Multimodal Service Flow Coordination via Correlation Identifiers
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Solution Overview
Problem
Current communication networks face challenges in coordinating and managing multiple correlated data traffic flows in multimodal service flows, leading to inefficiencies in resource allocation and increased latency, particularly in scenarios involving IoT and machine-type communications.
Innovation Solution
The method involves using a service correlation identifier to manage and prioritize data traffic flows, allowing for customizable synchronization and resource allocation based on assistance parameters that indicate which flows are correlated, mandatory, or dependent, enabling efficient handling of interruptions and handover procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple correlated data traffic flows are managed without service correlation identifiers, then network simplicity is maintained, but coordination efficiency and resource allocation performance deteriorate
Solution Approach 1:
The patent segments the management of data traffic flows by introducing service correlation identifiers that group related flows together. Each identifier acts as a segmentation key that allows the network to independently manage and coordinate specific groups of correlated flows without affecting other flows, thereby improving coordination efficiency while maintaining manageable complexity through organized segmentation.
Solution Approach 2:
The service correlation identifier acts as an intermediary element between multiple data traffic flows and the network management system. This intermediary enables efficient coordination by providing a centralized reference point that links correlated flows, allowing the network to manage resource allocation and synchronization without direct complex interactions between individual flows.
2Reliability
If all data traffic flows are treated with equal priority, then fairness is maintained, but latency increases during network disruptions
Solution Approach 1:
The patent applies local quality by assigning different priority levels to different data traffic flows based on their correlation and service requirements. Instead of uniform treatment, each flow or group of correlated flows receives localized priority assignment, ensuring that critical services maintain higher reliability during network disruptions while less critical flows can be deprioritized, thereby reducing overall latency.
Solution Approach 2:
The patent changes the priority parameter of data traffic flows dynamically based on service correlation identifiers and network conditions. During normal operation, flows may operate with standard priorities, but during network disruptions, the system adjusts priority parameters to ensure critical correlated flows maintain service reliability while non-critical flows are temporarily deprioritized to reduce congestion and latency.
3Productivity
If resource allocation is performed without flow correlation information, then allocation speed is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by establishing service correlation identifiers and associating them with data traffic flows before resource allocation decisions are needed. This advance organization of flow information allows the network to quickly identify and allocate resources to correlated flows as a group during resource allocation events, improving resource utilization efficiency without requiring complex real-time analysis of flow relationships.
Data Source
AI summary
Techniques for enhancing multimodal service flow communications within communication networks are discussed. In an example embodiment, an apparatus is configured to receive one or more connection messages for one or more user devices. The apparatus is further configured to manage one or more data traffic flows associated with the one or more user devices based at least in part on a received service correlation identifier.