Measuring and Understanding Throughput of Network Topologies

High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to compare worst-case throughput performance is a subtle problem. In this paper, we develop a framework to benchmark the throughput of network topologies, using a two-pronged approach. First, we study performance on a variety of synthetic and experimentally-measured traffic matrices (TMs). Second, we show how to measure worst-case throughput by generating a near-worst-case TM for any given topology. We apply the framework to study the performance of these TMs in a wide range of network topologies, revealing insights into the performance of topologies with scaling, robustness of performance across TMs, and the effect of scattered workload placement. Our evaluation code is freely available

Optimal Networks from Error Correcting Codes

To address growth challenges facing large Data Centers and supercomputing clusters a new construction is presented for scalable, high throughput, low latency networks. The resulting networks require 1.5-5 times fewer switches, 2-6 times fewer cables, have 1.2-2 times lower latency and correspondingly lower congestion and packet losses than the best present or proposed networks providing the same number of ports at the same total bisection. These advantage ratios increase with network size. The key new ingredient is the exact equivalence discovered between the problem of maximizing network bisection for large classes of practically interesting Cayley graphs and the problem of maximizing codeword distance for linear error correcting codes. Resulting translation recipe converts existent optimal error correcting codes into optimal throughput networks.Comment: 14 pages, accepted at ANCS 2013 conferenc

A Co-evolutionary Algorithm-based Enhanced Grey Wolf Optimizer for the Routing of Wireless Sensor Networks

Wireless networks are frequently installed in arduous environments, heightening the importance of their consistent operation. To achieve this, effective strategies must be implemented to extend the lifespan of nodes. Energy-conserving routing protocols have emerged as the most prevalent methodology, as they strive to elongate the network\u27s lifetime while guaranteeing reliable data routing with minimal latency. In this paper, a plethora of studies have been done with the purpose of improving network routing, such as the integration of clustering techniques, heterogeneity, and swarm intelligence-inspired approaches. A comparative investigation was conducted on a variety of swarm-based protocols, including a new coevolutionary binary grey wolf optimizer (Co-BGWO), a BGWO, a binary whale optimization, and a binary Salp swarm algorithm. The objective was to optimize cluster heads (CHs) positions and their number during the initial stage of both two-level and three-level heterogeneous networks. The study concluded that these newly developed protocols are more reliable, stable, and energy-efficient than the standard SEP and EDEEC heterogeneous protocols. Specifically, in 150 m2 area of interest, the Co-BGWO and BGWO protocols of two levels were found the most efficient, with over than 33% increase in remaining energy percentage compared to SEP, and over 24% more than EDEEC in three-level networks

Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA)

A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network

A wireless rechargeable sensor network (WRSN) enables charging of rechargeable sensor nodes (RSN) wirelessly through a mobile charging vehicle (MCV). Most existing works choose the MCV’s stop point (SP) at random, the cluster’s center, or the cluster head position, all without exploring the demand from RSNs. It results in a long charging delay, a low charging throughput, frequent MCV trips, and more dead nodes. To overcome these issues, this paper proposes a hybrid metaheuristic algorithm for stop point selection (HMA-SPS) that combines the techniques of the dragonfly algorithm (DA), firefly algorithm (FA), and gray wolf optimization (GWO) algorithms. Using FA and GWO techniques, DA predicts an ideal SP using the run-time metrics of RSNs, such as energy, delay, distance, and trust factors. The simulated results demonstrate faster convergence with low delay and highlight that more RSNs can be recharged with fewer MCV visits, further enhancing energy utilization, throughput, network lifetime, and trust factor

