Patent application EP18382090A (EP), filed 2018-02-16
Publication number: EP3528435A1
Title: AUTOMATED CONFIGURATION AND DATA COLLECTION DURING MODELING OF NETWORK DEVICES
Abstract: In general, techniques are described for automated network device model creation using randomized test beds. A device comprising a processor may be configured to perform the techniques. The processor may generate, based on simulation configuration files, configuration objects for performing a plurality of simulation iterations with respect to the network device operating within a test environment. Each of the simulation iterations may be configured to randomly assign parameters within the test environment. The driver may conduct, based on the configuration objects, each of the simulation iterations within the test environment to collect simulation datasets representative of operating states of the network device. The analytics module may perform machine learning with respect to each of the simulation datasets to generate a model that predicts, responsive to configuration parameters, an operating state of the network device when configured with the configuration parameters for the network device.
IPC codes: G06N  99/    00            A I
H04L  12/    24            A I
H04L  12/    26            A I
G06N  20/    20            A I
G06N   5/    00            A I
G06N   7/    00            A I
Claims: number: 1
text: A method of automating model creation, the method comprising:
number: 2
text: The method of claim 1,
wherein each of the plurality of simulation datasets include feature usage data indicative of operating states of the network device relative to the test environment, and resource utilization data indicative of resource utilization within the network device,
wherein the method further comprises further comprises identifying associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of plurality of simulation datasets, and
wherein performing the machine learning comprises performing machine learning with respect to the associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of plurality of simulation datasets.
number: 3
text: The method of claim 2,
wherein the feature usage data comprises one or more of the number of peers, the number of groups, the number of route instances, the number of customer edge (CE) devices, the number of CE interfaces, the number of routes, and the type for each of the routes, and
wherein the resource utilization data comprises one or more of the memory usage per routing daemon executed by the network device during each simulation, and the memory usage in the PFE of the network device during each simulation.
number: 4
text: The method of any combination of claims 2 and 3, wherein performing the machine learning comprises performing machine learning with respect to each of the associations to generate a memory forecast sub-model representative of the network device that predicts, responsive to the configuration parameters for the network device, the memory usage per routing daemon executed by the network device, and the memory usage in the PFE of the network device.
number: 5
text: The method of any combination of claims 2-4,
wherein the feature usage data includes one or more of a number of routes for each of a plurality of routing protocols,
wherein the resource utilization data comprises a route learning duration for each of the plurality of routing protocols,
wherein identifying the associations comprises identifying associations between the number of routes for each of the plurality of routing protocols and the route learning durations for the corresponding one of the plurality of routing protocols, and
wherein performing the machine learning comprises performing machine learning with respect to each of the associations to generate a route learning sub-model representative of the device that predicts, responsive to the configuration parameters for the network device, a duration taken to converge on a current configuration state.
number: 6
text: The method of any combination of claim 2-5,
wherein the feature usage data includes one or more of a number of peers, a number of groups, a number of route instances, a number of customer edge (CE) devices, a number of CE interfaces, a number of routes, and a type for each of the routes, and
wherein the resource utilization data comprises one or more of processor utilization per routing daemon executed by the network device, and processor utilization in a packet forwarding engine (PFE) of the network device,
wherein performing the machine learning comprises performing machine learning with respect to each of the associations to generate a processor forecast sub-model representative of the network device that predicts, responsive to the configuration parameters for the network device, the processor usage per routing daemon executed by the network device, and the processor usage in the PFE of the network device.
number: 7
text: The method of any combination of claims 1-6, further comprising:
number: 8
text: The method of any combination of claims 1-7, wherein each of the simulation configuration files include data for coherently configuring the test environment and the network device such that the network device operates consistent with the test environment to support one or more services.
number: 9
text: The method of claim 8, wherein the one or more services include a layer three virtual private network service.
number: 10
text: The method of any combination of claims 1-9, wherein the machine learning comprises Monte Carlo simulation style experimentation.
number: 11
text: A device comprising:
number: 12
text: The device of claim 11,
wherein each of the plurality of simulation datasets include feature usage data indicative of operating states of the network device relative to the test environment, and resource utilization data indicative of resource utilization within the network device,
wherein the one or more processors are further configured to execute a data platform configured to identify associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of plurality of simulation datasets, and
wherein the analytics module is configured to perform the machine learning with respect to the associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of plurality of simulation datasets.
number: 13
text: The device of claim 12,
wherein the feature usage data comprises one or more of the number of peers, the number of groups, the number of route instances, the number of customer edge (CE) devices, the number of CE interfaces, the number of routes, and the type for each of the routes, and
wherein the resource utilization data comprises one or more of the memory usage per routing daemon executed by the network device during each simulation, and the memory usage in the PFE of the network device during each simulation.
number: 14
text: The device of claim 11, further comprising means for performing each of the steps recited by the method recited by any combination of claims 2-10.
number: 15
text: A computer-readable storage medium encoded with instructions for causing one or more programmable processors to perform the method recited by any combination of claims 1-10.
Office action: search_report, dated 2019-08-21
Prior art cited: ref_id: US5907696A
ref_type: patent
severity: background
ref_id: US7975190B2
ref_type: patent
severity: background
ref_id: - STUCKEY N C ET AL: "Estimating Queue Size in a Computer Network Using an Extended Kalman Filter", AEROSPACE CONFERENCE, 2007 IEEE, IEEE, PISCATAWAY, NJ, USA, 3 March 2007 (2007-03-03), pages 1 - 12, XP031214119, ISBN: 978-1-4244-0524-4
ref_type: npl
severity: background
ref_id: US5907696
ref_type: patent
severity: background
ref_id: US7975190
ref_type: patent
severity: background
ref_id: - STUCKEY N C ET AL, "Estimating Queue Size in a Computer Network Using an Extended Kalman Filter", AEROSPACE CONFERENCE, 2007 IEEE, IEEE, PISCATAWAY, NJ, USA, (20070303), ISBN 978-1-4244-0524-4, pages 1 - 12, XP031214119 [I] 1-15 * figures 5,9,10,11,13,23 * * page  1 * * page  3 * * page  4  - page  6 * * page  10  - page  11 *
ref_type: npl
severity: background
Final disposition: pending (2019-08-21)