Variable Aggregation for Asynchronous Resource Sharing in Data Workers
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Solution Overview
Problem
In large-scale data processing systems, workers are unaware of each other's resource usage, leading to resource contention and underutilization, as they contend for shared resources without a holistic understanding of the system's state, potentially causing system congestion or idle resources.
Innovation Solution
Implementing a system where workers can asynchronously aggregate and share information about their use of shared resources through a designated leader, using pre-defined or custom aggregators, allowing them to make informed decisions about resource allocation and usage, without relying on the master node for aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If workers operate independently without sharing resource usage information, then system complexity is reduced and ease of operation is improved, but resource contention increases and productivity decreases due to underutilization
Solution Approach 1:
Workers autonomously publish their own resource usage metrics and subscribe to others' metrics without master node intervention. Each worker independently manages its own information publication and consumption, enabling self-optimized resource usage while maintaining system simplicity
Solution Approach 2:
Workers receive feedback about resource usage through metric subscriptions, allowing them to adjust their queries based on aggregated information from other workers. This feedback mechanism enables productivity optimization while workers continue operating independently
2Productivity
If workers share resource usage information through aggregation, then resource utilization improves and productivity increases, but device complexity increases due to additional aggregation infrastructure
Solution Approach 1:
The aggregation function is extracted from the master node and distributed to individual workers. Each worker locally aggregates metrics it publishes and distributes this aggregated information to subscribers, eliminating the need for complex centralized aggregation infrastructure
Solution Approach 2:
Workers perform multiple functions: executing queries, publishing resource usage metrics, aggregating their own metrics, and distributing aggregated information to subscribers. This multi-functionality reduces overall system complexity by eliminating dedicated aggregation components
3Productivity
If workers continuously aggregate and share metrics, then resource utilization optimization improves, but loss of time increases due to asynchronous communication delays
Solution Approach 1:
Workers publish metrics periodically rather than continuously, and aggregation occurs asynchronously at convenient intervals. This periodic action reduces communication overhead and time loss while maintaining effective resource optimization through timely information sharing
Solution Approach 2:
The system maintains continuous capability for metric publication and aggregation through asynchronous operations, ensuring that resource optimization can proceed without interruption despite temporary delays in information propagation
Data Source
AI summary
A method and a system for variable aggregation among large-scale data processing workers is discussed. Workers join groups and add variables to the groups. One worker is elected as the group leader. Workers post values to group variables, which are aggregated by the leader. The leader also announces the aggregated values. This variable aggregation allows for asynchronous communication among data processing workers.