Griffin Update: Toward an Agile, Predictive Infrastructure

1. Griffin Update: Toward an Agile, Predictive Infrastructure Anthony D. Joseph UC Berkeley http://www.cs.berkeley.edu/~adj/ Sahara Retreat, June 2003

2. Outline • Griffin • Motivation • Goals • Components • Tapas Update • Tapestry/Brocade Update • REAP/MINO Update

3. Near-Continuous, Highly-Variable Internet Connectivity • Connectivity everywhere: campus, in-building, satellite… • Projects: Sahara (01-), Iceberg (98-01), Rover (95-97) • 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5 orders of magnitude of bandwidth from 10's Kb/s 1 Gb/s • 46 orders of magnitude of latency from 1 sec 1,000's ms • 59 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

4. Griffin Goals • Users always see excellent ( local, lightly loaded) application behavior and performance • Independent of the current infrastructure conditions • Move away from “reactive to change” model • Agility: key metric is time to react and adapt • Help legacy applications handle changing conditions • Analyze, classify, and predict behavior • Pre-stage dynamic/static code/data (activate on demand) • Architecture for developing new applications • Input/control mechanisms for new applications • Application developer tools

5. Griffin: An Adaptive, Predictive Approach • Continuous, cross-layer, multi-timescale introspection • 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 • 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 • Avoid standardization delays/inertia • Enables dynamic service placement • Challenge: Efficient interoperation with IP routing policies

6. Some Enabling Infrastructure Components • Tapas network characteristics toolkit • Measuring/modeling/emulating/predicting delay, loss, … • Provides micro-scale network weather information • Mechanism for monitoring/predicting available QoS • 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 • Tapestry, Brocade, and Mobile Tapestry • Overlay routing layer providing efficient application-level object location and routing • Mobility support, fault-tolerance, varying delivery semantics

7. Outline • Griffin • Motivation • Goals • Components • Tapas Update • Tapestry/Brocade Update • REAP/MINO Update

8. Tapas • Accurate modeling and emulation for protocol design • 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 • Goal: Create models/artificial traces that are statistically indistinguishable from real network traces • 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 • More accurately models/emulates long-/short-term dependence effects than classic approaches (Gilbert, Markov, HMM, Bernoulli)

9. Tapas Update • Domain analysis tool • Chooses most accurate model for a metric • Markov-based Trace Analysis, Modified hidden Markov Model • New Tapas-based link simulator • Complete reimplementation of Wsim • Enables quick and repeatable testing of new apps • Tapas talk this afternoon

10. real network metric trace trace analysis algorithm network model artificial network metric trace Domain Analysis: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 • Challenge: domain analysis – which model to use? • Gilbert, HMM, MTA, M3 have different properties • 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 Correlation Coefficient) • Sigmetrics 2003 paper

11. Domain Analysis Experiment • Create artificial network environment with varying bad state densities (generate synthetic reference traces) • For each trace: • Create classical and data preconditioning models • Generate artificial traces from models • Plot error and error free distribution • Calculate Correlation Coefficient (CC) between distributions of reference and artificial traces • Optimal model for a given set of properties is the one with the highest CC value • Plot optimal model as a function of the good and bad state exponential values: Domain of Applicability Plot

12. Domain of Applicability Plot, Lden= 0.2

13. Domain of Applicability Plot , Lden= 0.7

14. Tapestry/Brocade • Starting point is Tapestry • Distributed Object Location and Routing (DOLR) overlay network • Extend Tapestry with unique, powerful routing functions • SLA-compatible efficient wide-area routing • Rapid, scalable mobility support • Rapid fault route-around using pre-computed backup routes • Monitoring, measurement, and analysis entry point

15. Tapestry/Brocade Update • Major push to improve Tapestry reliability • Pre-computed backup paths enable near- instantaneous fail-over (3 paths/router entry) • Improved Patchwork network link monitoring • Now ready for integration with link prediction support • Improved repair algorithms to handle long-term faults • Building new applications • SpamWatch (Middleware 2003 paper) • Summer focus on inter-domain Brocade routing

16. Improved Tapestry Fault-Tolerance

17. REAP/MINO • Introspective code / data migration in 3-tier hierarchies • Distributes server load, empowers limited devices • Provides illusion of high connectivity • Combines static trace analysis w/ dynamic monitoring of clients to predict appl’n / communication behavior • Identify and optimize code/data placement • Pre-stage statically/dynamically generated components • Explore various granularities of code & data migration • Predict costs using multiple criteria • MINO E-mail OceanStore application • Basis for exploring code/data migration choices

18. REAP/MINO Update • Code migration work is mostly complete, now focused on data migration • Understanding how users access data and how they move, so that we can better place/cache data • Collecting user mobility / data access traces • Web proxy traces from NLANR (access clustering) • EECS IMAP server traces (user location, access info) • College campus NFS-level traces from a login server (used for e-mail reading) • Built cooperative caching simulator • Exploring multi-criteria optimization: frequency of access, number of users accessing, $ paid/user, etc.. • Cache refill can leverage link predictor information

19. Recent Griffin Progress Summary • Tapas: Network modeling and analysis • Thesis: Almudena Konrad, “TAPAS: A Research Paradigm for the Modeling, Prediction and Analysis of Non-stationary Network Behavior,” (PhD, expected August 2003) • Simulator almost ready for release • Publications • Konrad, A.; Joseph, A. D., Choosing an Accurate Network Path Model, In Proc. Of SIGMETRICS 2003, June, 2003. • Konrad, A.; Zhao, B. Y.; Joseph, A. D.; Ludwig, R., A Markov-Based Channel Model Algorithm for Wireless Networks, ACM Wireless Networks, vol. 9, num. 3, May, 2003.

20. Recent Griffin Progress Summary • Tapastry / Brocade: • Robustness fixes to Tapestry, lots of measurements • Publications • Zhao, B. Y.; Huang, L.; Stribling, J.; Rhea, S. C.; Joseph, A. D.; Kubiatowicz J. D., Tapestry: A Resilient Global-scale Overlay for Service Deployment. To appear in IEEE JSAC, Fall 2003. • Zhou, F.; Zhuang, L.; Zhao, B.; Huang, L.; Joseph, A. D.; Kubiatowicz, J., Approximate Object Location and Spam Filtering on Peer-to-Peer Systems, In Proc.of ACM Middleware 2003, June, 2003. • REAP/MINO • Simulator developed, lots of traces collected • Beginning analysis phase

21. Griffin Update: Toward an Agile, Predictive Infrastructure Anthony D. Joseph UC Berkeley http://www.cs.berkeley.edu/~adj/ Sahara Retreat, June 2003