Managing an entity using a state machine abstract

• US 9,172,552 B2
• Filed: 01/31/2013
• Issued: 10/27/2015
• Est. Priority Date: 01/31/2013
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Marwah, Manish, Bellala, Gowtham, Shah, Amip J, Arlitt, Martin, Bash, Cullen E
• Primary Examiner(s): Coulter, Kenneth R
• Application Number:
  US13/755,768
• Publication Number:
  US 20140215053A1
• Time in Patent Office: 999 Days
• Field of Search: 709/224, 709/223
• US Class Current: 1/1
• CPC Class Codes: G05B 2219/23289 State logic control, finite state, tasks, machine, fsm
• CPC Class Codes: G06F 9/4498 Finite state machines
• CPC Class Codes: H04L 12/28 characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks] wireless communication networks H04W ; arrangements for dividing the transmission path H04W40/00

Abstract

In an implementation, a method for managing an entity may include establishing of a framework for characterization of the entity, in which the framework abstracts operation of the entity to a state machine. In addition, each state in the state machine may be characterized in terms of distributions of a performance metric associated with the entity. The method may also include implementing of the framework for characterization of the entity to manage the entity.

Claims

1. A method for managing an entity, said method comprising:

establishing a framework for characterization of the entity by abstracting operation of the entity to a state machine, wherein each state in the state machine is characterized in terms of distributions of a performance metric associated with the entity, and wherein abstracting operation of the entity comprises;

determining a set of a plurality of states (n) of the entity;

determining a n×

n transition matrix such that an element (i,j) of the transition matrix specifies the probability of the entity transitioning from a state i to a state j;

determining a set of m symbols that correspond to events that lead to the transitions from state i to state j; and

determining a n×

n×

m matrix that specifies events that lead to transitions such that an element (i,j,k) of the n×

n×

m matrix specifies the probability that event (k) causes a transition from state i to state j; and

implementing, by a processor, the framework for characterization of the entity to manage the entity.

2. The method according to claim 1, wherein establishing the framework further comprises:

collecting data related to the entity from a source;

preprocessing the collected data to merge the collected data; and

transforming the preprocessed data.

3. The method according to claim 2, further comprising:

constructing a causal model for the entity that relates at least one of a resource use and a performance metric to at least one of utilization, performance, and environmental conditions of the entity.

4. The method according to claim 2, wherein abstracting operation of the entity to the state machine further comprises abstracting operation of the entity to the state machine using the transformed data.

5. The method according to claim 1, wherein determining the n×

n transition matrix further comprises determining the n×

n transition matrix by mapping collected data points to the states and computing the probability of the transitions.

6. The method according to claim 1, further comprising:

characterizing each of the states of the entity in terms of a performance metric; and

incorporating the characterized states into the state machine abstraction.

7. The method according to claim 6, said method further comprising:

converting a multivariate time series that describes each time point of the collected data with a characterized state into a univariate symbol stream.

8. The method according to claim 7, wherein implementing the framework to analyze the entity further comprises implementing the framework to at least one of detect an anomaly that results in a degradation of the performance metric, assess an efficiency of the entity, predict a future state of the entity, and determine usage patterns of the entity.

9. An apparatus for managing an entity, said apparatus comprising:

a processor;

a memory storing at least one module comprising machine readable instructions that are to cause the processor to;

establish an abstraction of a state machine, wherein each state in the state machine is characterized in terms of distributions of a performance metric associated with the entity, wherein to establish the abstraction of the state machine, the machine readable instructions are further to cause the processor to;

determine a transition matrix such that an element of the transition matrix specifies the probability of the entity transitioning from a first state to a second state;

determine a set of symbols that correspond to events that lead to the transitions from the first state to the second state; and

determine a matrix that specifies events that lead to transitions such that an element of the determined matrix specifies the probability that an identified event causes a transition from the first state to the second state; and

implement the abstracted state machine to manage the entity.

10. The apparatus according to claim 9, wherein machine readable instructions are further to:

collect data related to the entity from a source;

preprocess the collected data to merge the collected data; and

transform the preprocessed data.

11. The apparatus according to claim 9, wherein the machine readable instructions are further to:

characterize each of the states of the entity in terms of a performance metric; and

incorporate the characterized states into the state machine abstraction.

12. The apparatus according to claim 11, wherein the machine readable instructions are further to:

convert a multivariate time series that describes each time point of the collected data with a characterized state into a univariate symbol stream using the abstraction of the state machine.

13. The apparatus according to claim 9, wherein the machine readable instructions are further to:

implement the framework to at least one of detect an anomaly that results in a degradation of the performance metric, assess an efficiency of the entity, predict a future state of the entity, and determine usage patterns of the entity.

14. A non-transitory computer readable storage medium on which is stored machine readable instructions that when executed by a processor, implements a method to manage an entity, wherein said machine readable instructions are to cause the processor to:

establish an abstraction of a state machine, wherein each state in the state machine is characterized in terms of distributions of a performance metric associated with the entity, wherein to establish the abstraction of the state machine, the machine readable instructions are further to cause the processor to;

determine a transition matrix such that an element of the transition matrix specifies the probability of the entity transitioning from a first state to a second state;

determine a set of symbols that correspond to events that lead to the transitions from the first state to the second state; and

determine a matrix that specifies events that lead to transitions such that an element of the determined matrix specifies the probability that an identified event causes a transition from the first state to the second state;

characterize each of the states of the entity in terms of a performance metric;

incorporate the characterized states into the state machine abstraction; and

implement the abstracted state machine with the incorporated characterized states to manage the entity.

15. The non-transitory computer readable storage medium according to claim 14, wherein to determine the transition matrix, the machine readable instructions are further to cause the processor to determine the transition matrix by mapping collected data points to the states and computing the probability of the transitions.