WTRU PDU Session Management for Federated ML
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Solution Overview
Problem
Current communication networks face challenges in optimizing network resource utilization for federated machine learning operations, particularly due to bursty traffic patterns and deadlines for flow completion time, which can lead to resource wastage and energy consumption issues.
Innovation Solution
The proposed solution involves a WTRU architecture that includes a Predictor Engine (PE) to accurately predict packet delays and a PDU Session Modifier (PSM) to dynamically manage PDU Sessions, ensuring optimal resource allocation and preventing resource wastage in federated machine learning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated for federated machine learning operations, then service quality for FL tasks is improved, but resource wastage increases due to bursty traffic patterns and missed deadlines
Solution Approach 1:
The system performs preliminary actions by proactively identifying and suspending PDU sessions that are at risk of missing deadlines before actual resource wastage occurs. The PDU Session Modifier monitors session states and takes preventive suspension actions based on predicted completion times, avoiding the waste of allocating resources to sessions that cannot meet their deadlines.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring PDU session states, calculating predicted flow completion times, and adjusting session management decisions based on this feedback. The network entity receives status information from WTRUs and uses this feedback to make informed decisions about suspending or maintaining PDU sessions, optimizing resource utilization dynamically.
2Ease of operation
If PDU sessions are maintained for potential FL operations, then service availability is improved, but network resources are wasted on sessions that miss deadlines
Solution Approach 1:
The system applies dynamics by making PDU session management flexible and adaptive rather than static. The PDU Session Modifier dynamically adjusts session states based on real-time conditions, suspending sessions that are unlikely to meet deadlines while maintaining sessions that can succeed. This dynamic approach optimizes both service availability and resource utilization by adapting to changing network and task conditions.
Solution Approach 2:
The system changes key parameters such as PDU session state (active/suspended), resource allocation levels, and session priority based on predicted completion times and deadline requirements. By adjusting these parameters dynamically, the system maintains service availability for viable FL operations while releasing resources from sessions that cannot meet their deadlines.
3Power
If computational resources are allocated at WTRU for AI/ML tasks, then processing capability is improved, but energy consumption increases and battery life decreases
Solution Approach 1:
The system performs preliminary assessment of task completion feasibility before full resource allocation. The PDU Session Modifier and Predictor Engine evaluate whether a PDU session can meet its deadline based on current progress and network conditions, suspending sessions early if completion is unlikely. This prevents wasteful energy consumption on computational tasks that cannot be completed in time.
Solution Approach 2:
The WTRU autonomously monitors its own PDU session states and makes decisions about suspending or continuing local AI/ML processing based on predicted completion times. This self-service mechanism allows the device to optimize its own energy consumption by stopping computations for sessions that cannot meet deadlines, extending battery life without requiring constant network control.
Data Source
AI summary
A method implemented in a Wireless Transmit Receive Unit (WTRU) for controlling resource usage associated with delivery of training results for a machine learning (ML) operation includes receiving one-way packet delay measurements between the WTRU and an Application Server or Application Function (AS/AF), predicting one or more upcoming one-way packet delays using the one-way packet delay measurements, determining whether the WTRU is capable of processing and delivering training results of the ML operation to the AS/AF within a specified time period based on the predicted upcoming one-way packet delays, and initiating one of a packet data unit session (PDU) release procedure or a PDU session modification procedure.