Pipeline Bursting Across Computing Systems for Latency Reduction
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Solution Overview
Problem
Existing systems face challenges in optimizing data processing latency and resource utilization across computing systems in cellular networks by efficiently distributing data processing tasks and configuring pipelines.
Innovation Solution
A method and system for pipeline bursting across computing systems, involving centralized and distributed controllers to predict data processing usage, generate pipeline creation and redistribution rules, and distribute data processing tasks to optimize resource allocation and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing tasks are distributed across multiple computing systems, then processing capacity and resource utilization are improved, but data processing latency increases due to communication overhead and task distribution
Solution Approach 1:
The system performs preliminary actions by predicting future data processing usage patterns and pre-configuring pipelines before actual processing occurs. The centralized controller predicts usage based on historical data and proactively creates or adjusts pipelines in edge computing systems, so that when data processing tasks arrive, the infrastructure is already optimized and ready, reducing latency.
Solution Approach 2:
The system dynamically adjusts pipeline configurations and task distributions based on real-time conditions. The centralized controller continuously monitors performance metrics and usage patterns, then adapts pipeline creation rules and redistribution strategies to balance processing capacity across multiple computing systems while minimizing latency for each specific task.
2Productivity
If pipeline computing resources are allocated to optimize predicted data processing usage, then resource utilization efficiency is improved, but system complexity increases due to predictive modeling and rule generation
Solution Approach 1:
The system segments the complex pipeline management function into separate components: a centralized controller that handles prediction and rule generation, and distributed edge computing systems that execute the pipelines. This segmentation allows the centralized controller to focus on strategic optimization while edge systems handle tactical execution, reducing overall system complexity.
Solution Approach 2:
The centralized controller acts as an intermediary between data processing tasks and edge computing pipelines. It receives task information, predicts usage patterns, generates optimization rules, and directs resource allocation without requiring direct complex interactions between individual tasks and computing systems, thereby simplifying the overall architecture.
Data Source
AI summary
Provided are a computer program product, system, and method for pipeline bursting across computing systems. The computing systems receive, from distributed controllers, information on processing performance and data processing tasks, for user equipment. Data processing usage at the computing systems is predicted from the received information. Pipeline creation rules are generated to allocate pipeline computing resources to the computing systems to optimize for the predicted data processing usage. Pipeline redistribution rules are generated to direct traffic for the user equipment to pipelines within the computing systems allocated according to the pipeline creation rules. The pipeline redistribution rules include a bursting pipeline redistribution rule indicating to distribute received data to pipelines in multiple computing systems to process. The pipeline creation rules and the pipeline redistribution rules, including bursting redistribution rules, are transmitted to the distributed controllers to implement the pipeline creation rules and the pipeline redistribution rules.