Patent classifications
H04L41/5054
Datapath load distribution for a RIC
To provide a low latency near RT RIC, some embodiments separate the RIC's functions into several different components that operate on different machines (e.g., execute on VMs or Pods) operating on the same host computer or different host computers. Some embodiments also provide high speed interfaces between these machines. Some or all of these interfaces operate in non-blocking, lockless manner in order to ensure that critical near RT RIC operations (e.g., datapath processes) are not delayed due to multiple requests causing one or more components to stall. In addition, each of these RIC components also has an internal architecture that is designed to operate in a non-blocking manner so that no one process of a component can block the operation of another process of the component. All of these low latency features allow the near RT RIC to serve as a high speed IO between the E2 nodes and the xApps.
Service assurance monitoring based on telemetry
Methods are provided for modifying assurance monitoring of a service based on operational states. The methods involve establishing, based on service configuration information, an assurance monitoring for a service provided by a plurality of network nodes that establish network connectivity for the service. The service includes a plurality of sub-services. The methods further involve obtaining, from the plurality of network nodes, telemetry data related to the service, determining one or more operational states of the plurality of network nodes based on the telemetry data, and modifying the assurance monitoring for the service based on the one or more operational states of the plurality of network nodes.
Selecting low priority pods for guaranteed runs
Service assurance is provided. A low priority pod corresponding to a low priority service in an orchestration platform that is to be evicted due to a predicted peak load period of a high priority service is identified based on analysis of historical and resource information. The low priority service corresponding to the low priority pod that is to be evicted due to the predicted peak load period of the high priority service is marked as an assured service for a guaranteed run in response to receiving an input from a user who was notified regarding eviction of the low priority pod. The low priority pod corresponding to the low priority service that is to be evicted due to the predicted peak load period of the high priority service is provisioned on a second host node prior to the eviction of the low priority pod from a first host node.
Dynamic Adjustment of Deployment Location of Software Within a Network
Optimizing a performance of a software function within a content delivery network, such as a software-implemented virtual cable modem termination system (CMTS) network, a virtualized Radio Access Network (vRAN), a Passive Optical Network (PON), or a Wi-Fi network. The performance may be optimized by dynamically changing a deployment location of a software function for a set of one or more users but not all users of a content delivery network from an original location to an updated location using an instance management platform. The deployment location may be dynamically changing in response to a variety of trigger conditions or concerns, such as but not limited to a difference in compute resources, responding to latency needs or tolerances, and a desired cohabitation of data or other software.
RESILIENT CONSENSUS-BASED CONTROL PLANE
Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.
RESILIENT CONSENSUS-BASED CONTROL PLANE
Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.
Task processing method applied to network topology, electronic device and storage medium
A task processing method includes: acquiring target data and target algorithm required by a target task to be executed; acquiring from a network topology at least one first-type node capable of providing the target data and at least one second-type node capable of executing the target algorithm; selecting, from the at least one first-type node, a node that provides one set of target data as a first target node, and selecting, from the at least one second-type node, a node that provides one set of target algorithm as a second target node; and controlling the second target node to process the target data in the first target node by using the target algorithm.
Multi-access edge computing low latency information services
A multi-access edge computing (MEC) platform may receive an indication that a user device has downloaded a MEC application client associated with a MEC application and may send, to the user device, instructions to install a device client. The device client may transmit device information associated with the user device to the MEC platform. The MEC platform may receive the device information associated with the user device and determine, based on the received device information, performance information associated with the MEC application.
System and method for managing clusters in an edge network
- Venkata Vamsi Krishna Kothuri ,
- Akshay Narayan Muramatti ,
- Anurag Choudhary ,
- Ashish Ramdas Mankar ,
- Nikita Vishwanath Shetty ,
- Sameer Narkhede ,
- Isha Singhal ,
- Matthew James Armstrong ,
- Prashant Batra ,
- Shi Shu ,
- Yiran Deng ,
- Zhuoran Li ,
- Mukesh Sohanlal Bafna ,
- Praveen Uday Bhaskara Pisipati ,
- Amarsinh Vijaysinh Patil ,
- Arvind Mohan
Various embodiments disclosed herein are related to an apparatus. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to collect, at a cloud server, service data from a collector framework service of an edge network. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to provide a configuration to the collector framework service based on the service data.
System and method for managing clusters in an edge network
- Venkata Vamsi Krishna Kothuri ,
- Akshay Narayan Muramatti ,
- Anurag Choudhary ,
- Ashish Ramdas Mankar ,
- Nikita Vishwanath Shetty ,
- Sameer Narkhede ,
- Isha Singhal ,
- Matthew James Armstrong ,
- Prashant Batra ,
- Shi Shu ,
- Yiran Deng ,
- Zhuoran Li ,
- Mukesh Sohanlal Bafna ,
- Praveen Uday Bhaskara Pisipati ,
- Amarsinh Vijaysinh Patil ,
- Arvind Mohan
Various embodiments disclosed herein are related to an apparatus. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to collect, at a cloud server, service data from a collector framework service of an edge network. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to provide a configuration to the collector framework service based on the service data.