DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 10/25/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-20 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12,149,964 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of U.S. Patent No. 12,149,964 teaches on each and every limitation of claims 1-20 of the Instant Application.
Instant Application
Parent/CON Application US 12,149,964 B1
1. A device, comprising:
one or more processors configured to:
monitor a set of local Key Performance Indicators ("KPIs") associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitor a set of application KPIs associated with the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
and perform a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs and the set of application KPIs,
wherein the central orchestrator performs, based on the application KPIs,
a second set of orchestration actions with respect the particular edge computing device.
1. A device, comprising: one or more processors configured to:
monitor a set of local Key Performance Indicators (“KPIs”) associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitor a set of application KPIs associated the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
maintain a set of orchestration models that each include: one or more conditions or criteria associated with the set of local KPIs, and orchestration actions to perform with respect to the particular edge computing device based on the conditions or criteria;
and perform a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs, the set of application KPIs, and the set of orchestration models,
wherein the central orchestrator identifies that the application KPIs are below a threshold and performs, based on identifying that the application KPIs are below the threshold,
a second set of orchestration actions with respect to one or more edge computing devices.
2. The device of claim 1, wherein the local KPIs include network information associated with a wireless network to which the particular edge computing device is communicatively coupled.
2. The device of claim 1, wherein the local KPIs include network information associated with a wireless network to which the particular edge computing device is communicatively coupled.
3. The device of claim 2, wherein the wireless network includes a radio access network ("RAN"), wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
3. The device of claim 2, wherein the wireless network includes a radio access network (“RAN”), wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
4. The device of claim 1, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
4. The device of claim 1, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
5. The device of claim 1, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
5. The device of claim 1, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
6. The device of claim 5, wherein the one or more processors are further configured to:
perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
6. The device of claim 5, wherein the one or more processors are further configured to:
perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
7. The device of claim 1, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
7. The device of claim 1, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
8. A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:
monitor a set of local Key Performance Indicators ("KPIs") associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitor a set of application KPIs associated with the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
and perform a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs and the set of application KPIs,
wherein the central orchestrator performs, based on the application KPIs,
a second set of orchestration actions with respect the particular edge computing device.
8. A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:
monitor a set of local Key Performance Indicators (“KPIs”) associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitor a set of application KPIs associated the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
maintain a set of orchestration models that each include: one or more conditions or criteria associated with the set of local KPIs, and orchestration actions to perform with respect to the particular edge computing device based on the conditions or criteria;
and perform a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs, the set of application KPIs,
and the set of orchestration models,
wherein the central orchestrator identifies that the application KPIs are below a threshold and performs, based on identifying that the application KPIs are below the threshold,
a second set of orchestration actions with respect to one or more edge computing devices.
9. The non-transitory computer-readable medium of claim 8, wherein the local KPIs include network information associated with a wireless network to which the particular edge computing device is communicatively coupled.
9. The non-transitory computer-readable medium of claim 8, wherein the local KPIs include network information associated with a wireless network to which the particular edge computing device is communicatively coupled.
10. The non-transitory computer-readable medium of claim 9, wherein the wireless network includes a radio access network ("RAN"), wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
10. The non-transitory computer-readable medium of claim 9, wherein the wireless network includes a radio access network (“RAN”), wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
11. The non-transitory computer-readable medium of claim 8, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
11. The non-transitory computer-readable medium of claim 8, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
12. The non-transitory computer-readable medium of claim 8, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
12. The non-transitory computer-readable medium of claim 8, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
13. The non-transitory computer-readable medium of claim 12, wherein the plurality of processor-executable instructions further include processor-executable instructions to:
perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
13. The non-transitory computer-readable medium of claim 12, wherein the plurality of processor-executable instructions further include processor-executable instructions to:
perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
14. The non-transitory computer-readable medium of claim 8, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
14. The non-transitory computer-readable medium of claim 8, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
15. A method, comprising:
monitoring a set of local Key Performance Indicators ("KPIs") associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitoring a set of application KPIs associated with the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
and performing a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs and the set of application KPIs,
wherein the central orchestrator performs, based on the application KPIs,
a second set of orchestration actions with respect the particular edge computing device.
