Network throughput

Summary

Network throughput (or just throughput, when in context) refers to the rate of message delivery over a communication channel, such as Ethernet or packet radio, in a communication network. The data that these messages contain may be delivered over physical or logical links, or through network nodes. Throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second (p/s or pps) or data packets per time slot. The system throughput or aggregate throughput is the sum of the data rates that are delivered to all terminals in a network. Throughput is essentially synonymous to digital bandwidth consumption; it can be determined numerically by applying the queueing theory, where the load in packets per time unit is denoted as the arrival rate (λ), and the drop in packets per unit time is denoted as the departure rate (μ). The throughput of a communication system may be affected by various factors, including the limitations of t

Official source

https://en.wikipedia.org/wiki/Network_throughput

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Throughput Analysis of Large Networks

Serj Haddad

While wired infrastructure constitutes the backbone of most wireless networks, wireless systems appeal the most to the dynamic and rapidly evolving requirements of today's communication systems because of their ease of deployment and mobility, not to mention the high cost of building a wired infrastructure. This led to an increased interest in the so called wireless ad hoc networks formed of a group of users, known as nodes, capable of communicating with each other through a shared wireless channel. Needless to say, these nodes are asked to use the shared wireless medium in the most efficient fashion, which is not an easy task given the absence of wired backbone. This requires a profound understanding of the wireless medium to establish a decentralized cooperation scheme, if needed, that best utilizes the resources available in the wireless channel. A significant part of this thesis focuses on the properties of the shared wireless channel, whereby we are interested in studying the spatial diversity and the beamforming capabilities in large wireless networks which are crucial in analyzing the throughput of ad hoc networks. In this thesis, we mainly focus on the problem of broadcasting information in the most efficient manner in a large two-dimensional ad hoc wireless network at low SNR and under line-of-sight propagation. A new communication scheme, which we call multi-stage back-and-forth beamforming, is proposed, where source nodes first broadcast their data to the entire network, despite the lack of sufficient available power. The signal's power is then reinforced via successive back-and-forth beamforming transmissions between different groups of nodes in the network, so that all nodes are able to decode the transmitted information at the end. This scheme is shown to achieve asymptotically the broadcast capacity of the network, which is expressed in terms of the largest singular value of the matrix of fading coefficients between the nodes in the network. A detailed mathematical analysis is then presented to evaluate the asymptotic behavior of this largest singular value. We further characterize the maximum achievable broadcast rate under different sparsity regimes. Our result shows that this rate depends negatively on the sparsity of the network. This is to be put in contrast with the number of degrees of freedom available in the network, which have been shown previously to increase with the sparsity of the network. In this context, we further characterize the degrees of freedom versus beamforming gain tradeoff, which reveals that high beamforming gains can only be obtained at the expense of reduced spatial degrees of freedom. Another important factor that impacts the throughput in wireless networks is the transmit/receive capability of the transceiver at the nodes. Traditionally, wireless radios are half-duplex. However, building on self-interference cancellation techniques, full-duplex radios have emerged as a viable paradigm over the recent years. In the last part of this thesis, we ask the fundamental question: how much can full-duplex help? Intuitively, one may expect that full-duplex radios can at most double the capacity of wireless networks, since they enable nodes to transmit and receive at the same time. However, we show that the capacity gain can indeed be larger than a factor of 2; in particular, we construct a specific instance of a wireless network where the the full-duplex capacity is triple the half-duplex capacity.

EPFL2017

R3P2: a Replicated Request-Response Pair Protocol

Antoine Albertelli

Consensus protocol have seen increased usage in recent years due to the industry shift to distributed computing. However, it has traditionally been implemented in the application layer. We propose to move the consensus protocol in the transport layer, to offer reliable ordered delivery of messages to applications. Our implementation builds on R2P2, a novel RPC framework for datacenter applications. It can run both using Linux UDP sockets or kernel bypass using DPDK. We observe tail latency increase of only 10 μs and a throughput loss of only 13% compared to the unreplicated application, showing that easy-to-use consensus can also have high performance.

2019

Global-scale online services, such as Google’s Web search and Facebook’s social networking, run in large-scale datacenters. Due to their massive scale, these services are designed to scale out (or distribute) their respective loads and datasets across thousands of servers in datacenters. The growing demand for online services forced service providers to build networks of datacenters, which require an enormous capital outlay for infrastructure, hardware, and power consumption. Consequently, efficiency has become a major concern in the design and operation of such datacenters, with processor efficiency being of, utmost importance, due to the significant contribution of processors to the overall datacenter performance and cost. Scale-out workloads, which are behind today’s online services, serve independent requests, and have large instruction footprints and little data locality. As such, they benefit from processor designs that feature many cores and a modestly sized Last-Level Cache (LLC), a fast access path to the LLC, and high-bandwidth interfaces to memory. Existing server-class processors with large LLCs and a handful of aggressive out-of-order cores are inefficient in executing scale-out workloads. Moreover, the scaling trajectory for these processors leads to even lower efficiency in future technology nodes. This thesis presents a family of throughput-optimal processors, called Scale-Out Processors, for the efficient execution of scale-out workloads. A unique feature of Scale-Out Processors is that they consist of multiple stand-alone modules, called pods, wherein each module is a server running an operating system and a full software stack. To design a throughput-optimal processor, we developed a methodology based on performance density, defined as throughput per unit area, to quantify how effectively an architecture uses the silicon real estate. The proposed methodology derives a performance-density optimal processor building block (i.e., pod), which tightly couples a number of cores to a small LLC via a fast interconnect. Scale-Out Processors simply consist of multiple pods with no inter-pod connectivity or coherence. Moreover, they deliver the highest throughput in today’s technology and afford near-ideal scalability as process technology advances. We demonstrate that Scale-Out Processors improve datacenters’ efficiency by 4.4x-7.1x over datacenters designed using existing server-class processors.

EPFL2013

Throughput Analysis of Large Networks

Serj Haddad

EPFL2017