Methods and systems of predictive monitoring of objects in a distributed network system

• US 9,483,334 B2
• Filed: 03/15/2013
• Issued: 11/01/2016
• Est. Priority Date: 01/28/2013
• Status: Active Grant

• Current Assignee: Rackspace US Incorporated (Apollo Global Management, Inc.)
• Original Assignee: Rackspace US Incorporated (Apollo Global Management, Inc.)
• Inventors: Walsh, Alexander Leonard
• Primary Examiner(s): Chen, Qing
• Assistant Examiner(s): Morshed, Hossain
• Application Number:
  US13/841,552
• Publication Number:
  US 20140214745A1
• Time in Patent Office: 1,327 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• CPC Class Codes: G06F 11/3041 where the computing system component is an input/output interface interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units G06F13/00
• CPC Class Codes: G06F 11/3051 Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs verification or detection of system hardware configuration G06F11/2247
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 2209/547 Messaging middleware
• CPC Class Codes: G06F 2209/548 Queue
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: G06F 9/465 Distributed object oriented systems remote method invocation [RMI] G06F9/548
• CPC Class Codes: G06F 9/546 Message passing systems or structures, e.g. queues
• CPC Class Codes: H04L 41/069 using logs of notifications; Post-processing of notifications
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications

Abstract

Predictive monitoring of objects in a distributed network system providing cloud services is disclosed. In one embodiment, the methods and systems observing one or more update messages sent and received among components of the distributed network system, the update messages comprising information associated with a state of an object on the distributed network system, determine the state of the object in response to the one or more update messages, and reference a predictive object state model to predict occurrence of an artifact in response to the state of the object. Advantageously, the present embodiments provide advanced warning of potential failures in a distributed network systems, which may enable a system administrator or dynamic process to resolve the failure before it ever occurs. Additional benefits and advantages of the present embodiments will become evident in the following description.

Claims

1. A method of predictive monitoring of a process in a distributed network system providing cloud services, the method comprising:

observing one or more notifications transmitted among components of the distributed network system, the notifications transmitted in the completion of an API request initiating the monitored process;

determining a state of the monitored process based on the observed notifications;

updating a predictive state model configured to generate state predictions for the monitored process, the updating of the predictive state model comprising assigning higher predictive weights to more recent state transitions, wherein the predictive state model is updated based on the determined state and based on timing information for the determined state, wherein the timing information includes the time of the transition by the monitored process to the determined state; and

referencing, based on the determined state, the predictive state model to generate probabilities of future state transitions by the monitored process. wherein the updating of the predictive state model includes assigning higher predictive weights to more recent state transitions.

2. The method of claim 1, wherein the timing information includes the duration of time spent by the monitored process in the determined state.

5. The method of claim 1, wherein the state of the monitored process is determined based on a mapping of notifications to specific states of the monitored process.

3. The method of claim I, wherein the generated probabilities of future state transitions include a probability of a specific state transition occurring within a specified time threshold.

4. The method of claim 3, wherein the specific state transitions are required for the completion of the API request.

6. A system for predictive monitoring of a process in a distributed network comprising:

a processor; and

a memory coupled to the processor, the memory including computer-readable instructions that, upon execution by the processor, cause the system to;

observe one or more notifications transmitted among components of the distributed network system, the notifications transmitted in the completion of an API request initiating the monitored process;

determine a state of the monitored process based on the observed notifications; and

update a predictive state model configured to generate state predictions for the monitored process, the updating of the predictive state model comprising assigning higher predictive weights to more recent state transitions, wherein the predictive state model is updated based on the determined state and based on timing information for the determined state, wherein the timing information includes the time of the transition by the monitored process to the determined state;

reference, based on the determined state, the predictive state model to generate probabilities of future state transitions by the monitored process.

7. The distributed network system of claim 6, wherein the timing information includes the duration of time spent by the monitored process in the determined state.

8. The distributed network system of claim 6, wherein the generated probabilities of future state transitions include a probability of a specific state transition occurring within a specified time threshold.

9. The distributed network system of claim 8, wherein the specific state transitions are required for the completion of the API request.

10. The distributed network system of claim 6, wherein the state of the monitored process is determined based on a mapping of notifications to specific states of the monitored process.

11. A non-transitory computer-accessible storage medium storing program instructions that, when executed by a data processing device, cause the data processing device to implement operations for monitoring a process in a distributed network system providing cloud services, the operations comprising:

observe one or more notifications transmitted among components of the distributed network system, the notifications transmitted in the completion of an API request initiating the monitored process;

determine a state of the monitored process based on the observed notifications;

update a predictive state model configured to generate state predictions for the monitored process, the updating of the predictive state model comprising assigning higher predictive weights to more recent state transitions, wherein the predictive state model is updated based on the determined state and based on timing information for the determined state, wherein the timing information includes the time of the transition by the monitored process to the determined state; and

reference, based on the determined state, the predictive state model to generate probabilities of future state transitions by the monitored process.

12. The computer-accessible storage medium of claim 11, wherein the timing information includes the duration of time spent by the monitored process in the determined state.

13. The computer-accessible storage medium of claim 11, wherein the generated probabilities of future state transitions include a probability of a specific state transition occurring within a specified time threshold.

14. The computer-accessible storage medium of claim 13, wherein the specific state transitions are required for the completion of the API request.