US-12619832-B2 - Natural language-based management of computing resources executing radio access network workloads

US12619832B2US 12619832 B2US12619832 B2US 12619832B2US-12619832-B2

Abstract

The techniques disclosed herein manage computing environments associated with radio access networks using a natural language interface. This is achieved through utilizing natural language processing to analyze user generated inputs and generate robust large language model queries. In various examples, the queries can include radio access network documentation, diagnostic data, and past interactions to provide custom context to the large language model. Accordingly, the query can cause the large language model to generate an operation sequence comprising a plurality of commands to interface with a resource management tool and control computing resources and supporting components. In this way, the present techniques can alleviate the technical burden on end users and minimize the risk of errors.

Inventors

Sanjeev Mehrotra
Anuj Kalia
Manikanta Kotaru

Assignees

MICROSOFT TECHNOLOGY LICENSING, LLC

Dates

Publication Date: 20260505
Application Date: 20230615

Claims (20)

1 . A method for translating a natural language input into an automated computing resource management task for a radio access network providing a telecommunications service, the method performed by a computing environment associated with the radio access network, the method comprising: receiving the natural language input defining a desired outcome for the computing environment associated with the radio access network; analyzing the natural language input utilizing a natural language processing function to detect the desired outcome for the computing environment associated with the radio access network; retrieving, based on the desired outcome, an auxiliary information set pertaining to the radio access network; generating, by the natural language processing function, a custom contextual query based on the desired outcome defined by the natural language input and the auxiliary information set; providing the custom contextual query to a large language model of the natural language processing function, wherein the custom contextual query causes the large language model to generate an operation sequence containing a plurality of commands defining the automated computing resource management task; and configuring the computing environment associated with the radio access network with the operation sequence generated by the large language model based on the desired outcome defined by the natural language input and the auxiliary information set, wherein the operation sequence causes the computing environment associated with the radio access network to perform the automated computing resource management task.
2 . The method of claim 1 , wherein the desired outcome defined by the natural language input comprises an implementation of networking functionality within the radio access network.
3 . The method of claim 2 , wherein the operation sequence causes the computing environment associated with the radio access network to retrieve and install a component facilitating the network functionality.
4 . The method of claim 1 , wherein the auxiliary information set comprises documentation that is specific to the computing environment associated with the radio access network.
5 . The method of claim 1 , wherein the auxiliary information set comprises diagnostic information that is retrieved in response to an event within the radio access network.
6 . The method of claim 1 , wherein the custom contextual query includes an intrinsic instruction for constraining a behavior of the large language model.
7 . The method of claim 1 , wherein the natural language input is a predetermined natural language input that is selected from a set of predetermined natural language inputs.
8 . A system for translating a natural language input into an automated computing resource management task for a radio access network providing a telecommunications service, the system comprising: a processing system; and a computer readable medium having encoded thereon computer readable instructions that when executed by the processing system cause the system to perform operations comprising: receiving the natural language input defining a desired outcome for the radio access network; analyzing the natural language input utilizing a natural language processing function; detecting, based on the analyzing, the desired outcome for the radio access network; retrieving, based on the desired outcome, an auxiliary information set pertaining to the radio access network; generating, by the natural language processing function, a custom contextual query based on the desired outcome defined by the natural language input and the auxiliary information set; providing the custom contextual query to a large language model of the natural language processing function, wherein the custom contextual query causes the large language model to generate an operation sequence containing a plurality of commands defining the automated computing resource management task; and executing the operation sequence generated by the large language model based on the desired outcome defined by the natural language input and the auxiliary information set, wherein execution of the operation sequence causes the radio access network to perform the automated computing resource management task.
9 . The system of claim 8 , wherein the desired outcome defined by the natural language input comprises an implementation of networking functionality within the radio access network.
10 . The system of claim 9 , wherein the operation sequence causes the system to retrieve and install a component facilitating the network functionality.
11 . The system of claim 8 , wherein the auxiliary information set comprises documentation that is specific to the radio access network.
12 . The system of claim 8 , wherein the auxiliary information set comprises diagnostic information that is retrieved in response to an event within the radio access network.
13 . The system of claim 8 , wherein the custom contextual query includes an intrinsic instruction for constraining a behavior of the large language model.
14 . The system of claim 8 , wherein the natural language input is a predetermined natural language input that is selected from a set of predetermined natural language inputs.
15 . A computer readable storage medium having encoded thereon computer readable instructions that, when executed by a system, cause the system to perform operations comprising: receiving a natural language input defining a desired outcome for a radio access network; analyzing the natural language input utilizing a natural language processing function; detecting, based on the analyzing, the desired outcome for the radio access network; retrieving, based on the desired outcome, an auxiliary information set pertaining to the radio access network; generating, by the natural language processing function, a custom contextual query based on the desired outcome defined by the natural language input and the auxiliary information set; providing the custom contextual query to a large language model of the natural language processing function, wherein the custom contextual query causes the large language model to generate an operation sequence containing a plurality of commands defining an automated computing resource management task; and executing the operation sequence generated by the large language model based on the desired outcome defined by the natural language input and the auxiliary information set, wherein execution of the operation sequence causes the radio access network to perform the automated computing resource management task.
16 . The computer readable storage medium of claim 15 , wherein the desired outcome defined by the natural language input comprises an implementation of networking functionality within the radio access network.
17 . The computer readable storage medium of claim 16 , wherein the operation sequence causes the system to retrieve and install a component facilitating the network functionality.
18 . The computer readable storage medium of claim 15 , wherein the auxiliary information set comprises documentation that is specific to the radio access network.
19 . The computer readable storage medium of claim 15 , wherein the auxiliary information set comprises diagnostic information that is retrieved in response to an event within the radio access network.
20 . The computer readable storage medium of claim 15 , wherein the custom contextual query includes an intrinsic instruction for constraining a behavior of the large language model.

