Adaptive Compute Distribution in Collaboration Mesh Networks
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Solution Overview
Problem
Current collaboration session management technologies face inefficiencies in dynamically distributing compute resources and tasks among multiple participants, leading to suboptimal resource utilization and increased power consumption.
Innovation Solution
A method that involves a session initiator collecting local mesh-pertinent state, transmitting probe requests to session peers, and using multi-user constrained optimization to obtain and deploy an initial mesh configuration for adaptive compute operations, ensuring optimal distribution of tasks and resource management across participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute resources are statically allocated in collaboration sessions, then resource management is simple, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic compute resource allocation by continuously collecting mesh-pertinent state from all participants and using multi-user constrained optimization to determine optimal task distribution in real-time. The system transitions from static to dynamic resource management, allowing compute resources to be redistributed based on changing participant capabilities and session requirements, thereby improving utilization efficiency while managing complexity through automated optimization algorithms.
2Loss of energy
If compute tasks are manually distributed among participants, then control is straightforward, but power consumption increases
Solution Approach 1:
The system implements self-service through automated multi-user constrained optimization that autonomously determines optimal task distribution without manual intervention. The optimization algorithm automatically balances compute tasks across participants based on their mesh-pertinent state, enabling the system to self-regulate resource allocation and minimize power consumption while maintaining collaboration session effectiveness.
3Measurement precision
If real-time state collection from all participants is implemented, then resource distribution accuracy improves, but communication overhead increases
Solution Approach 1:
The patent applies local quality by collecting only mesh-pertinent state information relevant to each participant's specific role and capabilities rather than requiring complete system state from all participants. The optimization algorithm processes this localized state information to determine optimal task distribution, reducing communication overhead while maintaining sufficient accuracy for effective resource allocation.
Data Source
AI summary
A method for scaling (or adaptively distributing) compute resource consuming responsibilities amongst multiple participants of any given collaboration solution session. Specifically, the disclosed method analyzes mesh networking pertinent state (or changes thereof), associated with the various participants, using multi-user constrained optimization to produce mesh network configurations (or adjustments thereto). A mesh network configuration, subsequently, outlines a set of adaptive compute operations, pertinent to the management of a given collaboration solution session, which may be optimally distributed across the various participants based on their current state and user-imposed resource or capability budgets (if any).