LAN Task Offload via Arbitration to Reduce Latency
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Solution Overview
Problem
Existing technologies face challenges in efficiently offloading machine learning (ML)-based tasks within a local area network (LAN) due to limited computing resources on individual devices and concerns about security, privacy, and transmission latency when offloading tasks to remote devices.
Innovation Solution
A method is disclosed for a LAN system where a first device determines the need for an ML-based task and broadcasts a task request to a separate set of devices. These devices perform an arbitration process to select a second device with favorable computing resource availability, which then performs the ML-based task and transmits the output back to the first device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If ML-based tasks are offloaded to remote devices, then computing resource availability is improved, but transmission latency and security risks increase
Solution Approach 1:
The patent introduces a local area network (LAN) as an intermediary between the first device and potential computing resources. Instead of directly offloading to remote devices, the system uses local devices within the LAN as intermediaries to perform ML tasks, thereby reducing transmission latency while maintaining security through the arbitration mechanism that selects appropriate local devices based on computing resource availability.
Solution Approach 2:
The patent transitions from a single-dimension remote offloading approach to a multi-dimensional local distribution approach. By utilizing multiple devices within the local network space, the system creates additional dimensions for task execution, allowing simultaneous evaluation of multiple local devices' computing resources rather than relying on a single remote device.
2Power
If ML-based tasks are offloaded to remote devices, then computing power is improved, but data security and privacy concerns worsen
Solution Approach 1:
The patent applies local quality by selecting specific local devices within the LAN that have appropriate computing resources and security clearances to handle particular ML tasks. Instead of uniformly offloading to any remote device, the system evaluates and selects devices based on their local characteristics including computing power, security policies, and task compatibility, thereby maintaining data security while utilizing distributed computing power.
Solution Approach 2:
The arbitration process acts as a security intermediary that mediates between the first device and potential executing devices. This intermediary layer verifies computing resource availability and security requirements before task offloading, ensuring that data security and privacy concerns are addressed while still accessing the needed computing power from appropriate local devices.
3Reliability
If individual devices perform ML tasks locally, then data security is improved, but device complexity increases
Solution Approach 1:
The patent makes local devices universal by enabling them to serve multiple functions: acting as end devices, potential task execution devices, and arbitration participants. Each device in the LAN is equipped with the capability to perform ML tasks and participate in the arbitration process, creating a multi-functional system where devices can dynamically switch roles based on task requirements and resource availability, thereby maintaining security without permanently increasing individual device complexity.
4Loss of time
If local devices are used for ML task execution, then transmission latency is reduced, but device complexity increases
Solution Approach 1:
The arbitration process is self-service in that devices autonomously evaluate their own computing resource availability and participate in the selection process without requiring centralized control. Each device monitors its own resources and communicates availability status to the network, enabling automatic task distribution based on current resource states. This self-service approach reduces transmission latency for arbitration while avoiding the complexity of centralized resource management systems.
Data Source
AI summary
In one aspect, disclosed in a method for use in connection with a local area network (LAN) system comprising a group of multiple devices that includes a first device and a separate set of devices. The method includes: the first device determining that a machine learning (ML)-based task is to be performed; the first device broadcasting to the separate set of devices, a ML-based task request for the ML-based task, (i) wherein the separate set of devices are configured to perform an arbitration process to select a second device, and (ii) wherein the second device is configured to perform the ML-based task in accordance with the ML-based task request, thereby generating ML-based task output, and to transmit the generated ML-based task output to the first device; and the first device receiving the generated output from the second device and using the received output to facilitate performing one or more operations.