Reconfigurable Optically Interconnected Systems

With the immense growth of data consumption in today's data centers and high-performance computing systems driven by the constant influx of new applications, the network infrastructure supporting this demand is under increasing pressure to enable higher bandwidth, latency, and flexibility requirements. Optical interconnects, able to support high bandwidth wavelength division multiplexed signals with extreme energy efficiency, have become the basis for long-haul and metro-scale networks around the world, while photonic components are being rapidly integrated within rack and chip-scale systems. However, optical and photonic interconnects are not a direct replacement for electronic-based components. Rather, the integration of optical interconnects with electronic peripherals allows for unique functionalities that can improve the capacity, compute performance and flexibility of current state-of-the-art computing systems. This requires physical layer methodologies for their integration with electronic components, as well as system level control planes that incorporates the optical layer characteristics. This thesis explores various network architectures and the associated control plane, hardware infrastructure, and other supporting software modules needed to integrate silicon photonics and MEMS based optical switching into conventional datacom network systems ranging from intra-data center and high-performance computing systems to the metro-scale layer networks between data centers. In each of these systems, we demonstrate dynamic bandwidth steering and compute resource allocation capabilities to enable significant performance improvements. The key accomplishments of this thesis are as follows. In Part 1, we present high-performance computing network architectures that integrate silicon photonic switches for optical bandwidth steering, enabling multiple reconfigurable topologies that results in significant system performance improvements. As high-performance systems rely on increased parallelism by scaling up to greater numbers of processor nodes, communication between these nodes grows rapidly and the interconnection network becomes a bottleneck to the overall performance of the system. It has been observed that many scientific applications operating on high-performance computing systems cause highly skewed traffic over the network, congesting only a small percentage of the total available links while other links are underutilized. This mismatch of the traffic and the bandwidth allocation of the physical layer network presents the opportunity to optimize the bandwidth resource utilization of the system by using silicon photonic switches to perform bandwidth steering. This allows the individual processors to perform at their maximum compute potential and thereby improving the overall system performance. We show various testbeds that integrates both microring resonator and Mach-Zehnder based silicon photonic switches within Dragonfly and Fat-Tree topology networks built with conventional equipment, and demonstrate 30-60% reduction in execution time of real high-performance benchmark applications. Part 2 presents a flexible network architecture and control plane that enables autonomous bandwidth steering and IT resource provisioning capabilities between metro-scale geographically distributed data centers. It uses a software-defined control plane to autonomously provision both network and IT resources to support different quality of service requirements and optimizes resource utilization under dynamically changing load variations. By actively monitoring both the bandwidth utilization of the network and CPU or memory resources of the end hosts, the control plane autonomously provisions background or dynamic connections with different levels of quality of service using optical MEMS switching, as well as initializing live migrations of virtual machines to consolidate or distribute workload. Together these functionalities provide flexibility and maximize efficiency in processing and transferring data, and enables energy and cost savings by scaling down the system when resources are not needed. An experimental testbed of three data center nodes was built to demonstrate the feasibility of these capabilities. Part 3 presents Lightbridge, a communications platform specifically designed to provide a more seamless integration between processor nodes and an optically switched network. It addresses some of the crucial issues faced by the works presented in the previous chapters related to optical switching. When optical switches perform switching operations, they change the physical topology of the network, and they lack the capability to buffer packets, resulting in certain optical circuits being unavailable. This prompts the question of whether it is safe to transmit packets by end hosts at any given time. Lightbridge was developed to coordinate switching and routing of optical circuits across the network, by having the processors gain information about the current state of the optical network before transmitting packets, and being able to buffer packets when the optical circuit is not available. This part describes details of Lightbridge which is constituted by a loadable Linux kernel module along with other supporting modifications to the Linux kernel in order to achieve the necessary functionalities

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

FatPaths: Routing in Supercomputers and Data Centers when Shortest Paths Fall Short

We introduce FatPaths: a simple, generic, and robust routing architecture that enables state-of-the-art low-diameter topologies such as Slim Fly to achieve unprecedented performance. FatPaths targets Ethernet stacks in both HPC supercomputers as well as cloud data centers and clusters. FatPaths exposes and exploits the rich ("fat") diversity of both minimal and non-minimal paths for high-performance multi-pathing. Moreover, FatPaths uses a redesigned "purified" transport layer that removes virtually all TCP performance issues (e.g., the slow start), and incorporates flowlet switching, a technique used to prevent packet reordering in TCP networks, to enable very simple and effective load balancing. Our design enables recent low-diameter topologies to outperform powerful Clos designs, achieving 15% higher net throughput at 2x lower latency for comparable cost. FatPaths will significantly accelerate Ethernet clusters that form more than 50% of the Top500 list and it may become a standard routing scheme for modern topologies