Pile de protocoles

Résumé

Une pile de protocoles est une mise en œuvre particulière d'un ensemble de protocoles de communication réseau. L'intitulé « pile » implique que chaque couche de protocole s'appuie sur celles qui sont en dessous afin d'y apporter un supplément de fonctionnalité. Il en existe de plusieurs : thumb|Position de la couche réseau dans le modèle OSI et dans TCP-IP • Le « modèle de référence OSI » — OSI signifiant « Open Systems Interconnection » soit en français « Interconnexion de systèmes ouverts » — défini par l'ISO décrit ainsi sept couches empilées les unes sur les autres. • Le « modèle Internet » se contente de cinq par suppression de la couche numéro 5 et agglomération des deux plus hautes couches. Voici une description très simplifiée de chacune (consulter l'article sur chaque couche de protocole pour plus d'information). Le modèle OSI Le modèle OSI peut être modélisé de la manière suivante : ; 1 • Physique : La couche physique définit la façon dont les « sym

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This thesis investigates the feasibility of porting Node.js, a JavaScript web application framework and server, to IX, a dataplane operating system specifically developed to meet the needs of high performance microsecond-computing type of applications in a datacentre setting. We show that porting requires extensions to the IX kernel to support UDS polling, which we implement. We develop a distributed load generator to benchmark the framework. The results show that running Node.js on IX improves throughput by up to 20.6%, latency by up to 5.23×, and tail latency by up to 5.68× compared to a Linux baseline. We show how server side request level reordering affect the latency distribution, predominantly in cases where the server is load saturated. Finally, due to various limitations of IX, we are unable at this time to recommend running Node.js on IX in a production environment, despite improved metrics in all test cases. However, the limitations are not fundamental, and could be resolved in future work.

2015

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Optimus Prime: Accelerating Data Transformation in Servers

Mario Paulo Drumond Lages De Oliveira, Babak Falsafi, Siddharth Gupta, Hussein Kassir, Christoph Koch, Arash Pourhabibi Zarandi, Mark Johnathon Sutherland, Zilu Tian

Modern online services are shifting away from monolithic applications to loosely-coupled microservices because of their improved scalability, reliability, programmability and development velocity. Microservices communicating over the datacenter network require data transformation (DT) to convert messages back and forth between their internal formats. This work identifies DT as a bottleneck due to reductions in latency of the surrounding system components, namely application runtimes, protocol stacks, and network hardware. We therefore propose Optimus Prime (OP), a programmable DT accelerator that uses a novel abstraction, an in-memory schema, to represent DT operations. The schema is compatible with today's DT frameworks and enables any compliant accelerator to perform the transformations comprising a request in parallel. Our evaluation shows that OP's DT throughput matches the line rate of today's NICs and has 60x higher throughput compared to software, at a tiny fraction of the CPU's silicon area and power. We also evaluate a set of microservices running on Thrift, and show up to 30% reduction in service latency.

ACM2020

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Automating the Testing of OpenFlow Applications

Marco Canini, Dejan Kostic, Jennifer Rexford

Software-defined networking, and the emergence of OpenFlow-capable switches, enables a wide range of new network functionality. However, enhanced programmability inevitably leads to more software faults (or bugs). We believe that tools for testing OpenFlow programs are critical to the success of the new technology. However, the way OpenFlow applications interact with the data plane raises several challenges. First, the space of possible inputs (e.g., packet headers and inter-packet timings) is huge. Second, the centralized controller has a indirect view of the traffic and experiences unavoidable delays in installing rules in the switches. Third, external factors like user behavior (e.g., mobility) and higher-layer protocols (e.g., the TCP state machine) affect the correctness of OpenFlow programs. In this work-in-progress paper, we extend techniques for symbolic execution to generate inputs that systematically explore the space of system executions. First, we analyze controller applications to identify equivalence classes of packets that exercise different parts of the code. Second, we propose several network models with increasing precision, ranging from simple traffic models to live testing on the target network. Initial experiences with our prototype, which symbolically executes OpenFlow applications written in Python, suggest that our techniques can help programmers identify bugs in their OpenFlow programs.

2011

Chargement