15. A method, comprising:
monitoring a set of local Key Performance Indicators (“KPIs”) associated with a particular edge computing device, wherein the particular edge computing device implements a particular set of applications, wherein the local KPIs are not provided to a central orchestrator that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices;
monitoring a set of application KPIs associated the particular set of applications, wherein the application KPIs are provided to the central orchestrator;
maintaining a set of orchestration models that each include: one or more conditions or criteria associated with the set of local KPIs, and orchestration actions to perform with respect to the particular edge computing device based on the conditions or criteria;
and performing a first set of orchestration actions with respect to the particular edge computing device based on the set of local KPIs, the set of application KPIs,
and the set of orchestration models, wherein the central orchestrator identifies that the application KPIs are below a threshold and performs, based on identifying that the application KPIs are below the threshold,
a second set of orchestration actions with respect to one or more edge computing devices.
16. The method of claim 15, wherein the local KPIs include network information associated with a radio access network ("RAN") to which the particular edge computing device is communicatively coupled, wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
16. The method of claim 15, wherein the local KPIs include network information associated with a radio access network (“RAN”) to which the particular edge computing device is communicatively coupled, wherein the local KPIs include at least one of:
load metrics associated with the RAN, or
channel quality information associated with the RAN.
17. The method of claim 15, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
17. The method of claim 15, wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of:
increasing an amount of resources associated with one or more containers that implement the particular set of applications, or
instantiating one or more additional containers to implement the particular set of applications.
18. The method of claim 15, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
18. The method of claim 15, wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device.
19. The method of claim 18, further comprising:
performing a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
19. The method of claim 18, further comprising:
perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications.
20. The method of claim 15, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
20. The method of claim 15, wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications.
Claim Objections
Claim(s) 1-20 is/are objected to because of the following informalities:
Claim 1 recites “the local KPIs” in line 5 and “the application KPIs” in lines 9 & 12. These should read “the set of local KPIs” and “the set of application KPIs”. This also applies to independent claims 8, 15 and dependent claims 2-3, 9-10, 16.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0014113 A1 (Guim Bernat).
Regarding Claim 1:
Guim Bernat teaches A device (Fig 5, edge nodes 522, 524), comprising: one or more processors ([0108] the local processor of the MC 820 may be capable of performing one or more of the functions of the compute circuitry 802 described herein.) configured to:
monitor a set of local Key Performance Indicators ("KPIs") (ie. resource information) associated with a particular edge computing device (ie. secondary orchestrators/mesh of orchestrator edge devices), wherein the particular edge computing device implements a particular set of applications (ie. services), ([0081] The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications. [0177] orchestrated delegated 1216-1218 implements means for monitoring.) Secondary orchestrators manage edge device services/monitoring. Fig 10-12.
wherein the set of local KPIs (ie. resource information) are not provided to a central orchestrator (primary or edge orchestrator 1010) that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices; ([0081] With the use of container pods, a pod controller oversees the partitioning and allocation of containers and resources. The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0150] The combination of the primary orchestrator 1010 and secondary orchestrators 1020-1022 enables self-management and orchestration across hardware and software resources. [0166] The orchestrators 1020-1024 form a mesh of orchestrators 1020-1024 in which each orchestrator 1020-1024 can relay services and/or associated data from one orchestrator 1020-1024 to another orchestrator 1020-1024 and can share in the responsibility to execute services in accordance with defined SLO, SLA, and/or other requirement/obligation. A primary or edge orchestrator 1010 can coordinate operation among the secondary orchestrators 1020-1024 and accommodate for faults, deficiencies, allocation issues, bandwidth, etc., by triggering allocation to a different orchestrator 1020-1024. In some examples, however, the secondary orchestrators 1020-1024 communicate among themselves ( e.g., via the orchestration delegates 1216-1218) to migrate a service, reallocate resources, share SLO/SLA responsibility, etc., without the edge orchestrator 1010.)
