimization model, namely, Hierarchical EneRgy Optimization (HERO), is established to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximizing link utilization. The power-saving performance of the proposed HERO model is evaluated by simulations with different traffic patterns. The simulation results have shown that HERO can reduce the power consumption of data center networks effectively with reduced complexity. Last, several heterogeneity aware dominant resource assistant heuristic algorithms, namely, dominant residual resource aware first-fit decreasing (DRR-FFD), individual DRR-FFD (iDRR-FFD) and dominant residual resource based bin fill (DRR-BinFill), are proposed for virtual machine (VM) consolidation. The proposed heuristic algorithms exploit the heterogeneity of the VMs’ requirements for different resources by capturing the differences among VMs’ demands, and the heterogeneity of the physical machines’ resource capacities by capturing the differences among physical machines’ resources. The performance of the proposed heuristic algorithms is evaluated with different classes of synthetic workloads under different VM requirement heterogeneity conditions, and the simulation results demonstrate that the proposed heuristics achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics

Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks

Congestion in a wireless sensor network causes an increase in the amount of data loss and delays in data transmission. In this paper, we propose a new congestion control technique (ACT, Adaptive Compression-based congestion control Technique) based on an adaptive compression scheme for packet reduction in case of congestion. The compression techniques used in the ACT are Discrete Wavelet Transform (DWT), Adaptive Differential Pulse Code Modulation (ADPCM), and Run-Length Coding (RLC). The ACT first transforms the data from the time domain to the frequency domain, reduces the range of data by using ADPCM, and then reduces the number of packets with the help of RLC before transferring the data to the source node. It introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. The ACT assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the amount of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrate that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods. Moreover, it exhibits a very high ratio of the available data in the sink

Impact of RoCE Congestion Control Policies on Distributed Training of DNNs

RDMA over Converged Ethernet (RoCE) has gained significant attraction for datacenter networks due to its compatibility with conventional Ethernet-based fabric. However, the RDMA protocol is efficient only on (nearly) lossless networks, emphasizing the vital role of congestion control on RoCE networks. Unfortunately, the native RoCE congestion control scheme, based on Priority Flow Control (PFC), suffers from many drawbacks such as unfairness, head-of-line-blocking, and deadlock. Therefore, in recent years many schemes have been proposed to provide additional congestion control for RoCE networks to minimize PFC drawbacks. However, these schemes are proposed for general datacenter environments. In contrast to the general datacenters that are built using commodity hardware and run general-purpose workloads, high-performance distributed training platforms deploy high-end accelerators and network components and exclusively run training workloads using collectives (All-Reduce, All-To-All) communication libraries for communication. Furthermore, these platforms usually have a private network, separating their communication traffic from the rest of the datacenter traffic. Scalable topology-aware collective algorithms are inherently designed to avoid incast patterns and balance traffic optimally. These distinct features necessitate revisiting previously proposed congestion control schemes for general-purpose datacenter environments. In this paper, we thoroughly analyze some of the SOTA RoCE congestion control schemes vs. PFC when running on distributed training platforms. Our results indicate that previously proposed RoCE congestion control schemes have little impact on the end-to-end performance of training workloads, motivating the necessity of designing an optimized, yet low-overhead, congestion control scheme based on the characteristics of distributed training platforms and workloads

