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The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing, Rich Wolski, Neil Spring, and Jim Hayes, Journal.

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Presentation on theme: "The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing, Rich Wolski, Neil Spring, and Jim Hayes, Journal."— Presentation transcript:

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

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 these resources 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

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21 4. Performance Monitoring: NWS Sensors

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23 CPU Sensor

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25 Network Sensor

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28 Packet loss rate (%) vvv vvvvvvvvvvvvvvv [A LINE:] 15% 111 *************** ^^^ ^ ^ ^ Avg latency (ms) min avg max latency

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33 5. Forecasting

34  Promotes extensibility

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45 “Future” developments…

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48 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 \ Take a look at including a bit of Jenny Schopf’s stuff on prognostication. o


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