monitor a set of application KPIs (ie. service SLAs) associated with the particular set of applications, wherein the set of application KPIs are provided to the central orchestrator (ie. edge orchestrator/primary orchestrator); ([0143] An edge orchestrator (referred to as a primary orchestrator) coordinates among the local orchestrators to manage services across a plurality of edge tiers 911-919. As such, the edge orchestrator provides edge-to-edge (e2e) orchestration among the local orchestrators. [0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. )
and perform a first set of orchestration actions (ie. local control and orchestration) with respect to the particular edge computing device based on the set of local KPIs (ie. resource information) and the set of application KPIs (ie. service SLAs), ([0080] In a setting that uses one or more container pods, a pod controller or orchestrator is responsible for local control and orchestration of the containers in the pod. Various edge node resources (e.g., storage, compute, services, depicted with hexagons) provided for the respective edge slices 532, 534 are partitioned according to the needs of each container. [0081] The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0082] If each tenant specific pod has a tenant specific pod controller, there will be a shared pod controller that consolidates resource allocation requests to avoid typical resource starvation situations.)
wherein the central orchestrator (primary or edge orchestrator 1010) performs, based on the set of application KPIs (ie. service SLAs), a second set of orchestration actions (ie. sending instructions, migrate a service) with respect to the particular edge computing device. ([0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. ))
Regarding Claim 8:
Guim Bernat teaches A non-transitory computer-readable medium, storing a plurality of processor-executable instructions ([0130] In further examples, a machine-readable medium also includes any tangible medium that is capable of storing, encoding or carrying instructions for execution by a machine and that cause the machine to perform any one or more of the methodologies of the present disclosure) to:
monitor a set of local Key Performance Indicators ("KPIs") (ie. resource information) associated with a particular edge computing device (ie. secondary orchestrators/mesh of orchestrator edge devices), wherein the particular edge computing device implements a particular set of applications (ie. services), ([0081] The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications. [0177] orchestrated delegated 1216-1218 implements means for monitoring.) Secondary orchestrators manage edge device services/monitoring. Fig 10-12.
wherein the set of local KPIs (ie. resource information) are not provided to a central orchestrator (primary or edge orchestrator 1010) that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices; ([0081] With the use of container pods, a pod controller oversees the partitioning and allocation of containers and resources. The pod controller receives instructions from an orchestrator ( e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0150] The combination of the primary orchestrator 1010 and secondary orchestrators 1020-1022 enables self-management and orchestration across hardware and software resources. [0166] The orchestrators 1020-1024 form a mesh of orchestrators 1020-1024 in which each orchestrator 1020-1024 can relay services and/or associated data from one orchestrator 1020-1024 to another orchestrator 1020-1024 and can share in the responsibility to execute services in accordance with defined SLO, SLA, and/or other requirement/obligation. A primary or edge orchestrator 1010 can coordinate operation among the secondary orchestrators 1020-1024 and accommodate for faults, deficiencies, allocation issues, bandwidth, etc., by triggering allocation to a different orchestrator 1020-1024. In some examples, however, the secondary orchestrators 1020-1024 communicate among themselves ( e.g., via the orchestration delegates 1216-1218) to migrate a service, reallocate resources, share SLO/SLA responsibility, etc., without the edge orchestrator 1010.)
