IP Library Granted Patent US 12,301,428
Granted Patent B2
US 12,301,428 · App. 18/151,999 · Granted May 13, 2025

Queue-based orchestration simulation

Inventor: Viktor M. E. Leijon (San Jose, CA)
Assignee: Cisco Technology, Inc.
H04L41/145G06F9/546H04L41/12
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Quick Facts
Patent No.
US 12,301,428
App. No.
18/151,999
Granted
May 13, 2025
Kind
B2
Abstract

Techniques for simulation of orchestration and automation are disclosed. These techniques include simulating a distributed orchestration system using a simulation topology including a plurality of queues used in place of processing elements in the distributed orchestration system. The techniques further include identifying a plurality of characteristics of the simulation topology, after the simulation, and modifying the distributed orchestration system based on the plurality of characteristics.

Claims (43)

1. A method, comprising:

simulating a distributed orchestration system using a simulation topology comprising a plurality of interconnected queues used to replace interconnected processing elements in the distributed orchestration system, wherein each of the interconnected queues mimic a processing time of the replaced interconnected processing elements;

identifying a plurality of characteristics of the simulation topology, after the simulation; and

modifying the distributed orchestration system based on the plurality of characteristics.

2. The method of claim 1 , wherein the distributed orchestration system comprises a plurality of processing elements and wherein the simulation topology replaces each of the plurality of processing elements with a queue.

3. The method of claim 2 , wherein each queue is assigned a distribution time function used to determine a duration of time spent in the queue.

4. The method of claim 3 , wherein each queue is assigned one of: (i) a normal distribution or (ii) an exponential distribution as the respective distribution time function.

5. The method of claim 1 , wherein modifying the distributed orchestration system based on the plurality of characteristics comprises:

scaling up a simulation of the distributed orchestration system based on results from an earlier simulation, wherein the plurality of characteristics relate to the scaled up simulation.

6. The method of claim 1 ,

wherein the plurality of characteristics comprises a queue depth relating to at least one of the plurality of interconnected queues, and

wherein modifying the distributed orchestration system based on the plurality of characteristics comprises modifying the distributed orchestration system to reduce the queue depth.

7. The method of claim 6 , wherein modifying the distributed orchestration system to reduce the queue depth comprises:

iteratively repeating simulation of the distributed orchestration system at least twice and modifying the distributed orchestration system to reduce the queue depth after each simulation.

8. The method of claim 1 ,

wherein the plurality of characteristics comprises an average processing time, and

wherein modifying the distributed orchestration system based on the plurality of characteristics comprises modifying the distributed orchestration system to reduce the average processing time.

9. The method of claim 1 , wherein modifying the distributed orchestration system based on the plurality of characteristics comprises adding one or more processing elements to the distributed orchestration system.

10. A system, comprising:

a processor; and

a memory having instructions stored thereon which, when executed on the processor, performs operations comprising:

simulating a distributed orchestration system using a simulation topology comprising a plurality of interconnected queues used to replace interconnected processing elements in the distributed orchestration system, wherein each of the interconnected queues mimic a processing time of the replaced interconnected processing elements;

identifying a plurality of characteristics of the simulation topology, after the simulation; and

modifying the distributed orchestration system based on the plurality of characteristics.

11. The system of claim 10 , wherein the distributed orchestration system comprises a plurality of processing elements and wherein the simulation topology replaces each of the plurality of processing elements with a queue.

12. The system of claim 11 , wherein each queue is assigned a distribution time function used to determine a duration of time spent in the queue.

13. The system of claim 12 , wherein each queue is assigned one of: (i) a normal distribution or (ii) an exponential distribution as the respective distribution time function.

14. The system of claim 10 , wherein modifying the distributed orchestration system based on the plurality of characteristics comprises:

scaling up a simulation of the distributed orchestration system based on results from an earlier simulation, wherein the plurality of characteristics relate to the scaled up simulation.

15. The system of claim 10 ,

wherein the plurality of characteristics comprises a queue depth relating to at least one of the plurality of interconnected queues, and

wherein modifying the distributed orchestration system based on the plurality of characteristics comprises modifying the distributed orchestration system to reduce the queue depth.

16. A non-transitory computer-readable medium having instructions stored thereon which, when executed by a processor, performs operations comprising:

simulating a distributed orchestration system using a simulation topology comprising a plurality of interconnected queues used to replace interconnected processing elements in the distributed orchestration system, wherein each of the interconnected queues mimic a processing time of the replaced interconnected processing elements;

identifying a plurality of characteristics of the simulation topology, after the simulation; and

modifying the distributed orchestration system based on the plurality of characteristics.

17. The non-transitory computer-readable medium of claim 16 , wherein the distributed orchestration system comprises a plurality of processing elements and wherein the simulation topology replaces each of the plurality of processing elements with a queue.

18. The non-transitory computer-readable medium of claim 17 , wherein each queue is assigned a distribution time function used to determine a duration of time spent in the queue.

19. The non-transitory computer-readable medium of claim 16 , wherein modifying the distributed orchestration system based on the plurality of characteristics comprises:

scaling up a simulation of the distributed orchestration system based on results from an earlier simulation, wherein the plurality of characteristics relate to the scaled up simulation.

20. The non-transitory computer-readable medium of claim 16 ,

wherein the plurality of characteristics comprises an average processing time, and

wherein modifying the distributed orchestration system based on the plurality of characteristics comprises modifying the distributed orchestration system to reduce the average processing time.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: LEIJON, VIKTOR M.E.
To: CISCO TECHNOLOGY, INC.
Reel/Frame 062317/0469 →
Continuity (1)
Related Publication 20240235949A1 · Jul 11, 2024
References Cited (17)
US 11010205B2 · Sharma et al. · 2021 [cited by applicant]
US 20100220622A1 · Wei · 2010 [cited by applicant]
US 20160080221A1 · Ramachandran · 2016 [cited by examiner]
US 20160142271A1 · Dunne et al. · 2016 [cited by applicant]
US 20190182128A1 · Shimamura et al. · 2019 [cited by applicant]
US 20190280918A1 · Hermoni · 2019 [cited by examiner]
US 20210105185A1 · Parekh · 2021 [cited by examiner]
US 20210144517A1 · Guim Bernat et al. · 2021 [cited by applicant]
US 20220078093A1 · Bruun, III et al. · 2022 [cited by applicant]
US 20220116304A1 · Sommers · 2022 [cited by examiner]
US 20220224605A1 · Jain · 2022 [cited by examiner]
US 20220224762A1 · Feng · 2022 [cited by examiner]
Ibrahim Afolabi et al., “Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System,” ResearchGate, Dated: Jan. 2022, pp. 1-16. [cited by applicant]
Jonathan Prados-Garzon et al., “Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation,” IEEE Transactions on Mobile Computing, Dated: Feb. 14, 2020, pp. 1-16. [cited by applicant]
Bin Han et al., “Multiservice-based Network Slicing Orchestration with Impatient Tenants,” arXiv.org, Dated: Apr. 16, 2020. [cited by applicant]
Jonathan Prados-Garzon et al., “A Queuing based Dynamic Auto Scaling Algorithm for the LTE EPC Control Plane,” Mosaic Lab, Date Access: Jul. 20, 2023, pp. 1-7. [cited by applicant]
Stefan Schneider et al., “Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets,” arxiv.org, Dated: Aug. 14, 2018, pp. 1-9. [cited by applicant]