Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
INCITE R. Baraniuk, E. Knightly, R. Nowak, R. Riedi (Rice), L. Cottrell, J. Navratil (SLAC), W. Feng, M. Gardner (LANL) Edge-based Traffic Processing and Service Inference for High-Performance Networks INCITE: InterNet Control and Inference Tools at the Edge 1 4 Network Tomography (Rice, Wisconsin) 1 1-by-2 Component 2-by-1 Component q4 q1 q2 Arrival order fixed at joining point q4 q1 q3 q2 q5 • Poor understanding of origins of complex network dynamics • Lack of adequate modeling techniques for network dynamics • Internal network inaccessible • Low impact, large scale monitoring • Application-driven traffic modulation • High-speed measurements 3 Mean + PingER/ABwE (SLAC) 8 q6 q3 Common Branch Different Branch Point: Arrival order Points: arrival order usually the same varies depending on delays, offset participate in science due to poor Internet connectivity •e.g. 10-20% of HENP collaborators are from developing nations • To understand need simple, low cost, performance measurements to and within developing regions providing: • planning, setting expectations, policy setting • PingER meets these needs • < 100bits/s, uses ubiquitous ping • covers > 100 countries (>90% of world’s Internet connected population) Rice LAN Pinger deployment Blue=monitoring site Red=remote site Arrival Order and Loss Arrival Order Only Loss Only 1000 probes 5 = • Key: both application and network properties important for traffic modeling ROC Curve Technical Challenges 2 99% • Many scientists are unable to Arrival Order Based Topology ID Approach: Active and passive network probing Statistical model based inference beta alpha bytes per time plots 1 Improve throughput over the Internet for DoE high performance projects Thrust 1: Traffic analysis and modeling Thrust 2: Path and tomographic inference Thrust 3: Data collection tools (PingER, MAGNeT, +) • Cause of burstiness in traffic? • Alpha: cause bursts, large transfers, high rate, low RTT, few connections • Beta: not-bursty, low rate, high RTT, most connections, possess long-range-dependence Two senders/receivers problem characterizes network tomography problem in general From edge-based traffic measurements (loss/delay/arrival order), infer internal topology, link level loss rates, queuing delays Objectives: 7 Canonical Subproblems: ? Alpha-Beta Traffic Model (Rice) pathChirp: Efficient Available Bandwidth and Tight Link Estimation (Rice) Chirp: packet train with increasing rate When probe rate exceeds available bandwidth, queuing delay increases ABwE tool: abing Characteristics • Interactive (1 – 2 second response) • Low network impact (20 packets/host/direction) • Simple & robust: just need simple responder installing • Provides measurements in both directions • Provides capacity & available bandwidth • Agrees with more intense/complex methods • Used in MonALISA, IEPM-BW & PlanetLab Bandwidth Impact and Connections Impact: Optimize performance of demanding applications (remote visualization, highcapacity data transfers) New understanding of the complex dynamics of large-scale, high-speed networks New edge-based tools to characterize and map network performance as a function of space, time, resource, application, protocol, and service Highly efficient methods for monitoring in distributed computing systems. Connections: Rice/SLAC/LANL synergy • Particle Physics Data Grid Collaboratory Pilot (Newman, Cottrell, Mount). • SciDAC Center for Supernova Research (Warren) • Scientific Workspaces of the Future (ANL, UIC, LANL, BU, Brown, NCSA). Globus • • • • • • • • Teragrid Transpac at Indiana U. European GridLab Project San Diego Supercomputing Center Telcordia IEPM-BW Internet2 ns-2 Simulator Reduce available bandwidth on Gigabit testbed using cross-traffic generator Locating tight links on two paths sharing 4 common links Available bandwidth estimates decrease in proportion to the introduced cross-traffic UIUCRice tight link SLACRice tight link TCP Low-Priority (Rice) 6 Goal: Utilize excessive bandwidth in a non-intrusive fashion Applications: bulk data transfer, P2P file sharing High-speed TCP-LP •TCP-LP + HSTCP [Floyd03] •Linux-2.4.22-web100 implementation • TCP alone 745.5 Kb/s • TCP plus TCP-LP 739.5 Kb/s 109.5 Kb/ • TCP-LP is invisible to TCP DoE SciDAC high-performance networking research project: INCITE The graphs show Abing monitoring data 9 via MonALISA Tools: MAGNeT & TICKET (LANL) MAGNeT: Monitor for Application-Generated Network Traffic Monitor traffic immediately after being generated by the application throughout the protocol stack to see how traffic gets modulated. Is TCP/IP the obstacle to high performance? TICKET: Traffic Information-Collecting Kernel with Exact Timing Current solutions to network packet capture (e.g., tcpdump) are too slow or too expensive Monitor and record traffic at gigabit-per-second (Gb/s) speeds and nanosecond granularity INCITE.rice.edu 2004