monitor a set of application KPIs (ie. service SLAs) associated with the particular set of applications, wherein the set of application KPIs are provided to the central orchestrator (ie. edge orchestrator/primary orchestrator); ([0143] An edge orchestrator (referred to as a primary orchestrator) coordinates among the local orchestrators to manage services across a plurality of edge tiers 911-919. As such, the edge orchestrator provides edge-to-edge (e2e) orchestration among the local orchestrators. [0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. )
and perform a first set of orchestration actions (ie. local control and orchestration) with respect to the particular edge computing device based on the set of local KPIs (ie. resource information) and the set of application KPIs (ie. service SLAs), ([0080] In a setting that uses one or more container pods, a pod controller or orchestrator is responsible for local control and orchestration of the containers in the pod. Various edge node resources (e.g., storage, compute, services, depicted with hexagons) provided for the respective edge slices 532, 534 are partitioned according to the needs of each container. [0081] The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0082] If each tenant specific pod has a tenant specific pod controller, there will be a shared pod controller that consolidates resource allocation requests to avoid typical resource starvation situations.)
wherein the central orchestrator (primary or edge orchestrator 1010) performs, based on the set of application KPIs (ie. service SLAs), a second set of orchestration actions (ie. sending instructions, migrate a service) with respect to the particular edge computing device. ([0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. )
Regarding Claim 15:
Guim Bernat teaches A method, comprising:
monitoring a set of local Key Performance Indicators ("KPIs") (ie. resource information) associated with a particular edge computing device (ie. secondary orchestrators/mesh of orchestrator edge devices), wherein the particular edge computing device implements a particular set of applications (ie. services), ([0081] The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications. [0177] orchestrated delegated 1216-1218 implements means for monitoring.) Secondary orchestrators manage edge device services/monitoring. Fig 10-12.
wherein the set of local KPIs (ie. resource information) are not provided to a central orchestrator (primary or edge orchestrator 1010) that is communicatively coupled to the particular edge computing device and a plurality of other edge computing devices; ([0081] With the use of container pods, a pod controller oversees the partitioning and allocation of containers and resources. The pod controller receives instructions from an orchestrator ( e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0150] The combination of the primary orchestrator 1010 and secondary orchestrators 1020-1022 enables self-management and orchestration across hardware and software resources. [0166] The orchestrators 1020-1024 form a mesh of orchestrators 1020-1024 in which each orchestrator 1020-1024 can relay services and/or associated data from one orchestrator 1020-1024 to another orchestrator 1020-1024 and can share in the responsibility to execute services in accordance with defined SLO, SLA, and/or other requirement/obligation. A primary or edge orchestrator 1010 can coordinate operation among the secondary orchestrators 1020-1024 and accommodate for faults, deficiencies, allocation issues, bandwidth, etc., by triggering allocation to a different orchestrator 1020-1024. In some examples, however, the secondary orchestrators 1020-1024 communicate among themselves ( e.g., via the orchestration delegates 1216-1218) to migrate a service, reallocate resources, share SLO/SLA responsibility, etc., without the edge orchestrator 1010.)
monitoring a set of application KPIs (ie. service SLAs) associated with the particular set of applications, wherein the set of application KPIs are provided to the central orchestrator (ie. edge orchestrator/primary orchestrator); ([0143] An edge orchestrator (referred to as a primary orchestrator) coordinates among the local orchestrators to manage services across a plurality of edge tiers 911-919. As such, the edge orchestrator provides edge-to-edge (e2e) orchestration among the local orchestrators. [0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts.)
and performing a first set of orchestration actions (ie. local control and orchestration) with respect to the particular edge computing device based on the set of local KPIs (ie. resource information) and the set of application KPIs (ie. service SLAs), ([0080] In a setting that uses one or more container pods, a pod controller or orchestrator is responsible for local control and orchestration of the containers in the pod. Various edge node resources (e.g., storage, compute, services, depicted with hexagons) provided for the respective edge slices 532, 534 are partitioned according to the needs of each container. [0081] The pod controller determines which container requires which resources and for how long in order to complete the workload and satisfy the SLA. [0082] If each tenant specific pod has a tenant specific pod controller, there will be a shared pod controller that consolidates resource allocation requests to avoid typical resource starvation situations.)
