Distributed Processing Reconfiguration via Partitioner Modules
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Solution Overview
Problem
Existing distributed processing systems face challenges in reconfiguring computing resources while maintaining strict ordering and concurrency constraints, which limits their ability to efficiently adapt to changes in data processing demands.
Innovation Solution
A method for distributed processing that involves a system of partitioner and accepter modules, allowing for reconfiguration of computing resources without requiring full system quiescence, by halting and reconfiguring these modules while ensuring data elements are routed according to partition and concurrency constraints, thereby enabling efficient reconfiguration and resource reallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the distributed processing system reconfigures computing resources to adapt to changes in data processing demands, then adaptability and resource utilization improve, but system complexity and difficulty of maintaining constraints increase
Solution Approach 1:
The system implements dynamic reconfiguration of distributed processing resources during runtime. The architecture allows computing resources to be dynamically added, removed, or repositioned within the processing graph while maintaining operational constraints. This is achieved through a dataflow graph model where nodes and edges can be dynamically modified, enabling the system to adapt to changing processing demands without static configuration limitations.
Solution Approach 2:
The processing system is segmented into independent computing resources (nodes) and dataflow edges that can be individually reconfigured. Each node represents a discrete processing unit that can be independently managed, added, or removed from the system. This segmentation allows partial reconfiguration without requiring system-wide shutdown or complex global coordination, reducing overall system complexity during adaptation.
2Reliability
If the system maintains strict ordering and concurrency constraints during reconfiguration, then reliability and data integrity are preserved, but productivity and reconfiguration speed decrease
Solution Approach 1:
The system maintains continuous data processing during reconfiguration operations. Rather than halting processing to maintain constraints, the architecture allows dataflow to continue through active nodes while reconfiguration occurs. The dataflow graph model enables incremental updates where constrained ordering and concurrency rules are maintained through continuous validation during the reconfiguration process, not by stopping the system.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor dataflow constraints during reconfiguration. The dataflow graph model includes built-in constraint validation that provides feedback when ordering or concurrency rules are violated, allowing the system to automatically adjust reconfiguration operations to maintain reliability without requiring complete system quiescence. This feedback-driven approach preserves data integrity while enabling ongoing productivity.
3Ease of operation
If the system halts all processing to reconfigure computing resources, then constraint maintenance becomes simpler, but loss of time and overall efficiency worsen
Solution Approach 1:
The system transitions from static to dynamic constraint management. Instead of requiring system halt for reconfiguration, the dataflow graph model enables constraints to be dynamically maintained during active reconfiguration. Nodes and edges can be modified while the system operates, with constraint validation occurring continuously rather than as a bulk check after complete system pause.
Solution Approach 2:
The system performs preliminary validation and preparation for reconfiguration operations before executing changes. The dataflow graph model allows reconfiguration plans to be prepared and validated against constraints beforehand, ensuring that when reconfiguration occurs, constraint maintenance is simplified without requiring full system halt. This preliminary action reduces the actual downtime needed while maintaining ease of constraint enforcement.
Data Source
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AI summary
Distributed processing of a data collection includes receiving information for configuring a distributed processing system. A first configuration of components is formed including sources (110) of data elements and workers (240) configured to process data elements, distributed among computing resources (190-194). Each data element includes a partition value that identifies a subset of the workers according to a partition rule. Data elements are accepted from the sources for a first part of the data collection in a first processing epoch and the data elements are routed through the first configuration. After accepting a first part of the data collection, change of configuration is initiated to a second configuration. A succession of two or more transitions between configurations of components is performed to a succession of modified configurations, a last of which corresponds to the second configuration. Further data elements are accepted from sources of the second configuration in a second processing epoch.