Stream Computing Parallelism Adjustment
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Solution Overview
Problem
In stream computing systems, the initially set parallelism degree often fails to adapt to real-time changes in processing conditions, leading to resource wastage and limited data processing capability due to static configuration.
Innovation Solution
A control node dynamically adjusts the parallelism degree of working nodes in real-time by collecting data traffic and processing speed information, determining an optimized parallelism degree, and adding or removing execution units as needed to match current load conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static parallelism degree is configured for working nodes, then the system structure is simple and easy to configure, but the system cannot adapt to real-time changes in processing conditions, leading to resource wastage and limited data processing capability
Solution Approach 1:
The patent implements dynamic adjustment of parallelism degree for working nodes based on real-time data traffic and processing speed monitoring. The system transitions from static configuration to dynamic adaptation by continuously collecting performance metrics and adjusting the number of execution units accordingly, allowing the system to respond to changing processing conditions while maintaining manageable complexity through automated control
Solution Approach 2:
The system establishes a feedback mechanism where the control node continuously collects data traffic information and processing speed information from working nodes, compares current performance with target performance, and adjusts the parallelism degree accordingly. This closed-loop feedback system enables automatic adaptation to real-time changes without requiring complex manual reconfiguration
2Productivity
If the parallelism degree is increased to handle higher data traffic, then data processing capability is improved, but resource utilization becomes inefficient when traffic is low
Solution Approach 1:
The system dynamically adjusts the parallelism degree of working nodes based on real-time data traffic conditions. When data traffic increases, the system allocates more execution units to maintain high processing capability. When traffic decreases, execution units are reduced or released, preventing resource wastage. This dynamic scaling ensures optimal matching between resource allocation and actual processing demands
Solution Approach 2:
The system changes the parallelism degree parameter of working nodes based on monitored performance metrics such as data traffic volume and processing speed. By adjusting this key parameter dynamically, the system optimizes the balance between processing capability and resource consumption, avoiding both under-provisioning and over-provisioning of computational resources
3Speed
If more execution units are allocated to working nodes, then processing speed is improved, but system resource utilization becomes unbalanced
Solution Approach 1:
The system applies differentiated resource allocation to different working nodes based on their specific processing needs and current workload characteristics. Rather than uniformly allocating execution units across all nodes, the control node monitors individual node performance and adjusts parallelism degree locally for each working node, achieving optimal processing speed while maintaining overall resource balance
Solution Approach 2:
The system adjusts the parallelism degree parameter individually for each working node based on local performance metrics such as data traffic volume and processing speed. This localized parameter adjustment enables each node to operate at optimal speed while preventing overall system resource imbalance, as the control node coordinates adjustments across the entire system
Data Source
AI summary
A stream computer system and a method for processing a data stream in a stream computing system are disclosed. The method includes a first working node invokes at least one execution unit to process a data stream according to an initial parallelism degree, a control node collects information reflecting data traffic between the first working node and a second working node, and information reflecting data processing speed of the first working node, determines an optimized parallelism degree for the first working node according to the collected information, and adjusts the parallelism degree of the first working node to be consistent with the optimized parallelism degree.