Mesh Node Load Balancing via Autonomous Task Forwarding
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Solution Overview
Problem
Current load balancing techniques in networked computer systems are inefficient and lack scalability, making it difficult to distribute tasks effectively among nodes, especially as the number of Internet users and services increases.
Innovation Solution
A method for load balancing where a node determines if it has sufficient resources to perform a task, and if not, it randomly or weightedly selects another node to forward the request, using a communication interface and processing unit to manage request distribution and resource allocation across a network of communicatively connected nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional load balancing techniques are used to distribute tasks among networked computers, then task distribution is achieved, but processing resource usage is inefficient and scalability is limited
Solution Approach 1:
Each node independently determines its own resource capacity and makes autonomous decisions about task acceptance and forwarding without requiring complex centralized coordination. The node serves itself by evaluating its own state and self-managing its task queue, eliminating the need for resource-intensive centralized load balancing algorithms.
Solution Approach 2:
Instead of having a centralized system assign tasks to nodes based on complex algorithms, the patent inverts the approach by having nodes independently evaluate and accept tasks they can handle, then forward rejected tasks to other nodes. This reversal simplifies the load balancing mechanism and reduces overall processing overhead.
2Adaptability or versatility
If more nodes are added to the network to handle increased Internet services, then service capacity increases, but load balancing complexity increases
Solution Approach 1:
The load balancing function is segmented and distributed to individual nodes rather than being centralized. Each node independently manages its own task evaluation and forwarding decisions, allowing the system to scale to any number of nodes without increasing central coordination complexity. The complexity is divided and assigned to each node autonomously.
Solution Approach 2:
Each node autonomously determines its resource capacity and makes independent decisions about task acceptance and forwarding. This self-service approach allows nodes to be added or removed from the network dynamically without requiring complex reconfiguration or centralized management, thereby enhancing scalability while maintaining simple load balancing logic at each node.
3Reliability
If a node forwards tasks to other nodes when resources are insufficient, then task completion is achieved, but communication overhead increases
Solution Approach 1:
Nodes perform a simple resource capacity check before accepting tasks, and only forward tasks when clearly insufficient. This partial action approach avoids excessive communication by not requiring continuous status updates or complex negotiation protocols. The communication overhead is minimized by acting only when necessary (when resources are insufficient) rather than continuously coordinating.
Data Source
AI summary
Techniques for load balancing in a network of nodes can include a first node receiving and/or generating a request having an identifier, task description, and information regarding an entity originating the request. The first node can either perform the task and report back to the originating entity of the completion of the task, or forward the request to a random one of a plurality of other nodes with which the first node is communicatively connected.