wherein the central orchestrator (primary or edge orchestrator 1010) performs, based on the set of application KPIs (ie. service SLAs), a second set of orchestration actions (ie. sending instructions, migrate a service) with respect to the particular edge computing device. ([0144] For example, the edge orchestrator can instantiate and allocate resources in a selected edge location to execute a service with a particular SLA and particular cost. The edge orchestrator can monitor SLAs associated with an application during execution of the application and compare a monitored SLA with a provided SLA, for example. The edge orchestrator can migrate a service from one edge location to another edge location based on one or more criterion and/or factors such as additional resources to be used to satisfy the SLA. [0081] The pod controller receives instructions from an orchestrator (e.g., the orchestrator 560) that instructs the controller on how best to partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. )
Regarding Claims 2, 9:
Guim Bernat teaches on the inventions of claims 1, 8 as described.
Guim Bernat teaches wherein the set of local KPIs include network information associated with a wireless network to which the particular edge computing device is communicatively coupled. ([0058] The edge environment 110 is formed from network components and functional features operated by and within the edge services 135 (e.g., the orchestrator 142, the edge node 148, etc.). The edge environment 110 can be implemented as any type of network that provides edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are shown in FIG. 1. Example use cases, KPIs: [0136]-[0143] For example, in a small cell tier 911, with a round-trip network latency 920 of less than one millisecond (ms), available compute power is less than 500 Watts (W); the form factor is a small box; thermals are ambient; there is no physical surveillance for security; and management is remote. Use cases for the example small cell tier 911 include smart cities, vehicle-to-vehicle (V2V), retail, video analytics, etc. Key performance indicators (KPIs) associated with these example use cases include data privacy, backhaul traffic savings, reliability, latency, etc.)
Regarding Claims 3, 10:
Guim Bernat teaches on the inventions of claims 2, 9 as described.
Guim Bernat teaches wherein the wireless network includes a radio access network ("RAN"), wherein the set of local KPIs include at least one of: load metrics associated with the RAN, or channel quality information associated with the RAN. ([0058] The edge environment 110 is formed from network components and functional features operated by and within the edge services 135 (e.g., the orchestrator 142, the edge node 148, etc.). The edge environment 110 can be implemented as any type of network that provides edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are shown in FIG. 1. Example use cases, KPIs: [0136]-[0143] For example, in a small cell tier 911, with a round-trip network latency 920 of less than one millisecond (ms), available compute power is less than 500 Watts (W); the form factor is a small box; thermals are ambient; there is no physical surveillance for security; and management is remote. Use cases for the example small cell tier 911 include smart cities, vehicle-to-vehicle (V2V), retail, video analytics, etc. Key performance indicators (KPIs) associated with these example use cases include data privacy, backhaul traffic savings, reliability, latency, etc.)
Regarding Claims 4, 11, 17:
Guim Bernat teaches on the inventions of claims 1, 8, 15 as described.
Guim Bernat teaches wherein performing the first set of orchestration actions includes:
allocating a set of resources of the edge computing device for the particular set of applications, wherein allocating the set of resources includes at least one of: increasing an amount of resources associated with one or more containers that implement the particular set of applications, or instantiating one or more additional containers to implement the particular set of applications. ([0073]-[0077] provisioning of resources. [0171] In certain examples, remediation of problems in the fog 1300, on the edge 1100, 1200, in the cloud, etc., can be automated using the orchestrators 1010, 1020-1024. Tracing of software execution can be used to identify and remediate an issue with execution of a service, for example. Allocated memory can also be increased or decreased depending upon remediation workflow, for example.)
Regarding Claims 5, 12, 18:
Guim Bernat teaches on the inventions of claims 1, 8, 15 as described.
