Fault-Resilient Cluster Communication Protocol
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Solution Overview
Problem
Existing fault-tolerant computing systems generate excessive intra-cloud network traffic, which can lead to inefficiencies in workload management and communication between nodes, particularly in handling workload changes and health status updates across clusters.
Innovation Solution
A communication protocol that allows master nodes to receive workload changes and health status updates from worker nodes, select a subset of changes, and send delta data packets to apply these changes efficiently, while also tolerating packet losses and network failures, thereby optimizing network bandwidth and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fault-tolerant systems send complete workload configuration updates to ensure systems are functioning properly, then reliability is improved, but network traffic increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for fault detection by worker nodes - specifically workload identifiers and health status indicators - rather than transmitting complete workload configurations. This selective extraction maintains reliability for detecting node failures while dramatically reducing network traffic volume.
Solution Approach 2:
The communication protocol segments network traffic into different types: heartbeats for health monitoring, workload assignment messages for configuration, and status reports for updates. This segmentation allows the system to maintain reliability through targeted message types while reducing overall traffic by sending only necessary information in each segment.
2Measurement precision
If the system sends frequent health status updates from worker nodes to master nodes, then system monitoring capability is improved, but network bandwidth consumption increases
Solution Approach 1:
Worker nodes send heartbeats at periodic intervals rather than continuously, providing sufficient health status monitoring while reducing network bandwidth consumption. The periodic nature allows the system to detect failures within acceptable timeframes without the overhead of continuous transmission.
Solution Approach 2:
The heartbeat messages extract only the critical health status indicator and workload identifier information needed for monitoring, rather than transmitting complete system state data. This extraction maintains measurement precision for detecting node health while minimizing bandwidth usage.
3Stability of the object's composition
If the master node sends complete workload configuration changes to all worker nodes, then workload consistency is improved, but communication efficiency deteriorates
Solution Approach 1:
The master node extracts and transmits only the specific workload assignment information and health status data that worker nodes need to maintain consistency, rather than sending complete configuration sets. This extraction maintains workload consistency for assigned tasks while improving communication efficiency by reducing message size.
Solution Approach 2:
The system uses feedback mechanisms where worker nodes report their current workload and health status back to the master node, which then sends targeted configuration updates only when changes are needed. This feedback loop maintains workload consistency while improving communication efficiency by avoiding unnecessary transmissions.
Data Source
AI summary
Embodiments are directed to communicating between computing nodes in a cluster of nodes. In one scenario, a computer system receives a data packet from a worker node including the worker node's current workload identifiers and health status, where the data packet includes an associated version number. The computer system determines that the version number in the received data packet is different than a previously received data packet and evaluates the worker node's current workload configuration to determine whether workload changes are to be made on the worker node. Then, upon determining that workload changes are to be made on the worker node, the computer system selects a subset of workload changes to apply to the worker node, generates an indication of the selected subset of workload changes to the worker node and sends the generated indication of workload changes to the worker node.