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Griffin Update: Towards an Agile, Predictive Infrastructure Anthony D. Joseph UC Berkeley http://www.cs.berkeley.edu/~adj/ Sahara Retreat, January 2003 Outline Griffin – – – Tapas Update – – – – 2 Motivation Goals Components Motivation Data preconditioning-based network modeling Model accuracy issues and validation Domain analysis Near-Continuous, Highly-Variable Internet Connectivity Connectivity everywhere: campus, in-building, satellite… – Most applications support limited variability (1% to 2x) – – – – – – Design environment for legacy apps is static desktop LAN Strong abstraction boundaries (APIs) hide the # of RPCs But, today’s apps see a wider range of variability – 3 Projects: Sahara (01-), Iceberg (98-01), Rover (95-97) 35 orders of magnitude of bandwidth from 10's Kb/s 1 Gb/s 46 orders of magnitude of latency from 1 sec 1,000's ms 59 orders of magnitude of loss rates from 10-3 10-12 BER Neither best-effort or unbounded retransmission may be ideal Also, overloaded servers / limited resources on mobile devices Result: Poor/variable performance from legacy apps Griffin Goals Users always see excellent ( local, lightly loaded) application behavior and performance – – – Help legacy applications handle changing conditions – – – 4 Analyze, classify, and predict behavior Pre-stage dynamic/static code/data (activate on demand) Architecture for developing new applications – Independent of the current infrastructure conditions Move away from “reactive to change” model Agility: key metric is time to react and adapt Input/control mechanisms for new applications Application developer tools Leverage Sahara policies and control mechanisms Griffin: An Adaptive, Predictive Approach Continuous, cross-layer, multi-timescale introspection – – – Convey app reqs/network info to/from lower-levels – – Break abstraction boundaries in a controlled way Challenge: Extensible interfaces to avoid existing least common denominator problems Overlay more powerful network model on top of IP – – 5 Collect & cluster link, network, and application protocol events Broader-scale: Correlate AND communicate short-/long-term events and effects at multiple levels (breaks abstractions) Challenge: Building accurate models of correlated events – Avoid standardization delays/inertia Enables dynamic service placement Challenge: Efficient interoperation with IP routing policies Some Enabling Infrastructure Components Tapas network characteristics toolkit – – – REAP protocol modifying / application building toolkit – – – Introspective mobile code/data support for legacy / new apps Provides dynamic placement of data and service components MINO E-mail application on OceanStore / Planet Lab Brocade, Mobile Tapestry, and Fault-Tolerant Tapestry – 6 Measuring/modeling/emulating/predicting delay, loss, … Provides Sahara with micro-scale network weather information Mechanism for monitoring/predicting available QoS – Overlay routing layer providing Sahara with efficient application-level object location and routing Mobility support, fault-tolerance, varying delivery semantics Outline Griffin – – – Tapas Update – – – – 7 Motivation Goals Components Motivation Data preconditioning-based network modeling Model accuracy issues and validation Domain analysis Tapas Motivation Accurate modeling and emulation for protocol design – – – Creating models/artificial traces that are statistically indistinguishable from traces from real networks – – – 8 Very difficult to gain access to new or experimental networks Delay, error, congestion in IP, GSM, GPRS, 1xRTT, 802.11a/b Study interactions between protocols at different levels Such models have both predictive and descriptive power Better understanding of network characteristics Can be used to optimize new and existing protocols Tapas Novel data preconditioning-based analysis approach – More accurately models/emulates long-/short-term dependence effects than classic approaches (Gilbert, Markov, HMM, Bernoulli) Analysis, simulation, modeling, prediction tools: – – MultiTracer: Multi-layer trace collection and analysis (download) Trace analysis and synthetic trace generator tools – – – 9 Markov-based Trace Analysis, Modified hidden Markov Model WSim: Wireless link simulator (currently trace-driven) Simple feedback algorithm