ADAPTIVE REMOTE DECISION MAKING UNDER QUALITY OF INFORMATION REQUIREMENTS

• US 20110119523A1
• Filed: 11/16/2009
• Published: 05/19/2011
• Est. Priority Date: 11/16/2009
• Status: Active Grant

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: He, Ting, Zafer, Murtaza, BISDIKIAN, CHATSCHIK
• Granted Patent: US 8,660,022 B2
• Time in Patent Office: Days
• US Class Current: 714/2
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/0829 Packet loss
• CPC Class Codes: H04L 43/103 with adaptive polling, i.e. dynamically adapting the polling rate
• CPC Class Codes: H04L 43/16 Threshold monitoring
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

A system and method for adaptive remote decision making includes steps of: receiving from an application layer a target range for a level of reporting quality for processed data; setting data collection parameters to meet the target range; collecting the data from a plurality of remote data collecting devices deployed in the distributed computing system, a portion of said data being compromised during the collecting process; processing the collected data to produce the processed data; evaluating the processed data based on observable metrics of current collected data and reported data losses; forecasting an expected reporting quality while continuing to collect the data; comparing the expected reporting quality with the target range; and reporting the processed data when the expected reporting quality falls within the target range for the level of reporting quality.

Claims

1. A method comprising steps of:

using an information fusing device as part of a distributed computing system for;

receiving from an application layer a target range for a level of reporting quality for processed data;

setting data collection parameters to meet the target range;

collecting the data from a plurality of remote data collecting devices deployed in the distributed computing system, a portion of said data being compromised during the collecting process;

processing the collected data to produce the processed data;

evaluating the processed data based on observable metrics of current collected data and reported data losses;

forecasting an expected reporting quality while continuing to collect data;

comparing the expected reporting quality with the target range; and

reporting the processed data when the expected reporting quality falls within the target range for the level of reporting quality.

2. The method of claim 1 further comprising a step of:

dynamically adjusting the collecting of the data when the estimated expected reporting quality falls below the target range.

3. The method of claim 1 further comprising a step of:

dynamically adjusting the processing of the collected data when the estimated reporting quality falls below the target range.

4. The method of claim 2 wherein the adjusting comprises at least one action selected from a group consisting of:

adjusting a timeframe for data collection, adjusting a rate of the data collection, adjusting a precision of the data collection, and shifting to a different data collecting process.

5. The method of claim 3 wherein the adjusting comprises at least one operation selected from a set of operations consisting of:

adjusting a size of a cluster of the remote data collecting devices for which data is aggregated, adjusting a granularity of the data by which information is aggregated, and shifting to a different aggregation operator.

6. The method of claim 2 wherein the adjusting step takes into account cost constraints.

7. The method of claim 6 wherein the adjusting step takes into account the cost constraint selected from a group consisting of:

a cost of communicating adjusted parameters to the remote information collecting devices, a cost of applying the adjustment at the remote information collecting devices, and a cost of reverting back to original parameters at a completion of an observation period.

8. The method of claim 6 wherein applying the adjustment comprises modifying settings of the remote information collecting devices and the cost constraint comprises costs incurred under the modified settings due to the adjustment.

9. The method of claim 2 wherein the adjusting can be performed offline in a simulation environment before said adjusting is applied to the information fusing device.

10. The method of claim 1 wherein the evaluating step comprises setting a timeframe for observing the metrics of the current collected data and the realized data losses.

11. The method of claim 1 wherein the forecasting step comprises:

generating a statistical model of the collected data.

12. A distributed computing system for remote adaptive decision making, the system comprising:

a plurality of remote data collecting devices; and

a fusion device operatively coupled with at least some of the plurality of the remote data collecting devices, said fusion device configured for executing steps of;

receiving from an application layer a target range for a level of reporting quality for processed data;

setting data collection parameters to meet the target range;

collecting the data from the plurality of remote data collecting devices deployed in the distributed computing system, a portion of said data being comprised during the collecting process;

processing the collected data to produce the processed data;

evaluating the processed data based on observable metrics of current collected data and reported data losses;

forecasting an expected reporting quality while continuing to collect data;

comparing the expected reporting quality with the target range; and

reporting the processed data when the expected reporting quality falls within the target range for the level of reporting quality.

13. The distributed computing system of claim 12 wherein the data is categorized into multiple classes, wherein each class has a different target range.

14. The distributed computing system of claim 12 wherein each class uses different data collecting devices and processes.

15. The distributed computing system of claim 14 wherein the remote data collecting devices are grouped in clusters.

16. The distributed computing system of claim 12 wherein the fusion device is further configured for:

dynamically adjusting the collecting of the data when the estimated expected reporting quality falls below the target range.

17. The distributed computing system of claim 12 wherein the fusion device is further configured for:

dynamically adjusting the processing of the collected data when the estimated reporting quality falls below the target range.

18. The distributed computing system of claim 12 further comprising the application layer.

19. A computer readable storage medium comprising program code that, when executed, enables a computing device to:

receive from an application layer a target range for a level of reporting quality for processed data;

set data collection parameters to meet the target range;

collect the data from a plurality of remote data collecting devices deployed in a distributed computing system, a portion of said data being compromised during the collecting process;

process the collected data to produce the processed data;

evaluate the processed data based on observable metrics of current collected data and reported data losses;

forecast an expected reporting quality while continuing to collect data;

compare the expected reporting quality with the target range; and

report the processed data when the expected reporting quality falls within the target range for the level of reporting quality.