Edge Resource Orchestration for Low-Latency Cloud-Edge Task Scheduling
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in managing edge compute resources due to challenges such as device mobility, channel variability, and reliance on unreliable cloud services, leading to performance inconsistencies and increased latency.
Innovation Solution
Implementing intelligent cloud-edge resource management through edge compute resource orchestration, where end devices and edge nodes dynamically schedule and distribute processing tasks based on network link information, considering factors like quality of service, compute resource availability, and device capabilities to enhance efficiency and resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If processing tasks are centralized in cloud data centers, then compute resource capacity is improved, but network latency and backhaul costs increase
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes deployed throughout the network. Instead of one centralized data center, multiple edge nodes are positioned closer to end devices, dividing the compute workload across geographically distributed locations. This segmentation reduces the distance data must travel, lowering network latency while maintaining aggregate compute capacity.
Solution Approach 2:
The patent introduces a spatial dimension to computing resource deployment by placing edge compute nodes at multiple geographic locations within the network infrastructure. This transforms the single-point cloud architecture into a multi-dimensional distributed architecture, where compute resources exist across different spatial positions, enabling local processing that reduces backhaul requirements and latency.
2Loss of time
If edge compute nodes are deployed throughout the network, then network latency is reduced, but system complexity increases
Solution Approach 1:
The patent creates a universal orchestration framework that manages multiple edge compute nodes through standardized interfaces and protocols. This universal management layer handles task distribution, resource allocation, and coordination across diverse edge nodes, reducing the operational complexity that would otherwise arise from managing numerous distributed components.
Solution Approach 2:
The patent introduces an intermediary orchestration layer between end devices and edge compute nodes. This intermediary manages the complexity of distributed edge computing by handling task routing, resource matching, and coordination, allowing edge nodes to be deployed throughout the network without proportionally increasing system management complexity.
3Reliability
If edge compute resources are distributed across multiple nodes, then network availability is improved, but task orchestration complexity increases
Solution Approach 1:
The patent implements preliminary action through pre-computation and caching at edge nodes. Tasks and data are prepared in advance at distributed edge locations, so when requests arrive, processing can begin immediately without complex real-time coordination. This pre-positioning of resources and data improves availability while reducing orchestration complexity during active operations.
Solution Approach 2:
The patent employs feedback mechanisms where edge compute nodes report their status, capacity, and performance metrics to the orchestration system. This feedback enables dynamic task allocation and load balancing across distributed nodes, improving overall system availability while the automated feedback loop manages orchestration complexity through real-time adaptation rather than complex static planning.
Data Source
AI summary
This disclosure provides systems, methods and apparatuses for intelligent cloud-edge resource management. An end device may provide edge nodes of an edge computing system with network link information, which may enable the edge nodes to schedule and distribute task workloads more effectively, providing greater performance, power, security, and mission-critical network availability. For example, if the end device transmits a processing task request to a first edge node, the first edge node may assign the processing task to a second edge node according to the network link information. Additionally, or alternatively, the end device may transmit an indication of processing task parameters to one or more edge nodes and may receive an indication of an estimated completion time of the processing task from the one or more edge nodes. Accordingly, the end device may assign the processing task to an edge node with the lowest completion time.