Multiplexing and congestion control

• US 10,447,544 B2
• Filed: 06/01/2017
• Issued: 10/15/2019
• Est. Priority Date: 03/07/2003
• Status: Active Grant

• Current Assignee: TRIA Network Systems, LLC
• Original Assignee: TRIA Network Systems, LLC
• Inventors: Day, John D.
• Primary Examiner(s): Lee, Bryan
• Application Number:
  US15/611,447
• Publication Number:
  US 20170272325A1
• Time in Patent Office: 866 Days
• CPC Class Codes: G06F 21/10 Protecting distributed programs or content, e.g. vending or licensing of copyrighted material protection in video systems or pay television H04N7/16 ; Digital rights management [DRM]
• CPC Class Codes: H04L 41/00 Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
• 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 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/5025 by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
• CPC Class Codes: H04L 45/00 Routing or path finding of packets in data switching networks routing or path finding in wireless networks H04W40/00
• CPC Class Codes: H04L 47/10 Flow control; Congestion control
• CPC Class Codes: H04L 47/12 Avoiding congestion; Recovering from congestion
• CPC Class Codes: H04L 47/193 at the transport layer, e.g. TCP related
• CPC Class Codes: H04L 47/21 using leaky-bucket
• CPC Class Codes: H04L 47/2416 Real-time traffic
• CPC Class Codes: H04L 49/90 Buffering arrangements
• CPC Class Codes: H04L 61/10 of different types
• CPC Class Codes: H04L 61/5061 Pools of addresses
• CPC Class Codes: H04L 61/5084 Providing for device mobility network addressing or numbering for mobility support in wireless networks H04W8/26; mobile IP H04W80/04
• CPC Class Codes: H04L 65/1069 Session establishment or de-establishment
• CPC Class Codes: H04L 65/765 intermediate
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/565 : Conversion or adaptation of application format or content adding application control or application functional data H04L67/561
• CPC Class Codes: H04L 69/06 : Notations for structuring of protocol data, e.g. abstract syntax notation one [ASN.1]
• CPC Class Codes: H04L 69/08 : Protocols for interworking; Protocol conversion
• CPC Class Codes: H04L 69/24 : Negotiation of communication capabilities
• CPC Class Codes: H04L 69/32 : Architecture of open systems interconnection [OSI] 7-layer type protocol stacks, e.g. the interfaces between the data link level and the physical level
• CPC Class Codes: H04L 69/325 : in the network layer [OSI layer 3], e.g. X.25 H04L69/16 takes precedence
• CPC Class Codes: H04L 69/329 : in the application layer [OSI layer 7]
• CPC Class Codes: H04Q 3/0054 : Service creation techniques
• CPC Class Codes: H04Q 3/0095 : Specification, development or application of network management software, e.g. software re-use
• CPC Class Codes: H04W 40/248 : Connectivity information update
• CPC Class Codes: H04W 8/087 : for preserving data network PoA address despite hand-offs
• CPC Class Codes: H04W 8/26 : Network addressing or numbering for mobility support
• CPC Class Codes: H04W 80/04 : Network layer protocols, e.g. mobile IP [Internet Protocol]

Abstract

Methods, systems and devices for network congestion control exploit the inherent burstiness of network traffic, using a wave-based characterization of network traffic and corresponding multiplexing methods and approaches.

Claims

1. A method comprising:

receiving a request for a quality of service (QoS) associated with an application executing on a network, the QoS expressed as requested QoS parameters;

obtaining operating parameters of the network;

modeling traffic for a group of flows in the network as probability waves based at least in part on the operating parameters to generate a model of the traffic including model flows;

determining QoS parameters for the network, wherein the QoS parameters for the network are expressed in terms of properties of the probability waves; and

controlling an operational condition of the network in accordance with the model, thereby controlling the QoS parameters for the network to satisfy the requested QoS parameters.

2. The method of claim 1, wherein the QoS parameters for the network are determined based on waveforms of the probability waves.

3. The method of claim 2, wherein a height of each of the probability waves at time t is equal to a probability of traffic, P(t) at the time t.

4. The method of claim 1, wherein the properties of the probability waves comprise bandwidth, wavelength, jitter, or combinations thereof.

