DoE SciDAC high-performance networking research project: INCITE INCITE.rice.edu 2004 Technical Challenges INCITE R. Baraniuk, E. Knightly, R. Nowak, R. Riedi (Rice), L. Cottrell, J. Navratil (SLAC), W. Feng, M. Gardner (LANL) INCITE: InterNet Control and Inference Tools at the Edge Impact and Connections Edge-based Traffic Processing and Service Inference for High-Performance Networks 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  Objectives:  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, +)  Approach:  Active and passive network probing  Statistical model based inference PingER/ABwE (SLAC) 8 Many scientists are unable to participate in science due to poor Internet connectivity e.g % of HENP collaborators are from developing nations To understand need simple, low cost, performance measurements to and within developing regions providing: The graphs show Abing monitoring data via MonALISA Bandwidth Tools: MAGNeT & TICKET (LANL)  MAGNeT:  Monitor for Application-Generated Network Traffic 9  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 Network Tomography (Rice, Wisconsin) 4 Chirp: packet train with increasing rate When probe rate exceeds available bandwidth, queuing delay increases  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? planning, setting expectations, policy setting PingER meets these needs < 100bits/s, uses ubiquitous ping covers > 100 countries (>90% of world’s Internet connected population) Pinger deployment Blue=monitoring site Red=remote site 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 pathChirp: Efficient Available Bandwidth and Tight Link Estimation (Rice) 5 Available bandwidth estimates decrease in proportion to the introduced cross-traffic Canonical Subproblems: Two senders/receivers problem characterizes network tomography problem in general 1-by-2 Component 2-by-1 Component ? From edge-based traffic measurements (loss/delay/arrival order), infer internal topology, link level loss rates, queuing delays 1 1 11 22 33 44 55 66 11 22 33 44 Common Branch Point: Arrival order usually the same Different Branch Points: arrival order varies depending on delays, offset Arrival order fixed at joining point ROC Curve 1000 probes Loss Only Arrival Order Only Arrival Order and Loss Rice LAN Arrival Order Based Topology ID  Impact:  Optimize performance of demanding applications (remote visualization, high- capacity 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 UIUC  Rice tight link SLAC  Rice tight link Reduce available bandwidth on Gigabit testbed using cross-traffic generator Locating tight links on two paths sharing 4 common links TCP Low-Priority (Rice) 6 Goal: Utilize excessive bandwidth in a non-intrusive fashion Applications: bulk data transfer, P2P file sharing TCP alone Kb/s TCP plus Kb/s TCP-LP Kb/ TCP-LP is invisible to TCP High-speed TCP-LP TCP-LP + HSTCP [Floyd03] Linux web100 implementation Alpha-Beta Traffic Model (Rice) 7 Mean 99% = + beta alpha bytes per time plots 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 Key: both application and network properties important for traffic modeling