Distributed Autonomous Resource Management for Cloud Scalability
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Solution Overview
Problem
Centralized network management systems face scalability limitations when managing large-scale datacenters or cloud clusters, as they can only handle a finite number of nodes, making it difficult to extend from tens to thousands of nodes efficiently.
Innovation Solution
A distributed, autonomous resource discovery, management, and stitching system where each node independently manages its resources and communicates with neighboring nodes to locate and utilize available resources without a centralized controller, using intelligent distribution functions to propagate resource requests and stitch resources across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized management is used to control and manage datacenter resources, then resource allocation and monitoring can be performed, but the system cannot scale infinitely due to computing and access limitations of the control infrastructure
Solution Approach 1:
The patent divides the centralized management system into distributed autonomous agents deployed on individual nodes. Each agent independently manages resource discovery, monitoring, and allocation for its local node, eliminating the single point of control and enabling the system to scale to thousands of nodes without overwhelming a central controller.
Solution Approach 2:
The patent transitions from a two-dimensional centralized control model to a three-dimensional distributed mesh architecture where nodes communicate horizontally with neighbors in addition to vertical agent-node relationships. This dimensional expansion distributes control plane load across multiple paths and layers, enabling scalable growth.
2Adaptability or versatility
If centralized control is implemented for federation between aggregate managers, then resource management can be coordinated, but additional external infrastructure is required which limits scalability
Solution Approach 1:
The patent enables nodes to autonomously discover and federate with neighboring nodes through distributed agent communication. Each agent independently negotiates resource sharing and coordination with adjacent nodes without requiring external federation infrastructure, allowing the system to self-organize and scale organically.
Solution Approach 2:
The patent combines resource management, monitoring, and federation capabilities into integrated autonomous agents that run locally on each node. This consolidation eliminates the need for separate external infrastructure for these functions, as each agent performs all management tasks for its host node while participating in the distributed network.
3Productivity
If cloud infrastructure controls thousands of nodes, then comprehensive resource management is achieved, but scalability issues arise due to the burden on central control
Solution Approach 1:
The patent segments the control function into autonomous agents deployed on individual nodes throughout the infrastructure. Each agent handles resource discovery, monitoring, and allocation locally, distributing the computational burden and eliminating the scalability bottleneck associated with centralized control of thousands of nodes.
Solution Approach 2:
The patent implements distributed feedback loops where autonomous agents continuously monitor local resource conditions and autonomously adjust resource allocation and routing decisions. This decentralized feedback mechanism enables efficient resource management without requiring constant central coordination, improving both productivity and scalability.
Data Source
AI summary
Aspects of the present invention include employing a distributed, scalable, autonomous resource discovery, management, and stitching system. In embodiments of the present invention, intelligent distribution systems and methods are employed in an autonomous resource discovery, management, and stitching systems. In embodiments of the present invention a set of rules or parameters may be used to determine whether a request for resources should be forwarded to other nodes. In embodiments of the present invention, an intelligent distribution engine selects the node to be used when more than one database instance can fulfill a request.