Adaptive Multi-Node AI Topology for Cloud Edge Load Balancing
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Solution Overview
Problem
Current cloud-based AI services face limitations such as high costs, unpredictable performance due to overload or denial-of-service attacks, and unavailability when users are offline, with AI models being over-engineered for general use but under-engineered for specific user needs, leading to inefficient resource usage and user dissatisfaction.
Innovation Solution
The development of adaptive multi-node AI topologies that utilize both local and remote configurations, incorporating reconfigurable neural network-based circuit units and software AI models, allowing for dynamic resource allocation, load balancing, and personalized training data usage to optimize performance and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based AI services are used to provide centralized processing and storage, then processing speed and model quality are improved, but service costs increase and reliability deteriorates under overload or attack conditions
Solution Approach 1:
The patent segments the centralized cloud AI service into distributed multi-node AI topologies deployed across multiple devices. Each node contains subset AI models that can operate independently, transforming a monolithic cloud service into a distributed system where nodes can function autonomously during cloud unavailability, thus improving reliability while maintaining model quality through coordinated operation.
Solution Approach 2:
The patent introduces local device nodes as intermediaries between users and cloud AI services. These nodes cache AI models and training data locally, acting as mediators that can serve requests without cloud connectivity. The nodes synchronize with the cloud when available, providing a buffer that ensures service continuity during outages or attacks.
2Measurement precision
If massive training data and complex models are deployed in the cloud, then AI model quality is improved, but processing demands and costs increase
Solution Approach 1:
The patent applies local quality by deploying different AI model subsets to different nodes based on their specific functions and requirements. Each node processes only the data and models relevant to its local function, avoiding the energy cost of processing entire massive datasets centrally. This distributed specialization maintains overall AI quality while reducing total processing energy consumption.
Solution Approach 2:
The patent implements partial action by having each node process only a subset of the total training data and model parameters needed for complete AI functionality. Nodes perform partial processing locally and coordinate with other nodes for complete decision-making, reducing the processing burden on any single node and overall system energy consumption while maintaining comprehensive AI capabilities.
3Adaptability or versatility
If cloud AI services operate as independent generative offerings, then service versatility is improved, but adaptability to specific user needs deteriorates
Solution Approach 1:
The patent segments the AI service into modular nodes that can be independently configured for specific user needs. Each node can be customized with particular model subsets and training data relevant to its function, allowing versatile adaptation to different user requirements without requiring complete reconfiguration of the entire system. The modular structure simplifies customization compared to monolithic cloud services.
4Device complexity
If single-node AI models are used for simplicity, then device complexity is reduced, but processing capability and reliability deteriorate
Solution Approach 1:
The patent merges multiple single-node AI models into a coordinated multi-node topology where each node contributes specific processing capabilities. The nodes combine their individual model outputs through weighted integration to achieve superior processing capability that exceeds any single node alone, while maintaining relative simplicity through standardized node interfaces and coordination protocols.
Data Source
AI summary
A multi-node artificial intelligence topology adapts to service many different overall purposes. Support processing nodes, discriminative AI elements, generative AI elements along with input, output and communication circuitry along with other outside interactions provide the nodal basis for the overall topology. Therewithin, outputs of several nodes drive a single node which uses influence balancing to optimize its own output. Influence is delivered in feed forward and feed back manner. Segmented processing is provided where sections of an overall output goal is processed through the topology in segments, e.g., chapter by chapter of a novel, episode by episode, a full topology processing using internal cross node influence followed by a second full topology processing using both internal cross node and cross segment influence. Pseudo random templating providing constraints used to progress through segments to control an output flow. AI elements can be fully software, use acceleration circuitry, and employ neural network circuitry such as analog and digital versions thereof. Topologies also adapt between local and remote processing locations on a node by node basis, where, for example, some AI elements or nodes operate in the cloud, while other AI elements operate on a particular user's device or other user devices located remotely. Topologies adapt in real time to move nodes to away from a user's device to a cloud counterpart and vice versa as circumstances change.