Data analytics for adaptive networks

• US 9,444,886 B2
• Filed: 09/25/2014
• Issued: 09/13/2016
• 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): Sough, H S
• Assistant Examiner(s): Wood, William C
• Application Number:
  US14/496,943
• Publication Number:
  US 20160094641A1
• Time in Patent Office: 719 Days
• Field of Search: None
• US Class Current: 1/1
• 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:

obtaining, by a computing device comprising a processor, first performance data for a first virtual machine of a plurality of virtual machines on a network, wherein the plurality of virtual machines are arranged in a hierarchy;

analyzing, by the computing device, the first performance data;

predicting, by the computing device, a first predicted performance of the first virtual machine;

evaluating, by the computing device, a potential transfer of execution of an application from the first virtual machine to another virtual machine on the network, in accordance with the first predicted performance and a predefined network policy;

identifying, by the computing device, a second virtual machine on the network in accordance with the hierarchy;

obtaining, by the computing device, second performance data for the second virtual machine,

analyzing, by the computing device, the second performance data;

predicting, by the computing device, a second predicted performance of the second virtual machine, in accordance with the analyzing of the second performance data and the evaluating of the potential transfer of execution of the application; and

determining, by the computing device, an expected improvement in execution of the application on the second virtual machine relative to the first virtual machine,

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 and stored at a first storage device accessible to the computing device and dynamic virtual machine performance data relating to current performance of the first virtual machine and the second virtual machine respectively,

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

2. The method of claim 1, wherein the network 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 network policy comprises a criterion for evaluating success of the transfer of execution of the application from the first virtual machine to the second virtual machine.

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

6. The method of claim 1, further comprising obtaining network performance data including traffic data, and wherein the determining is performed in accordance with the network performance data.

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 the network.

8. The method of claim 1, further comprising determining a performance trend for the first virtual machine, and wherein the evaluating is performed in accordance with the performance trend.

9. 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 traffic; and

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

10. The method of claim 9, wherein the network performance data includes static network performance data relating to past performance of the network and stored at a second storage device accessible to the computing device and dynamic network performance data relating to current performance of the network.

11. A computing device comprising:

a memory to store instructions; and

a processor coupled to the memory, wherein the processor, responsive to executing the instructions, performs operations comprising;

obtaining first performance data for a first virtual machine on a network and second performance data for a second virtual machine on the network, the first virtual machine executing an application, the first virtual machine and the second virtual machine being arranged in a hierarchy of virtual machines on the network, wherein the second virtual machine is identified in accordance with the hierarchy prior to obtaining the second performance data;

analyzing the first performance data and the second performance data;

predicting a first predicted performance of the first virtual machine;

predicting a second predicted performance of the second virtual machine, in accordance with the analyzing of the second performance data and a potential transfer of execution of the application from the first virtual machine to the second virtual machine; and

determining an expected improvement in execution of the application on the second virtual machine relative to the first virtual machine.

12. The computing device of claim 11, 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.

13. The computing device of claim 11, wherein the operations further comprise transferring execution of the application from the first virtual machine to the second virtual machine, based on the expected improvement and in accordance with a network policy.

14. The computing device of claim 11, wherein the operations further comprise obtaining network performance data including traffic data, and wherein the determining is performed in accordance with the network performance data.

15. The computing device of claim 11, wherein the first performance data and the second performance data include operations data obtained from equipment of users of the network.

16. The computing device of claim 11, wherein the operations further comprise determining a performance trend for the first virtual machine, and wherein the determining is performed in accordance with the performance trend.

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

obtaining first performance data for a first virtual machine on a network and second performance data for a second virtual machine on the network, the first virtual machine and the second virtual machine being arranged in a hierarchy of virtual machines on the network, wherein the second virtual machine is identified in accordance with the hierarchy prior to obtaining the second performance data;

analyzing the first performance data and the second performance data;

predicting a first predicted performance of the first virtual machine;

determining a performance trend for the first virtual machine;

predicting a second predicted performance of the second virtual machine, in accordance with the 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; and

determining an expected improvement in execution of the application on the second virtual machine relative to the first virtual machine.

18. The non-transitory machine-readable storage device of claim 17, 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 processor and dynamic data relating to current performance of the first virtual machine and the second virtual machine respectively.

19. The non-transitory machine-readable storage device of claim 17, wherein the operations further comprise transferring execution of the application from the first virtual machine to the second virtual machine, based on the performance trend and the expected improvement, and in accordance with a network policy.

20. The non-transitory machine-readable storage device of claim 17, wherein the operations further comprise obtaining network performance data including traffic data, and wherein the determining of the performance trend and the determining of the expected improvement are performed in accordance with the network performance data.