Intelligent Network of Distributed Compute Nodes for Latency Optimization
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Solution Overview
Problem
Conventional network management techniques fail to efficiently allocate compute tasks across distributed compute nodes due to latency and bandwidth limitations, leading to inefficient task distribution.
Innovation Solution
An intelligent network of distributed compute nodes is implemented, where available nodes are mapped based on compute capabilities and bandwidth, with an automated system determining the best nodes for task allocation by processing task requirements and updating network performance maps in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If compute tasks are distributed across multiple nodes, then computing capacity and versatility are improved, but latency and bandwidth limitations worsen
Solution Approach 1:
The network is segmented into multiple dedicated portions or slices, each optimized for specific compute tasks. Compute nodes are mapped to specific network portions based on task requirements, enabling specialized processing while managing latency through targeted communication paths rather than traversing the entire network.
Solution Approach 2:
An intelligent network manager acts as an intermediary between compute task requests and available nodes. This mediator processes task requirements, evaluates node capabilities, and establishes optimal mappings, thereby reducing latency by pre-computing efficient routing decisions rather than relying on conventional distributed decision-making.
2Productivity
If compute tasks are allocated to available nodes, then resource utilization is improved, but bandwidth constraints worsen
Solution Approach 1:
Different network portions are assigned different qualities or characteristics based on task requirements. High-bandwidth tasks are routed to portions with available bandwidth capacity, while latency-sensitive tasks use portions optimized for speed. This local optimization ensures that bandwidth constraints are managed by matching task needs with appropriate network segments rather than uniformly distributing all traffic.
3Productivity
If automated mapping of compute nodes is implemented, then task allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The intelligent network manager performs multiple functions within a single component: processing task requirements, evaluating node capabilities, determining optimal mappings, and managing resource allocation. This consolidation reduces overall system complexity by centralizing intelligence rather than distributing complex decision-making logic across multiple independent components.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for implementing an intelligent network of distributed compute nodes are provided herein. An example computer-implemented method includes processing information pertaining to multiple compute nodes within a network of distributed compute nodes; mapping available compute nodes, within the network, having compute capabilities and bandwidth capabilities for executing compute tasks onto dedicated portions of the network; processing information pertaining to at least one compute task requested within the network, including determining at least bandwidth requirements for the compute task and latency requirements for the compute task; and performing, based on the mapping and the processed information pertaining to the compute task, at least one automated action pertaining to allocating at least a portion of the compute task to at least one of the available compute nodes within the network.