Intent-Based Workload Placement for 5G Cloud Network Functions
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Solution Overview
Problem
Existing 5G network technologies fail to optimally match service requests with the most suitable COTS hardware for processing, leading to inefficiencies in power, space, and cooling, while conventional approaches also neglect the specific requirements of different service types and network metrics.
Innovation Solution
Implementing a Proximity, Locality, Intent Optimized Workload Placement (PLIOP) application function (AF) with a tensor mediation plane (TMP) that uses logical and physical hardware awareness to dynamically allocate workloads based on service requirements, user plane information, and control plane awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional workload placement methods are used, then implementation simplicity is maintained, but service performance and network metrics are not optimized
Solution Approach 1:
The patent introduces a workload placement optimization function (WPOF) as an intermediary component that sits between the service requests and the COTS hardware resources. This WPOF acts as a mediator that receives service requests, evaluates them against multiple criteria (service requirements, user plane information, control plane awareness), and determines optimal hardware placement. This intermediary layer enables sophisticated optimization without requiring changes to the underlying hardware or service request structures, thus improving service performance while managing complexity through a dedicated optimization layer.
2Use of energy by stationary object
If workload is not optimally matched to hardware, then device complexity is reduced, but power consumption and space utilization increase
Solution Approach 1:
The patent applies local quality by evaluating and matching specific characteristics of service requests with corresponding characteristics of COTS hardware resources. Instead of uniform workload distribution, the system assesses local attributes such as service type requirements, hardware capabilities, and operational metrics to determine the most suitable placement. This localized matching ensures that each workload is processed by hardware best suited for its specific requirements, optimizing both power consumption and processing efficiency simultaneously.
Solution Approach 2:
The system dynamically changes operational parameters by continuously monitoring service requirements, user plane information, and control plane awareness. Based on these changing parameters, the workload placement optimization function adjusts the mapping between services and hardware resources in real-time. This dynamic parameter adjustment enables the system to respond to varying load conditions and hardware states, optimizing power consumption and processing efficiency adaptively rather than relying on static configurations.
3Productivity
If traditional service placement is used, then system complexity is minimized, but network efficiency and resource utilization deteriorate
Solution Approach 1:
The patent segments the workload placement optimization process into distinct functional components: service request evaluation, hardware resource assessment, matching algorithms, and placement determination. By dividing the complex optimization task into manageable segments, the system can implement sophisticated optimization logic without creating an unmanageably complex monolithic system. Each segment handles a specific aspect of the optimization, making the overall system more maintainable and easier to implement while achieving high network efficiency.
Data Source
AI summary
Techniques for packet flow description (PDF) management are disclosed. In one embodiment, a method is disclosed comprising obtaining, by an application function (AF), information about network functions (NFs) instantiated as virtual machines (VMs) on compute resources of a cloud platform, obtaining, by the AF, information about the compute resources, obtaining, by the AF, information about a plurality of UEs accessing the NFs, and using, by the AF, the information about the NFs, compute resources of the cloud platform and the plurality of UEs to make an assessment of a workload placement corresponding to the NFs instantiated on the compute resources of the cloud platform.