Description

BACKGROUND As cloud computing rapidly gains popularity, more and more data and/or services are stored and/or provided online via network connections. Providing an optimal and reliable user experience is an important aspect for cloud service providers that offer network services. In many scenarios, a cloud service provider may provide a service to thousands or millions of users (e.g., customers, clients, etc.) geographically dispersed around a country, or even the world. In order to provide this service, a cloud service provider often utilizes different resources, such as server farms, hosted in various datacenters. Access to these resources is typically provided by a cloud platform which operates the datacenters. In addition, the service can be constructed of various software components such as virtual machines, containers, and requisite management infrastructure. These software components may be collectively referred to as a cluster. In recent years, a particular application space that has experienced the significant impact of cloud computing is radio access networks (RAN). Generally described, a radio access network is a component of a mobile telecommunication system that connects various devices (e.g., mobile phones, computers) to a core network (e.g., 5G, 4G LTE). Traditional radio access networks typically comprise many stand-alone base stations where each base station provides service to devices within a local geographical area. In addition, each base station possesses an individual set of resources (e.g., computing, cooling, power) to enable the base station to process and transmit its own signal to and from devices and forwards data payloads to the core network. Hence, the “cellular” nature of a cellular network. There are many well-known limitations of these traditional network architectures. Most prominently, the isolated nature of the base stations can give rise to subsequent drawbacks. For instance, due to limited availability in the frequency spectrum, different base stations oftentimes utilize the same frequencies which can lead to interference between base stations. This issue can be exacerbated when a network operator adds additional base stations to the network to increase capacity. In another example, base stations can be highly resource inefficient. Due to the mobile nature of network users, traffic at a given base station can fluctuate dramatically. However, average utilization across all base stations of a network can often be very low, only intermittently experiencing spikes in traffic. In addition, traditional base stations often lack the ability to share computing resources with other base stations. As such, individual base stations may typically be designed for worst-case scenario processing loads thereby leading to poor resource efficiency and increased operating costs. In contrast, cloud radio access networks (C-RANs), which can also be referred to as virtualized radio access networks (V-RAN) can leverage the computing power and flexibility of cloud platforms to virtualize radio access network functions. Consequently, cloud radio access networks can address many of the technical challenges facing traditional base station style radio access networks. For example, virtualizing functions that were previously performed by discrete computing devices enables the cloud radio access network to scale up and scale down available resources based on network conditions (e.g., traffic). In another example, centralizing network resources can streamline management and improve reliability. However, many existing tools for managing and orchestrating cloud computing resources such as Kubernetes may not have been designed with radio access network workloads in mind. As such, managing a cloud radio access network can be a highly complex task often requiring extensive manual customization. It is with respect to these and other considerations that the disclosure made herein is presented. SUMMARY The techniques disclosed herein enhance computing systems that execute radio access network (RAN) workloads through a natural language interface for managing computing resources. As mentioned above, radio access networks are components of a telecommunications system that connect devices such as mobile phones to the broader core network. While traditional systems utilized physical base stations to implement a radio access network, recent developments have seen rapid virtualization of radio access network functions using cloud computing infrastructure (e.g., a datacenter). However, managing computing resources for a cloud radio access network (C-RAN) can be a deeply complex task as many orchestration tools such as Kubernetes may not account for specific needs of radio access network workloads. For example, many default components can be ideal for standard web-based workloads in which computing resources can be freely enabled (e.g., scaled) and/or disabled (e.g., killed). In contrast, radio access network workloads