Network Orchestrator Tree Lookup for Lower-Memory Large-Scale Management
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Solution Overview
Problem
The increasing size and complexity of modern networks require larger computing resources for network service orchestrators, leading to higher energy consumption and operational costs, which contradicts the desire for 'Green IT' initiatives aimed at reducing energy footprint and costs.
Innovation Solution
A method for managing large-scale networks involves a network orchestrator performing lookup operations using a network configuration tree, enqueuing background loading of children nodes, and executing operations with on-demand data loading, hint-based prefetching, and parallel data insertion to maintain schema order while minimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If larger machines with more computing resources are used to host network service orchestrators, then network management capability is improved, but energy consumption and operating costs increase
Solution Approach 1:
The patent segments the network configuration data into hierarchical components (configuration store, data store, and specific configuration elements). By organizing data in a structured hierarchy, the system can access only relevant portions of the configuration during management operations, reducing the computational resources needed to process entire network configurations.
Solution Approach 2:
The patent performs preliminary organization and indexing of network configuration data in a structured format before management operations are executed. This pre-processing allows the network service orchestrator to quickly locate and manipulate specific configuration elements without scanning entire configuration sets, thereby reducing processing power requirements and energy consumption.
2Quantity of substance
If larger machines with more memory resources are used to host network service orchestrators, then configuration data storage capacity is improved, but hardware costs and energy footprint increase
Solution Approach 1:
The configuration data is segmented into hierarchical levels with selective loading capabilities. The system loads only the necessary configuration portions into memory based on operational requirements, rather than maintaining entire network configurations in RAM. This reduces memory capacity requirements and associated energy consumption.
Solution Approach 2:
The system implements partial loading of configuration data into memory, loading only the specific configuration elements needed for current operations. Less critical or less frequently accessed configuration data remains in persistent storage, reducing the memory footprint while maintaining operational effectiveness.
Data Source
AI summary
In one embodiment, a method for management of large-scale networks includes receiving, by a network orchestrator, a request for performance of a data operation and performing, by the network orchestrator, a lookup operation corresponding to a specific data node in a computer network in response to receiving the request. The method can further include enqueuing, by the network orchestrator, a background loading operation involving a network configuration tree that includes one or more children nodes associated with the specific data node in response to initiating performance of the lookup operation and executing, by the network orchestrator, the lookup operation using the network configuration tree.