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Network Weather Service

Network Weather Service. Sathish Vadhiyar. Sources / Credits: NWS web site: http://nws.cs.ucsb.edu NWS papers. Introduction. “NWS provides accurate forecasts of dynamically changing performance characteristics from a distributed set of metacomputing resources”

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Network Weather Service

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  1. Network Weather Service Sathish Vadhiyar • Sources / Credits: • NWS web site: http://nws.cs.ucsb.edu • NWS papers

  2. Introduction • “NWS provides accurate forecasts of dynamically changing performance characteristics from a distributed set of metacomputing resources” • What will be the future load (not current load) when a program is executed? • Producing short-term performance forecasts based on historical performance measurements • The forecasts can be used by dynamic scheduling agents

  3. Introduction • Resource allocation and scheduling decisions must be based on predictions of resource performance during a timeframe • NWS takes periodic measurements of performance and using numerical models, forecasts resource performance

  4. NWS Goals • Components • Persistent state • Name server • Sensors • Passive (CPU availability) • Active (Network measurements) • Forecaster

  5. Architecture

  6. Architecture

  7. Performance measurements • Using sensors • CPU sensors • Measures CPU availability • Uses • uptime • vmstat • Active probes • Network sensors • Measures latency and bandwidth • Each host maintains • Current data • One-step ahead predictions • Time series of data

  8. Network Measurements

  9. Issues with Network Sensors • Appropriate transfer size for measuring throughput • Collision of network probes • Solutions • Tokens and hierarchical trees with cliques

  10. Available CPU measurement

  11. Available CPU measurement • The formulae shown does not take into account job priorities • Hence periodically an active probe is run to adjust the estimates

  12. Predictions • To generate a forecast, forecaster requests persistent state data • When a forecast is requested, forecaster makes predictions for existing measurements using different forecast models • Dynamic choice of forecast models based on the best Mean Absolute Error, Mean Square Prediction Error, Mean Percentage Prediction Error • Forecasts requested by: • InitForecaster() • RequestForecasts() • Forecasting methods • Mean-based • Median based • Autoregressive

  13. Forecasting Methods Notations: Prediction Accuracy: Mean Absolute Error (MAE) is the average of the above Prediction Method:

  14. Forecasting Methods – Mean-based 1. 2. 3.

  15. Forecasting Methods – Mean-based 4. 5.

  16. Forecasting Methods – Median-based 1. 2. 3.

  17. Autoregression 1. ai found such that it minimizes the overall error. ri ,j is the autocorellation function for the series of N measurements.

  18. Forecasting Methodology

  19. Forecast Results

  20. Forecasting Complexity vs Accuracy • Semi Non-parametric Time Series Analysis (SNP) – an accurate but complicated model • Model fit using iterative search • Calculation of conditional expected value using conditional probability density

  21. Sensor Control • Each sensor connects to other sensors and perform measurements O(N2) • To reduce the time complexity, sensors organized in hierarchy called cliques • To avoid collisions, tokens are used • Adaptive control using adaptive token timeouts • Adaptive time-out discovery and distributed leader election protocol

  22. Synchronizing network probes • Consistent periodicity and mutual exclusion • Token • List of hosts to probe • Periodicity of probe • Parameters to the probe • Sequence number • Leader initiates the token • A hosts after receiving a token: • Conducts probes with the other hosts in the token • Passes the token to the next host • Token passed back to the leader

  23. Contd… • Leader notes the token circuit time and calculates the next token initiation time as (desired periodicity – token circuit time) • To avoid long delays in token circulation and to have fault tolerance: • Each host maintains a timer • When the timer times out, the host declares itself as the leader and initiates a new token • When a host encounters two tokens, the old token is destroyed • Calculation of time-outs • Each host records token circuit time, variance of the time • Uses NWS forecasting models to predict the next token arrival time

  24. New Protocol • Compromise between periodicity and mutual exclusion • NWS administrator specifies periodicity, and an upper range of desired periodicity • If network conditions are stable and if tokens are received within the upper range, then mutual exclusion is guaranteed • If not, hosts times out and start conducting probes with possible collisions • Thus the protocol switches between good and bad phases

  25. Illustration

  26. Comparison of 2 protocols – Experimental setup • 4 machines – 2 in Lyon, France and 2 in Tennessee, USA • 240 second periodicity • 5 second range

  27. Comparison - Periodicity

  28. Comparison – Mutual exclusion

  29. Use of NWS: Scheduling a Jacobi application The problem: Appropriate partitioning strategy to balance processor efficiencies and communication overheads, i.e. deriving partitions to obtain resource performance

  30. Deriving Partitions for Jacobi • Notations • Per-processor execution time • The goal

  31. Deriving Partitions for Jacobi • Communication time • Soultion: system of linear equations by Gaussian Elimination

  32. NWS in Jacobi

  33. Resource Selection and Scheduling

  34. Resource Selection and Scheduling

  35. References • Implementing a Performance Forecasting System for Metacomputing: The Network Weather Service. Rich Wolski, Neil Spring, Chris Peterson, in Proceedings of SC97, November, 1997. • Dynamically Forecasting Network Performance Using the Network Weather Service. Rich Wolski, in Journal of Cluster Computing, Volume 1, pp. 119-132, January, 1998. • The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. Rich Wolski, Neil Spring, and Jim Hayes, Journal of Future Generation Computing Systems,Volume 15, Numbers 5-6, pp. 757-768, October, 1999.

  36. References • Synchronizing Network Probes to avoid Measurement Intrusiveness with the Network Weather Service, B. Gaidioz, R. Wolski, and B. Tourancheau, Proceedings of 9th IEEE High-performance Distributed Computing Conference, August, 2000, pp. 147-154. • Experiences with Predicting Resource Performance On-line in Computational Grid Settings, Rich Wolski, ACM SIGMETRICS Performance Evaluation Review, Volume 30, Number 4, pp 41--49, March, 2003.

  37. Forecasting Methods Summary

  38. Prediction Accuracy

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