Subscription Rule Propagation for Distributed Data Nodes
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Solution Overview
Problem
In network environments with multiple computing nodes, the existing data object replication model leads to significant network and processing bandwidth issues as the number of objects and nodes increases, causing inefficiencies in data distribution and updates.
Innovation Solution
Implementing a system where each computing node stores subscription rules defining the characteristics of data objects it needs, allowing dynamic distribution of data objects only to nodes that have an interest in them, based on attribute matching between rule sets and data object attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data objects are replicated on all computing nodes in the network, then each node has ready access to the data object, but network and processing bandwidth consumption increases significantly
Solution Approach 1:
Instead of uniformly replicating data objects on all nodes, the system implements selective replication based on node-specific subscription rules. Each node maintains only the data objects that match its subscription criteria, creating a non-uniform distribution pattern that reduces redundant copies while ensuring each node has access to relevant data.
Solution Approach 2:
The system introduces a data object manager as an intermediary component that handles subscription rule propagation and data object distribution. This mediator coordinates between data sources and computing nodes, using subscription rules as intermediaries to determine which nodes should receive which data objects, thereby controlling replication scope and reducing bandwidth consumption.
2Reliability
If data objects are replicated on all computing nodes, then data availability is ensured, but processing efficiency decreases due to unnecessary data handling
Solution Approach 1:
Each computing node maintains a customized set of data objects based on its subscription rules rather than receiving all data objects. This local customization ensures that nodes only process and handle data objects relevant to their function, improving processing efficiency while maintaining data availability for subscribed objects.
Solution Approach 2:
Instead of providing complete data object replication to all nodes (excessive action), the system provides only the necessary data objects that match subscription rules (partial action). This partial replication approach eliminates unnecessary data processing while ensuring sufficient data availability for each node's purposes.
3Loss of energy
If the system dynamically distributes data objects based on subscription rules, then network bandwidth usage is minimized, but system complexity increases
Solution Approach 1:
The data object manager serves as a centralized intermediary that handles the complexity of subscription rule propagation and data object distribution. By consolidating these management functions in a dedicated component, the system reduces the complexity burden on individual nodes while enabling efficient dynamic distribution based on subscription rules.
Solution Approach 2:
The system performs preliminary actions by propagating subscription rules to computing nodes before data object distribution occurs. Nodes evaluate their subscription rules in advance to determine which data objects they need, allowing the system to prepare and distribute only relevant data objects efficiently, reducing both bandwidth usage and distribution complexity.
4Productivity
If subscription rules are propagated to all connected nodes, then accurate data object distribution is achieved, but processing time increases
Solution Approach 1:
Instead of propagating subscription rules to all possible nodes in the network (excessive action), the system propagates rules only to nodes that are connected and have expressed interest in specific data objects (partial action). This selective propagation reduces the scope of rule distribution while maintaining accurate data object allocation based on actual node needs.
Data Source
AI summary
A computing system implemented method is disclosed for distributing data objects in a network of computing nodes. Each node has stored thereon subscription rules defining characteristics of data objects that the particular node may require during processing. The nodes exchange their subscription rules with other nodes in the network. Each node then compares the rules received from other nodes with the attributes of data objects stored on the particular node. Where a rule set matches the attributes of a data object stored thereon, the node marks the particular data object for copying to the node from which the particular rule set was received. The marked data object is then copied to the appropriate node.