Data analytics for adaptive networks

• US 10,616,057 B2
• Filed: 01/03/2019
• Issued: 04/07/2020
• Est. Priority Date: 09/25/2014
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Rahman, Moshiur, Bajpay, Paritosh, Kumar, Shiv
• Primary Examiner(s): Dao, Tuan C
• Assistant Examiner(s): Wood, William C
• Application Number:
  US16/238,837
• Publication Number:
  US 20190158357A1
• Time in Patent Office: 460 Days
• Field of Search: None
• CPC Class Codes: G06F 2009/4557 Distribution of virtual machine instances; Migration and load balancing
• CPC Class Codes: G06F 2009/45595 Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: G06F 9/4856 resumption being on a different machine, e.g. task migration, virtual machine migration G06F9/5088 takes precedence
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: G06F 9/5077 Logical partitioning of resources; Management or configuration of virtualized resources specific details on emulation or internal functioning of virtual machines G06F9/455
• CPC Class Codes: G06F 9/5088 involving task migration
• CPC Class Codes: H04L 41/0816 the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0897 by horizontal or vertical scaling of resources, or by migrating entities, e.g. virtual resources or entities
• CPC Class Codes: H04L 41/14 Network analysis or design
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1012 based on compliance of requirements or conditions with available server resources
• CPC Class Codes: H04L 67/148 Migration or transfer of sessions

Abstract

Aspects of the subject disclosure may include, for example, a method in which first performance data and second performance data are obtained for a first virtual machine and a second virtual machine on a network, the performance data is analyzed, performance of the first virtual machine is predicted, and a performance trend for the first virtual machine is determined. Performance of the second virtual machine is predicted in accordance with analyzing of the second performance data and a potential transfer of execution of an application from the first virtual machine to the second virtual machine. Execution of the application is transferred to the second virtual machine after determining an expected improvement in execution of the application relative to the first virtual machine. Other embodiments are disclosed.

Claims

1. A method comprising:

predicting, by a processing system, a first predicted performance of a first virtual machine, wherein the first virtual machine executes an application, and wherein the first virtual machine and a second virtual machine are arranged in a hierarchy of virtual machines;

predicting, by the processing system, a second predicted performance of the second virtual machine based on first performance data of the first virtual machine, second performance data of the second virtual machine and a potential transfer of execution of the application from the first virtual machine to the second virtual machine; and

transferring, by the processing system, the execution of the application from the first virtual machine to the second virtual machine based on the first predicted performance and the second predicted performance,

wherein the second virtual machine is identified in accordance with the hierarchy prior to obtaining the second performance data of the second virtual machine.

2. The method of claim 1, wherein the first virtual machine and the second virtual machine are part of a network that comprises a communication network, wherein the application processes messages transmitted over the communication network, and wherein the first and second predicted performances are based on an expected change in message traffic on the network.

3. The method of claim 2, wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on a network policy, and wherein the network policy comprises a criterion for evaluating success of the transfer.

4. The method of claim 2, further comprising initiating a reconfiguration of the network based on the expected change in message traffic.

5. The method of claim 1, wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on an expected improvement in the execution of the application on the second virtual machine relative to the first virtual machine.

6. The method of claim 1, further comprising:

obtaining network performance data including traffic data; and

determining an expected improvement in the execution of the application on the second virtual machine relative to the first virtual machine in accordance with the network performance data,

wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on the expected improvement.

7. The method of claim 1, wherein the first performance data and the second performance data include operations data obtained from equipment of users of a network.

8. The method of claim 1, further comprising:

determining a performance trend for the first virtual machine; and

evaluating, by the processing system, the potential transfer of the execution of the application from the first virtual machine to the second virtual machine in accordance with the performance trend.

9. The method of claim 1, wherein the first performance data and the second performance data include static virtual machine performance data relating to past performance of the first virtual machine and second virtual machine, respectively.

10. The method of claim 9, wherein the static virtual machine performance data is stored at a first storage device accessible to the processing system.

11. The method of claim 1, wherein the first performance data and the second performance data include dynamic virtual machine performance data relating to current performance of the first virtual machine and the second virtual machine, respectively.

12. The method of claim 1, further comprising:

obtaining network performance data including traffic data;

determining a trend in network performance based on a change in the traffic data; and

initiating a reconfiguration of a network based on the trend to maintain a level of network performance in accordance with a network policy.

13. A computing device comprising:

a processing system including a processor; and

a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising;

predicting a first predicted performance of a first virtual machine, wherein the first virtual machine executes an application, wherein the first virtual machine and a second virtual machine are arranged in a hierarchy of virtual machines;

predicting a second predicted performance of the second virtual machine based on first performance data of the first virtual machine, second performance data of the second virtual machine and a potential transfer of execution of the application from the first virtual machine to the second virtual machine; and

transferring the execution of the application from the first virtual machine to the second virtual machine based on the first predicted performance and the second predicted performance,

wherein the second virtual machine is identified in accordance with the hierarchy prior to obtaining the second performance data of the second virtual machine.

14. The computing device of claim 13, wherein the first performance data and the second performance data include static data relating to past performance of the first virtual machine and second virtual machine respectively and stored at a storage device accessible to the computing device and dynamic data relating to current performance of the first virtual machine and the second virtual machine respectively.

15. The computing device of claim 13, wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on an expected improvement in the execution of the application on the second virtual machine relative to the first virtual machine and in accordance with a network policy.

16. The computing device of claim 13, wherein the operations further comprise:

obtaining network performance data including traffic data; and

determining an expected improvement in the execution of the application on the second virtual machine relative to the first virtual machine in accordance with the network performance data,

wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on the expected improvement.

17. The computing device of claim 13, wherein the first performance data and the second performance data include operations data obtained from equipment of users of a network.

18. A non-transitory machine-readable storage device comprising instructions which, when executed by a processing system including a processor, cause the processing system to perform operations comprising:

predicting a first predicted performance of a first virtual machine, wherein the first virtual machine executes an application, wherein the first virtual machine and a second virtual machine are arranged in a hierarchy of virtual machines;

predicting a second predicted performance of the second virtual machine, based on first performance data of the first virtual machine, second performance data of the second virtual machine and a potential transfer of execution of the application from the first virtual machine to the second virtual machine; and

transferring the execution of the application from the first virtual machine to the second virtual machine based on the first predicted performance and the second predicted performance,

wherein the second virtual machine is identified in accordance with the hierarchy prior to obtaining the second performance data of the second virtual machine.

19. The non-transitory machine-readable storage device of claim 18, wherein the first performance data and the second performance data include stored static data relating to past performance of the first virtual machine and second virtual machine respectively and accessible to the processing system and dynamic data relating to current performance of the first virtual machine and the second virtual machine respectively.

20. The non-transitory machine-readable storage device of claim 18, wherein the transferring of the execution of the application from the first virtual machine to the second virtual machine is further based on a performance trend and an expected improvement in the execution of the application on the second virtual machine relative to the first virtual machine in accordance with a network policy.