Guim Bernat teaches wherein the second set of orchestration actions includes provisioning additional resources, of a set of hardware resources on which the particular edge computing device is implemented, for the particular edge computing device. ([0150] FIG. 11, the primary orchestrator 1010 can communicate with hardware and/or software resources of the edge entity 1030, 1032 via the secondary orchestrators 1020, 1022. The primary orchestrator 1010 allows consolidation of multiple services for multiple tenants to provide dynamic, adaptive SLA/SLO assurance with improved security. The combination of the primary orchestrator 1010 and secondary orchestrators 1020-1022 enables self-management and orchestration across hardware and software resources. [0171] In certain examples, remediation of problems in the fog 1300, on the edge 1100, 1200, in the cloud, etc., can be automated using the orchestrators 1010, 1020-1024. Tracing of software execution can be used to identify and remediate an issue with execution of a service, for example. Allocated memory can also be increased or decreased depending upon remediation workflow, for example.)
Regarding Claims 6, 13, 19:
Guim Bernat teaches on the inventions of claims 5, 12, 18 as described.
Guim Bernat teaches perform a third set of orchestration actions, after the additional resources have been provisioned, wherein the third set of orchestration actions includes allocating at least a portion of the additional resources for the particular set of applications. ([0073]-[0077] provisioning of resources. [0171] In certain examples, remediation of problems in the fog 1300, on the edge 1100, 1200, in the cloud, etc., can be automated using the orchestrators 1010, 1020-1024. Tracing of software execution can be used to identify and remediate an issue with execution of a service, for example. Allocated memory can also be increased or decreased depending upon remediation workflow, for example.)
Regarding Claims 7, 14, 20:
Guim Bernat teaches on the inventions of claims 1, 8, 15 as described.
Guim Bernat teaches wherein the particular edge computing device is a first edge computing device, wherein the second set of orchestration actions includes configuring at least a second edge computing device to implement the particular set of applications. ([0150] FIG. 11, the primary orchestrator 1010 can communicate with hardware and/or software resources of the edge entity 1030, 1032 via the secondary orchestrators 1020, 1022. The primary orchestrator 1010 allows consolidation of multiple services for multiple tenants to provide dynamic, adaptive SLA/SLO assurance with improved security. The combination of the primary orchestrator 1010 and secondary orchestrators 1020-1022 enables self-management and orchestration across hardware and software resources. [0171] In certain examples, remediation of problems in the fog 1300, on the edge 1100, 1200, in the cloud, etc., can be automated using the orchestrators 1010, 1020-1024. Tracing of software execution can be used to identify and remediate an issue with execution of a service, for example. Allocated memory can also be increased or decreased depending upon remediation workflow, for example.)
Regarding Claim 16:
Guim Bernat teaches on the invention of claim 15 as described.
Guim Bernat teaches wherein the local KPIs include network information associated with a radio access network ("RAN") to which the particular edge computing device is communicatively coupled, wherein the local KPIs include at least one of: load metrics associated with the RAN, or channel quality information associated with the RAN. ([0058] The edge environment 110 is formed from network components and functional features operated by and within the edge services 135 (e.g., the orchestrator 142, the edge node 148, etc.). The edge environment 110 can be implemented as any type of network that provides edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are shown in FIG. 1. Example use cases, KPIs: [0136]-[0143] For example, in a small cell tier 911, with a round-trip network latency 920 of less than one millisecond (ms), available compute power is less than 500 Watts (W); the form factor is a small box; thermals are ambient; there is no physical surveillance for security; and management is remote. Use cases for the example small cell tier 911 include smart cities, vehicle-to-vehicle (V2V), retail, video analytics, etc. Key performance indicators (KPIs) associated with these example use cases include data privacy, backhaul traffic savings, reliability, latency, etc.)
Conclusion & Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL J HACKENBERG whose telephone number is (571)272-5417. The examiner can normally be reached 9am-5pm M-F.
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/RACHEL J HACKENBERG/Primary Examiner, Art Unit 2454
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