Orchestrator for Heterogeneous Node Resource Allocation
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Solution Overview
Problem
The proliferation of networked computing devices with heterogeneous processing capabilities presents challenges in achieving efficient resource allocation and maintaining security, as much of this computing power is underutilized and isolated within small networks, making it difficult to optimize distributed processing and regulate access to private data.
Innovation Solution
A system and method for resource management in a self-optimizing network of heterogeneous processing nodes, where an orchestrator receives processing requests, apportions tasks based on scorecards, and adds nodes to a token network if they demonstrate high computational value by successfully completing tasks, promoting probabilistic execution and self-optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous networked computing devices are connected in small isolated networks, then device compatibility and communication are achieved, but resource utilization efficiency deteriorates due to underutilized computing power
Solution Approach 1:
The patent merges multiple small isolated networks into a large federated network, combining computing resources from diverse devices across different networks. This allows underutilized computing power from one network to be allocated to tasks in another network, significantly improving overall resource utilization efficiency while maintaining device compatibility through standardized communication protocols
Solution Approach 2:
The patent creates a universal resource allocation system that can handle multiple types of computing devices (IoT devices, smartphones, laptops, servers) with heterogeneous capabilities. The system provides multi-functional resource management that adapts to different device types and network configurations, enabling efficient resource sharing across diverse platforms
2Power
If computing resources are distributed across many devices, then computational capacity increases, but system complexity and difficulty of resource allocation worsen
Solution Approach 1:
The patent introduces an intermediary resource allocation system that acts as a mediator between computing resources and tasks. This intermediary layer abstracts the complexity of heterogeneous device management by providing standardized interfaces for resource registration, capability matching, and task distribution, thereby enabling efficient utilization of large distributed computational capacity without proportionally increasing system complexity
Solution Approach 2:
The patent dynamically adjusts allocation parameters based on device capabilities, network conditions, and task requirements. By changing parameters such as resource weights, priority levels, and allocation strategies according to real-time system state, the system efficiently manages large distributed computational capacity while keeping the control mechanism relatively simple
3Adaptability or versatility
If nodes are added to the network dynamically, then network flexibility and scalability improve, but security control and access regulation worsen
Solution Approach 1:
The patent implements preliminary security actions by establishing authentication and authorization frameworks before nodes are added to the network. The system pre-defines security policies, access control rules, and trust verification mechanisms that are applied automatically when new nodes join, ensuring security control is maintained despite dynamic network changes and improved flexibility
Data Source
AI summary
A method for distributed processing includes receiving a processing request at an orchestrator, and identifying one or more tasks within the processing request. The method further includes the operations of apportioning the one or more tasks of the processing request to a set of nodes of the network for processing, the apportioning based on a scorecard for each node of the set of nodes, receiving, via the network interface, a completed task from a node of the set of nodes and performing a determination whether the completed task was processed by a node in the token network. Additionally, the method includes updating a value of a progress metric for the node not in the token network, performing a determination whether the value of the progress metric exceeds a predetermined value, and when the value of the progress metric exceeds a predetermined value, adding the node to the token network.