Orchestrator Token Network for Heterogeneous Node Allocation
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Solution Overview
Problem
The proliferation of networked computers, including IoT devices, presents challenges in efficiently allocating heterogeneous processing resources, onboarding diffuse computing resources, and maintaining security and access in distributed computing networks.
Innovation Solution
A self-optimizing network system that utilizes an orchestrator to manage and allocate processing tasks across heterogeneous nodes, employing a token network mechanism to identify and reward high-performing nodes, thereby optimizing resource utilization and improving probabilistic execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If diffuse heterogeneous networked computers are brought together as a federation of secure nodes, then computing power and resource allocation capacity increase, but system complexity and security management difficulty increase
Solution Approach 1:
The patent segments the complex system into distinct functional layers: a resource management layer for allocation, a token network layer for secure identification and rewards, and individual node layers for execution. This segmentation allows each layer to be optimized independently while working together, managing the overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces an orchestrator as an intermediary component that mediates between resource requests and available nodes. The orchestrator handles the complex tasks of resource allocation, node selection, and coordination, shielding individual nodes from system complexity while enabling efficient resource utilization across the heterogeneous network.
2Power
If function-specific devices with large processing resources are used, then processing capability is improved, but resource utilization efficiency deteriorates due to large amounts of downtime between use cycles
Solution Approach 1:
The patent enables function-specific devices to serve multiple purposes by allowing them to participate in the token network for distributed computing tasks between their primary functional use cycles. This multi-functionality ensures that processing resources remain idle-free by continuously allocating tasks to available nodes, dramatically improving resource utilization efficiency.
Solution Approach 2:
The patent implements continuous resource allocation through the token network, where nodes are constantly assigned computing tasks between their primary function cycles. This ensures uninterrupted useful action on available processing power, eliminating downtime waste and maintaining continuous productivity across the heterogeneous device fleet.
3Adaptability or versatility
If diffuse computing resources are onboarded as nodes, then network scalability is improved, but security and access regulation difficulty increases
Solution Approach 1:
The patent implements self-service mechanisms where nodes automatically prove their identity and capabilities to the orchestrator, and the system autonomously manages resource allocation and token distribution. This self-service approach scales the network by allowing nodes to join and contribute without manual security configuration, while maintaining security through automated verification protocols.
Solution Approach 2:
The patent employs feedback mechanisms where nodes receive tokens based on their performance and contribution to the network. This feedback loop incentivizes secure and reliable node behavior, automatically regulating access and maintaining security through economic incentives rather than complex manual access control systems.
4Productivity
If heterogeneous distributed processing resources are allocated efficiently, then resource utilization is improved, but allocation algorithm complexity increases
Solution Approach 1:
The patent implements dynamic resource allocation where the orchestrator continuously adapts task assignments based on real-time node availability, capability, and token balances. This dynamic approach optimizes resource utilization by flexibly responding to changing network conditions, while managing algorithm complexity through heuristic-based decision-making rather than exhaustive optimization.
Data Source
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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 fiddlier 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 tor 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.