SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE CLOUD MANAGEMENT

• US 20150117259A1
• Filed: 10/30/2014
• Published: 04/30/2015
• Est. Priority Date: 10/30/2013
• Status: Abandoned Application

• Current Assignee: Remtcs Inc.
• Original Assignee: Remtcs Inc.
• Inventors: Xaypanya, Tommy, Malinowski, Richard E.
• Application Number:
  US14/528,560
• Publication Number:
  US 20150117259A1
• Time in Patent Office: Days
• US Class Current: 370/254
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence

Abstract

An artificial intelligence engine is provided to analyze a network'"'"'s bandwidth. The artificial intelligence engine then causes devices, such as links and traffic processing units to be dynamically allocated or de-allocated. The network may comprise a layered network or stacked cloud network whereby an overlaying network comprises a neural cluster of one or more of the artificial intelligence engines. The underlying network comprises the links connecting one or more devices, such as processing components and endpoints.

Claims

1. A system for managing bandwidth capacity of a network comprising:

a network interface operable to access a bandwidth capacity of the network and a bandwidth utilization of the network, wherein the network comprises an allocated number of traffic handling components, each traffic handling component providing a portion of the bandwidth of the network;

an artificial intelligence engine, operable to determine a target bandwidth capacity and further operable to signal a traffic handling unit manager in accord with the target bandwidth; and

the traffic handling unit manager operable to, upon receiving the signal from the artificial intelligence engine, allocate an adjusted number of traffic handling components selected to reduce the difference between the bandwidth capacity and the target bandwidth capacity.

2. The system of claim 1, wherein the artificial intelligence engine is further operable to determine the target bandwidth capacity which is greater than the bandwidth capacity when the bandwidth utilization is determined to be substantially limited by the bandwidth capacity.

3. The system of claim 1, wherein the traffic handling components comprise links operable to communicatively connect at least two devices, wherein the devices comprise processing components and end-points.

4. The system of claim 1, wherein the traffic handling components comprise traffic processing units.

5. The system of claim 4, wherein at least one of the number of traffic handling units comprises a virtual machines provided by private and commercial cloud computing providers.

6. The system of claim 1, wherein the traffic handing unit manager is operable to allocate an adjusted number of traffic handling components selected to reduce the difference between the bandwidth capacity and the target bandwidth capacity within a previously determined margin.

7. The system of claim 1, wherein when the traffic handling unit manager is further operable to, upon the adjusted number of traffic handling units being negative, de-allocate a number of the traffic handling units.

8. The system of claim 7, wherein the traffic handling unit manager is operable to de-allocate at least one traffic handling units by allocating the at least one traffic handling units to a task other than processing bandwidth for the network.

9. The system of claim 1, wherein said network comprises an overlay network superimposed over an underlying network and wherein the overlay network comprises a neural cluster comprising at least one artificial intelligence engine.

10. The system of claim 1, wherein the underlying network comprises traffic handling units comprise links operable to communicatively connect at least two devices, wherein the devices comprise processing components and end-points.

11. An artificial intelligence engine, comprising:

an input to receive a bandwidth capacity of a network and a bandwidth utilization of the network, wherein the network comprises an allocated number of traffic handling components, each traffic handling component providing a portion of the bandwidth of the network;

a logic unit operable to determine a target bandwidth capacity; and

an output operable to signal a traffic handling unit manager in accord with the target bandwidth.

12. The artificial intelligence engine of claim 11, wherein the input is further operable to receive the bandwidth capacity and bandwidth utilization periodically.

13. The artificial intelligence engine of claim 11, wherein the logic unit is operable to determine a target bandwidth capacity as an estimated future target bandwidth capacity.

14. A method for managing bandwidth capacity of a network comprising:

accessing a bandwidth capacity of the network and a bandwidth utilization of the network, wherein the network comprises an allocated number of traffic handling components, each traffic handling component providing a portion of the bandwidth of the network;

determining, by an artificial intelligence engine, a target bandwidth capacity and further operable to signal a traffic handling unit manager in accord with the target bandwidth; and

receiving, by the traffic handling unit manager, the signal from the artificial intelligence engine, and allocating an adjusted number of traffic handling components selected to reduce the difference between the bandwidth capacity and the target bandwidth capacity.

15. The method of claim 14, wherein the step of determining the target bandwidth capacity further comprises determining the target bandwidth capacity upon the bandwidth utilization being determined to be substantially limited by the bandwidth capacity.

16. The method of claim 14, wherein the traffic handling components comprise links operable to communicatively connect at least two devices, wherein the devices comprise processing components and end-points.

17. The method of claim 14, wherein the traffic handling components comprise traffic processing units.

18. The method of claim 17, wherein at least one of the number of traffic handling units comprises a virtual machines provided by private and commercial cloud computing providers.

19. The method of claim 14, wherein said network comprises an overlay network superimposed over an underlying network and wherein the overlay network comprises a neural cluster comprising at least one artificial intelligence engine.

20. The method of claim 14, wherein the underlying network comprises traffic handling units comprise links operable to communicatively connect at least two devices, wherein the devices comprise processing components and end-points.