Non-minimal adaptive routing for efficient interconnection networks

RESUMEN: La red de interconexión es un concepto clave de los sistemas de computación paralelos. El primer aspecto que define una red de interconexión es su topología. Habitualmente, las redes escalables y eficientes en términos de coste y consumo energético tienen bajo diámetro y se basan en topologías que encaran el límite de Moore y en las que no hay diversidad de caminos mínimos. Una vez definida la topología, quedando implícitamente definidos los límites de rendimiento de la red, es necesario diseñar un algoritmo de enrutamiento que se acerque lo máximo posible a esos límites y debido a la ausencia de caminos mínimos, este además debe explotar los caminos no mínimos cuando el tráfico es adverso. Estos algoritmos de enrutamiento habitualmente seleccionan entre rutas mínimas y no mínimas en base a las condiciones de la red. Las rutas no mínimas habitualmente se basan en el algoritmo de balanceo de carga propuesto por Valiant, esto implica que doblan la longitud de las rutas mínimas y por lo tanto, la latencia soportada por los paquetes se incrementa. En cuanto a la tecnología, desde su introducción en entornos HPC a principios de los años 2000, Ethernet ha sido usado en un porcentaje representativo de los sistemas. Esta tesis introduce una implementación realista y competitiva de una red escalable y sin pérdidas basada en dispositivos de red Ethernet commodity, considerando topologías de bajo diámetro y bajo consumo energético y logrando un ahorro energético de hasta un 54%. Además, propone un enrutamiento sobre la citada arquitectura, en adelante QCN-Switch, el cual selecciona entre rutas mínimas y no mínimas basado en notificaciones de congestión explícitas. Una vez implementada la decisión de enrutar siguiendo rutas no mínimas, se introduce un enrutamiento adaptativo en fuente capaz de adaptar el número de saltos en las rutas no mínimas. Este enrutamiento, en adelante ACOR, es agnóstico de la topología y mejora la latencia en hasta un 28%. Finalmente, se introduce un enrutamiento dependiente de la topología, en adelante LIAN, que optimiza el número de saltos de las rutas no mínimas basado en las condiciones de la red. Los resultados de su evaluación muestran que obtiene una latencia cuasi óptima y mejora el rendimiento de algoritmos de enrutamiento actuales reduciendo la latencia en hasta un 30% y obteniendo un rendimiento estable y equitativo.ABSTRACT: Interconnection network is a key concept of any parallel computing system. The first aspect to define an interconnection network is its topology. Typically, power and cost-efficient scalable networks with low diameter rely on topologies that approach the Moore bound in which there is no minimal path diversity. Once the topology is defined, the performance bounds of the network are determined consequently, so a suitable routing algorithm should be designed to accomplish as much as possible of those limits and, due to the lack of minimal path diversity, it must exploit non-minimal paths when the traffic pattern is adversarial. These routing algorithms usually select between minimal and non-minimal paths based on the network conditions, where the non-minimal paths are built according to Valiant load-balancing algorithm. This implies that these paths double the length of minimal ones and then the latency supported by packets increases. Regarding the technology, from its introduction in HPC systems in the early 2000s, Ethernet has been used in a significant fraction of the systems. This dissertation introduces a realistic and competitive implementation of a scalable lossless Ethernet network for HPC environments considering low-diameter and low-power topologies. This allows for up to 54% power savings. Furthermore, it proposes a routing upon the cited architecture, hereon QCN-Switch, which selects between minimal and non-minimal paths per packet based on explicit congestion notifications instead of credits. Once the miss-routing decision is implemented, it introduces two mechanisms regarding the selection of the intermediate switch to develop a source adaptive routing algorithm capable of adapting the number of hops in the non-minimal paths. This routing, hereon ACOR, is topology-agnostic and improves average latency in all cases up to 28%. Finally, a topology-dependent routing, hereon LIAN, is introduced to optimize the number of hops in the non-minimal paths based on the network live conditions. Evaluations show that LIAN obtains almost-optimal latency and outperforms state-of-the-art adaptive routing algorithms, reducing latency by up to 30.0% and providing stable throughput and fairness.This work has been supported by the Spanish Ministry of Education, Culture and Sports under grant FPU14/02253, the Spanish Ministry of Economy, Industry and Competitiveness under contracts TIN2010-21291-C02-02, TIN2013-46957-C2-2-P, and TIN2013-46957-C2-2-P (AEI/FEDER, UE), the Spanish Research Agency under contract PID2019-105660RBC22/AEI/10.13039/501100011033, the European Union under agreements FP7-ICT-2011- 7-288777 (Mont-Blanc 1) and FP7-ICT-2013-10-610402 (Mont-Blanc 2), the University of Cantabria under project PAR.30.P072.64004, and by the European HiPEAC Network of Excellence through an internship grant supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. H2020-ICT-2015-687689

On-the-Fly Adaptive Routing for dragonfly interconnection networks

Adaptive deadlock-free routing mechanisms are required to handle variable traffic patterns in dragonfly networks. However, distance-based deadlock avoidance mechanisms typically employed in Dragonflies increase the router cost and complexity as a function of the maximum allowed path length. This paper presents on-the-fly adaptive routing (OFAR), a routing/flow-control scheme that decouples the routing and the deadlock avoidance mechanisms. OFAR allows for in-transit adaptive routing with local and global misrouting, without imposing dependencies between virtual channels, and relying on a deadlock-free escape subnetwork to avoid deadlock. This model lowers latency, increases throughput, and adapts faster to transient traffic than previously proposed mechanisms. The low capacity of the escape subnetwork makes it prone to congestion. A simple congestion management mechanism based on injection restriction is considered to avoid such issues. Finally, reliability is considered by introducing mechanisms to find multiple edge-disjoint Hamiltonian rings embedded on the dragonfly, allowing to use multiple escape subnetworks