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Introduction to the Network Weather Service

Introduction to the Network Weather Service. T.L. Thomas, University of New Mexico. Feb 3, 2003.

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Introduction to the Network Weather Service

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  1. Introduction to the Network Weather Service T.L. Thomas, University of New Mexico Feb 3, 2003 • 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.

  2. Monitoring, Prediction and Scheduling on the Grid Jennifer M. Schopf Argonne National Lab

  3. Scheduling and Prediction on the Grid • First step of Grid computing – basic functionality • Run my job • Transfer my data • Security • Next step – more efficient use of the resources • Scheduling • Prediction • Monitoring

  4. How can theseresources be used effectively? • Efficient scheduling • Selection of resources • Mapping of tasks to resources • Allocating data • Accurate prediction of performance • Good performance prediction modeling techniques

  5. Replica Selection • Why not use something like Network Weather Service (NWS) probes? • Wolski and Swany, UCSB • Logging and prediction • Small data transfers • CPU load, memory, etc.

  6. 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.

  7. Paper Outline: 1. Introduction 2. System Architecture (Figure 1) 3. Naming and State Management 4. Performance Monitoring 4.1 NWS Sensors 4.2 CPU Sensor (Figure 2) 4.3 Network Sensor 5. Forecasting 5.1 Example Forecasting Results (Figure 3) 5.2 Incorporating Additional Forecasting Techniques 6. Reporting Interface 6.1 C API 6.2 CGI Interface 7. Sensor Control (Figure 4) 7.1 Adaptive Time-Out Discovery 8. Related Work 9. Conclusions and Future Work References

  8. 4. Performance Monitoring: NWS Sensors

  9. CPU Sensor

  10. Network Sensor

  11. Packet loss rate (%) vvv vvvvvvvvvvvvvvv [A LINE:] 15% 111 *************** < | > ^^^ ^ ^ ^ Avg latency (ms) min avg max latency

  12. 5. Forecasting

  13.  Promotes extensibility

  14. “Future” developments…

  15. Outline: \ My history; my monitoring stuff; cf. previous / continuing HEP monitoring infrastructure. \ My old Coke / Intel comparison forecast o History / people of the NWS project o NWS is distributed as part of… o o \ Take a look at including a bit of Jenny Schopf’s stuff on prognostication. o o

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