Collaborative Triggers for Distributed Notification Flooding
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Solution Overview
Problem
Distributed and dynamic computing systems face challenges in providing scalable and efficient notification mechanisms, as existing methods like triggers and polling overwhelm nodes with notifications, leading to bandwidth and processing issues.
Innovation Solution
Implementing collaborative triggers that distribute notifications among nodes, suppressing further triggers of the same type until a response is received from an actor, thereby preventing flooding and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional triggers or polling mechanisms are used for notifications in distributed computing systems, then nodes can be notified of events, but the system becomes overwhelmed with redundant notifications leading to bandwidth and processing issues
Solution Approach 1:
The patent combines multiple notification attempts for the same event into a single collaborative trigger distribution. When a node generates a collaborative trigger for an event, it suppresses other triggers of the same type until a response is received, merging redundant notification functions into one efficient operation.
Solution Approach 2:
The collaborative trigger acts as an intermediary mechanism between event sources and nodes. Instead of direct polling or redundant trigger propagation, the system uses collaborative triggers with query identifiers to mediate notification distribution, coordinating responses across the distributed system and preventing overload.
2Reliability
If collaborative triggers are distributed to all nodes, then comprehensive notification coverage is achieved, but network bandwidth and processing resources are consumed
Solution Approach 1:
The system performs preliminary suppression of collaborative triggers at each node based on query identifiers before distribution occurs. Nodes pre-check for existing suppressed triggers of the same type, preventing redundant distribution and consuming bandwidth only when necessary for unique events.
Solution Approach 2:
The notification system segments triggers by query identifier, allowing nodes to independently manage suppression states for different event types. This segmentation enables selective distribution where only unique collaborative triggers propagate through the network, reducing overall bandwidth consumption while maintaining coverage.
3Productivity
If multiple collaborative triggers of the same type are suppressed, then node overload is prevented, but response time may be delayed
Solution Approach 1:
The system implements feedback through response messages sent back to nodes when an actor processes a collaborative trigger. Upon receiving a response, nodes lift the suppression for that query identifier, allowing subsequent triggers of the same type to propagate. This feedback mechanism ensures timely resumption of notifications while preventing overload during active processing.
Data Source
AI summary
Using collaborative triggers within a distributed computing system can include, in response to detecting a first event of a selected type within a first node of the computing system, generating a first collaborative trigger having a query identifier correlated with the selected type of the event and distributing the first collaborative trigger through other nodes of the computing system so that the first collaborative trigger reaches an actor of the computing system. The computer-implemented method can include suppressing, within each node that distributes the first collaborative trigger, each other collaborative trigger having a same query identifier as the first collaborative trigger until a response to the first collaborative trigger is received from the actor.