5. The method of claim 1, wherein controlling the operational condition comprises generating instructions to manage traffic congestion on the network.

6. The method of claim 5, wherein the instructions comprise instructions for combining selected flows of the group of flows based on the model flows, a probability of traffic, available bandwidth and a predicted bandwidth, and based on a congestion factor.

7. The method of claim 6, wherein a probability wave of flow i, represented by P_i(t), is selected based on the available bandwidth and the predicted bandwidth, calculated from a sum of the probability waves of the group of flows according to B′

=Σ

BP_i(t)≤

B(1−

ε

), where B is the available bandwidth, B′

is the predicted bandwidth, and ε

is the congestion factor.

8. The method of claim 6, wherein the selected flows are chosen based on the corresponding probability waves of the selected flows when combined approximating a square wave.

9. The method of claim 6, wherein the network comprises a plurality of network elements in a plurality of layers, and further comprising transmitting the instructions to one or more of the plurality of network elements to combine the selected flows, wherein the selected flows are limited to a same layer of the network.

10. The method of claim 1, wherein the network comprises a plurality of network elements in a plurality of layers, wherein each of the plurality of network elements has an operating region associated therewith, and wherein a QoS parameter of the QoS parameters for the network is associated with each operating region.

11. A device comprising:

a memory storing executable instructions; and

a processor in communication with the memory, wherein the processor, responsive to executing the instructions, performs operations comprising;

obtaining operating parameters of a network;

modeling traffic for a group of flows in the network as probability waves based at least in part on the operating parameters to generate a model of the traffic including model flows;

determining quality of service (QoS) parameters for the network based on waveforms of the probability waves, wherein the QoS parameters for the network are expressed in terms of properties of the probability waves, wherein the properties of the probability waves comprise bandwidth, wavelength, jitter, or a combination thereof; and

controlling an operational condition of the network in accordance with the model, thereby controlling the QoS parameters for the network to satisfy requested QoS parameters corresponding to a requested quality of service of an application executing on the network.

12. The device of claim 11, wherein controlling the operational condition comprises generating instructions to manage traffic congestion on the network.

13. The device of claim 11, wherein the instructions comprise instructions for combining selected flows of the group of flows based on the model flows, a probability of traffic, available bandwidth and a predicted bandwidth, and based on a congestion factor.

14. The device of claim 13, wherein a probability wave of flow i, represented by P_i(t), is selected based on the available bandwidth and the predicted bandwidth, calculated from a sum of the probability waves of the group of flows according to B′

=Σ

BP_i(t)≤

B(1−

ε

), where B is the available bandwidth, B′

is the predicted bandwidth, and ε

is the congestion factor.

15. The device of claim 13, wherein the selected flows are chosen based on the corresponding probability waves of the selected flows when combined approximating a square wave.

16. A non-transitory machine-readable storage medium comprising executable instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising:

receiving a request for a quality of service (QoS) associated with an application executing on a network, the QoS expressed as requested QoS parameters;

obtaining operating parameters of the network;

modeling traffic for a group of flows in the network as probability waves based at least in part on the operating parameters to generate a model of the traffic including model flows;

determining QoS parameters for the network based on waveforms of the probability waves, wherein the QoS parameters for the network are expressed in terms of properties of the probability waves; and

controlling an operational condition of the network in accordance with the model, thereby controlling the QoS parameters for the network to satisfy the requested QoS parameters, wherein controlling the operational condition comprises generating instructions to manage traffic congestion on the network.

17. The non-transitory machine-readable storage medium of claim 16, wherein the instructions comprise instructions for combining selected flows of the group of flows based on the model flows, a probability of traffic, available bandwidth and a predicted bandwidth, and based on a congestion factor.

18. The non-transitory machine-readable storage medium of claim 16, wherein a height of each of the probability waves at time t is equal to a probability of traffic, P(t) at the time t.

19. The non-transitory machine-readable storage medium of claim 16, wherein the selected flows are chosen based on the corresponding probability waves of the selected flows when combined approximating a square wave.

20. The non-transitory machine-readable storage medium of claim 16, wherein the storage medium is coupled to a network management server of the network, and wherein the operations further comprise transmitting the instructions from the network management server to one or more network elements of the network.