Isolating Local Performance Variation in Website Monitoring

• US 20110029662A1
• Filed: 10/12/2010
• Published: 02/03/2011
• Est. Priority Date: 06/01/2005
• Status: Active Grant

• Current Assignee: Vercara LLC
• Original Assignee: Neustar Incorporated
• Inventors: Drees, Timothy P., Taylor, Doug J., Rachitsky, Lenny
• Granted Patent: US 8,370,485 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet
• CPC Class Codes: G06F 2201/88 Monitoring involving counting

Abstract

Systems and methods are provided for isolating performance variation in website monitoring. A set of geographically diverse monitoring agents are identified and a subset of baseline agents is selected from these monitoring agents. The baseline agents are those agents that are geographically close to the target website or otherwise enjoy reliable communications with the target website and are therefore less affected by network latencies. The frequency at which a target website is monitored is weighted in favor of the baseline agents in order to get an accurate baseline metric for the target website while also obtaining information regarding global accessibility of the website from the geographically disperse monitoring agents. The variations in sample frequency can be optimized for each particular set of baseline agents. The set of baseline agents can be pre-assigned or dynamically identified based on an analysis of the performance variation from each of the monitoring agents.

Claims

1. A computer implemented method for isolating local performance variation in monitoring a target website from a monitoring server that is communicatively coupled with the target website via a data communication network, comprising:

selecting a set of baseline agents where each baseline agent is in a first proximity zone that is closest to the target website;

selecting a set of monitor agents where each monitor agent is in a second proximity zone; and

monitoring the target website in a plurality of monitoring rounds, wherein each monitoring round comprises monitoring the target website from each baseline agent and from at least one monitor agent.

2. The method of claim 1, wherein the first and second proximity zones are determined geographically.

3. The method of claim 1, wherein the first and second proximity zones are determined based on network latency.

4. The method of claim 1, wherein the first and second proximity zones are determined based on packet round trip time.

5. The method of claim 1, wherein selecting a set of baseline agents comprises identifying those agents having the most reliable responses to a series of network communication tests with the target website.

6. The method of claim 5, wherein the most reliable responses are those responses having the fastest network round trip time.

7. The method of claim 5, wherein the most reliable responses are those responses having the least performance variation in website load time.

8. A computer implemented method for isolating local performance variation in monitoring a target website from a monitoring server that is communicatively coupled with the target website via a data communication network, comprising:

identifying a plurality of monitor agents dispersed across at least two proximity zones for the target website;

monitoring the target website in a plurality of monitoring rounds, wherein each monitoring round comprises monitoring the target website from each of the plurality of monitor agents.

9. The method of claim 8, further comprising determining a performance variation for each of at least a portion of the plurality of monitor agents, wherein determining the performance variation of a first monitor agent comprises analysis of historical monitoring performance for the first monitor agent.

10. The method of claim 8, further comprising determining a performance variation for each of at least a portion of the plurality of monitor agents, wherein determining the performance variation of a first monitor agent comprises identifying an average load time of a web page from the target website by the first monitor agent.

11. The method of claim 10, wherein determining the performance variation of a first monitor agent further comprises identifying a difference between the load time of the web page from the target website during a first monitoring round and the average load time of the web page.

12. A system for isolating local performance variation in monitoring a target website via a data communication network, comprising:

a plurality of baseline agents communicatively coupled with a target website via a data communication network, wherein each of the plurality of baseline agents is in a first proximity zone with respect to the target website;

a plurality of monitor agents communicatively coupled with the target website via the data communication network, wherein each of the plurality of monitor agents is in a second proximity zone with respect to the target website; and

a controller server communicatively coupled with the plurality of baseline agents, the plurality of monitor agents and the target website via the data communication network, wherein the controller server is configured to monitor the target website in a plurality of monitoring rounds, wherein each monitoring round comprises monitoring the target website from each baseline agent and from at least one monitor agent.

13. The system of claim 12, wherein the first and second proximity zones are determined geographically.

14. The system of claim 12, wherein the first and second proximity zones are determined based on network latency.

15. The system of claim 12, wherein the first and second proximity zones are determined based on packet round trip time.

16. The system of claim 12, wherein the controller server is configured to determine the plurality of baseline agents by identifying those agents having the most reliable responses to a series of network communication tests with the target website.

17. The system of claim 16, wherein the most reliable responses are those responses having the fastest network round trip time.

18. The system of claim 16, wherein the most reliable responses are those responses having the least performance variation in website load time.