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Introduction Jiří Navrátil SLAC Project Partners and Researchers INCITE: Edge-based Traffic Processing and Service Inference for High-Performance Networks Richard Baraniuk, Rice University; Les Cottrell, SLAC; Wu-chun Feng, LANL Rice University Richard Baraniuk, Edward Knightly, Robert Nowak, Rudolf Riedi Xin Wang, Yolanda Tsang, Shriram Sarvotham, Vinay Ribeiro Los Alamos National Lab (LANL) Wu-chun Feng, Mark Gardner, Eric Weigle Stanford Linear Accelerator Center (SLAC) Les Cottrell, Warren Matthews, Jiri Navratil Project Goals INCITE: Edge-based Traffic Processing and Service Inference for High-Performance Networks Richard Baraniuk, Rice University; Les Cottrell, SLAC; Wu-chun Feng, LANL • Objectives – scalable, edge-based tools for on-line network analysis, modeling, and measurement • Based on – advanced mathematical theory and methods • Designeted for – support high-performance computing infrastructures, such as computational grids, – ESNET, Internet2 and other HPNetworking project Project Elements INCITE: Edge-based Traffic Processing and Service Inference for High-Performance Networks Richard Baraniuk, Rice University; Les Cottrell, SLAC; Wu-chun Feng, LANL • Advanced techniques – from networking, supercomputing, statistical signal processing, applied mathematics • Multiscale analysis and modeling – understand causes of burstiness in network traffic – realistic, yet analytically tractable, statistically robust, and computationally efficient modeling • On-line inference algorithms – characterize and map network performance as a function of space, time, application, and protocol • Data collection tools and validation experiments Scheduled Accomplishments INCITE: Edge-based Traffic Processing and Service Inference for High-Performance Networks Richard Baraniuk, Rice University; Les Cottrell, SLAC; Wu-chun Feng, LANL • Multiscale traffic models and analysis techniques – based on multifractals, cascades, wavelets – study how large flows interact and cause bursts – study adverse modulation of application-level traffic by TCP/IP • Inference algorithms for paths, links, and routers – multiscale end-to-end path modeling and probing – network tomography (active and passive) • Data collection tools – add multiscale path, link inference to PingER suite – integrate into ESnet NIMI infrastructure – MAGNeT – Monitor for Application-Generated Network Traffic – TICKET – Traffic Information-Collecting Kernel with Exact Timing Future Research Plans INCITE: Edge-based Traffic Processing and Service Inference for High-Performance Networks Richard Baraniuk, Rice University; Les Cottrell, SLAC; Wu-chun Feng, LANL New, high-performance traffic models – guide R&D of next-generation protocols • Application-generated network traffic repository – enable grid and network researchers to test and evaluate new protocols with actual traffic demands of applications rather than modulated demands • Multiclass service inference – enable network clients to assess a system's multi-class mechanisms and parameters using only passive, external observations • Predictable QoS via end-point control – ensure minimum QoS levels to traffic flows – exploit path and link inferences in real-time end-point admission control (From Papers to Practice) MWFS, TOMO, TOPO 20 ms ~300 ms 40 T for new set of values (12 sec) First results What has been done • Phase 1 - Remodeling - Code separation (BW and CT) - Find how to call MATLAB from another program - Analyze Results and data - Find optimal params for model • Phase 2 - Webing of BW estimate Data Dispersions from sunstats.cern.ch ccnsn07.in2p3.fr sunstats.cern.c h pcgiga.cern.ch plato.cacr.caltech.edu pcgiga.cern.ch default WS BW ~ 70Mbps pcgiga.cern.ch WS 512K BW ~ 100 Mbps Reaction to the network problems After tuning MF-CT Features and benefits • No need access to routers ! – Current monitoring systems for Load of traffic are based on SNMP or Flows (needs access to routers) • Low cost: – Allows permanent monitoring (20 pkts/sec ~ overhead 10 Kbytes/sec) – Can be used as data provider for ABW prediction (ABW=BW-CT) • Weak point for common use MATLAB code Future work on CT • Verification model – Define and setup verification model (S+R) – Measurements (S) – Analyze results (S+R) • On-line running on selected sites – Prepare code for automation and Webing (S) – CT-Code modificaton ? (R) UDP echo SNMP counter CERN SNMP counter SNMP counter SLAC SNMP counter IN2P3 MF-CT Simulator UDP echo CT RE-ENGINEERING For practical monitoring would be necessary to do modification for using it in different modes: – Continuos mode for monitoring one site in Large time scale (hours) – Accumulation mode (1 min, 5 min, ?) for running for more sites in parallel – ? Solution without MATLAB ? Rob Nowak (and CAIDA people) say: www.caida.org Network Topology Identification Ratnasamy & McCanne (99) Duffield, et al (00,01,02) Bestavros, et al (01) Coates, et al (01) Pairwise delay measurements reveal topology Network Tomography source router / node link receivers Measure end-to-end (from source to receiver) losses/delays Infer link-level (at internal routers) loss rates and delay distributions Unicast Network Tomography Measure end-to-end losses of packets ‘0’ loss ‘1’ success ‘0’ loss ‘1’ success Cannot isolate where losses occur ! Packet Pair Measurements packet (2) packet (1) cross-traffic packet (2) packet (1) delay measurement packet pair (1) (2) packet and packet experience nearly identical losses and/or delays Delay Estimation Measure end-to-end delays of packet-pairs Packets experience the same delay on link 1 d 2 dmin 0 d 3 d min Extra delay on link 3 Packet-pair measurements Key Assumptions: packet (2) (n) packet (1) (n) • fixed routes • iid pair-measurements • losses & delays on each link are mutually independent y ( 2 ) ( n) 0 "loss " or 1 "success " y ( p ) ( n) 0,1,...,K "delay units" p{1, 2} y (1) (n) • packet-pair losses & delays on shared links are nearly identical y y (n), y (n) (1) ( 2) N n 1 record occurrences of losses and delays ns Simulation • 40-byte packet-pair probes every 50 ms • competing traffic comprised of: on-off exponential (500 byte packets) TCP connections (1000 byte packets) 2 10 1 10 0.5 10 2 cross-traffic link 9 5 Kbytes/s 1 time (s) 2 0.5 Test network showing link bandwidths (Mb/s) Future work on TM and TP • Model in frame of Internet (~100 sites) – – – – – Define verification model (S+R) Deploy and install code on sites (S) First measurements (S+R) Analyze results (form,speed,quantity) (S+R) ? Code modificaton (R) • Production model ? – Compete with Pinger, RIPE, Surveyor, Nimi ? – How to unify VIRTUAL structure with Real