and API Domain analysis tool: chooses most accurate model for a metric Error-tolerant radio / link layer protocols: RLPLite, PPPLite Collected >5,000 minutes of TCP, UDP, RLP traces in good/bad, stationary/mobile environments (download) MultiTracer Measurement Testbed Application Packetization RTP Socket Interface TCP/UDP (Lite) Multi-layer trace collection • RLP, UDP/TCP, App • Easy trace collection • Rapid, graphical analysis Packetization RTP Socket Interface TCP/UDP (Lite) IP IP PPP/PPP Lite PPP/PPP Lite RLP / non RLP RLP / non RLP GSM Network Mobile Host Unix BSDi 3.0 10 Application SocketDUMP TCPdump TCPstats RLPDUMP PSTN Fixed Host Unix BSDi 3.0 GSM Base Station MultiTracer 300 B/s Plotting & Analysis SocketDUMP TCPdump TCPstats Choosing the Right Network Model Collect empirical packet trace: T = {1,0}* – 1: corrupted/delayed packet, 0: correct/non-delayed packet Create mathematical models based on T real network metric trace artificial network metric trace Classic models don’t always work well (can’t capture variations) MTA, M3 – Trace data preconditioning algorithms – – – 11 network model T may be non-stationary (statistics vary over time) – trace analysis algorithm Decompose T into stationary sub-traces & model transitions Stationary sub-traces can be modeled with high-order DTMC Markov-based Trace Analysis (MTA) and Modified hidden Markov Model (M3) tools accurately model time varying links Creating Stationarity in Traces Our idea for MTA and M3: decompose T into stationary sub-traces – – Bad sub-traces B1..n = 1{1,0}*0c, Good sub-traces G1..n = 0* C is a change-of-state constant: mean + std dev of length of 1* MTA: Model B with a DTMC, model state lengths with exponential distribution, and compute transitions between states M3: Similar, but models multiple states using HMM to transition Bad Subtrace c Error Trace … 10001110011100….0 Bad Trace 12 Good Trace Bad Subtrace c Good Subtrace 0000…0000 11001100…00 … 10001110011100….0 11001100…00 ... Good Subtrace 00000..000... Model B with DTMC … 0000…0000 00000..000... Issues in Modeling Evaluating the accuracy of a particular model – How much trace data do we need to collect to accurately model a network characteristic? – How much work? Can a model be used to accurately model a network scenario? – 13 How closely does it model a network characteristic? I.e., can we model a case like poor fixed indoor coverage and use the model to model conditions at a later time? Evaluating Model Accuracy Determine CDFs of burst lengths in Lossy and Error Free subtraces of a collected trace Create Model – 14 Use model to generate an artificial trace and determine CDFs of Lossy and Error Free subtraces Calculate correlation coefficient (cc) between Lossy and Error-Free CDFs of collected and artificial traces Observation: Accurate models have cc > 0.96 Model Evaluation Methodology What size collected trace is needed for accurate model? – – – How representative is a model? – – – 15 Sub-divide trace into subtraces of length len/2j Compare cc values between subtraces and collected trace Trace lengths > max(EF burst size) yield cc > 0.96 – Collect large trace, AB, and sub-divide into A and B Create model from A Use cc to compare model A with A, B, and AB Representative models have all cc values > 0.96 Challenge: Domain Analysis Which model to use? – Algorithm (applied to Gilbert, HMM, MTA, M3): – – Collect traces, compute exponential functions for lengths of good and bad state and compute 1’s density of bad state For a given density, determine model parameters and optimal model (best cc) Experiment: – – 16 Gilbert, HMM, MTA, M3 have different properties Apply to artificial network environment with varying bad state densities Plot optimal model as a function of the good and bad state exponential values: Domain of Applicability Plot Domain of Applicability Plot, Lden= 0.2 b 17 Domain of Applicability Plot , Lden= 0.7 18 Griffin Summary On-going Tapas work: – – – – Tapestry and MINO talks at retreat – 19 Sigmetrics 2003 submission on domain analysis Trace collection: CDMA 1xRTT, GPRS, & IEEE 802.11a, PlanetLab IP Release of WSim Dissertation (Almudena Konrad) In joint and ROC/OS sessions Griffin Update: Towards an Agile, Predictive Infrastructure Anthony D. Joseph UC Berkeley http://www.cs.berkeley.edu/~adj/ Sahara Retreat, January 2003