Multi-OS Computing Node Layout for Lock-Free Parallel Queries
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Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, leading to potential deadlocks and inefficiencies in multi-operating system environments.
Innovation Solution
A parallelized database system architecture that includes a parallelized data input sub-system, data store and process sub-system, query and response sub-system, administrative sub-system, and configuration sub-system, utilizing multiple computing devices and nodes with independent processing core resources to handle data partitioning, storage, and query optimization, allowing for lock-free and parallel execution of operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple operating systems share resources on a single microprocessor, then resource utilization is improved, but system reliability deteriorates due to potential deadlocks
Solution Approach 1:
The system divides the computing device into multiple independent nodes, each with its own microprocessor and operating system instance. This segmentation isolates resources so that each node operates independently, preventing deadlocks from propagating across the entire system while maintaining high resource utilization through parallel operation of multiple nodes.
2Device complexity
If database operations are processed sequentially on a single microprocessor, then system complexity is reduced, but processing speed deteriorates
Solution Approach 1:
The database system is segmented across multiple nodes, with each node handling a portion of database operations independently. This allows parallel processing of queries and data operations, significantly increasing processing speed while keeping each individual node's architecture relatively simple and manageable.
Solution Approach 2:
The system transitions from single-dimensional sequential processing on one microprocessor to multi-dimensional parallel processing across multiple nodes. By adding the spatial dimension of multiple independent processing units, the system achieves higher throughput without increasing the complexity of individual processing units.
3Device complexity
If data is stored in traditional storage methods, then storage simplicity is maintained, but data access speed deteriorates
Solution Approach 1:
Data is segmented and distributed across multiple storage locations associated with different nodes. Each node stores and processes a portion of the database, allowing parallel data access operations. This segmentation enables faster data retrieval by allowing simultaneous access to multiple data partitions without requiring complex centralized storage management.
Data Source
AI summary
A computing device includes main volatile memory and a node. The node includes a central processing module, non-volatile memory; and a non-volatile memory interface unit. A combination of the non-volatile memory and the main volatile memory stores an application specific operating system and at least a portion of a computing device operating system. The application specific operating system includes a plurality of application specific system level operations and the computing device operating system includes a plurality of general system level operations. A first processing module of the central processing module operates in accordance with a selected operating system and ignores operation not included in the selected operating system. The selected operating system includes one or more selected application specific level operations of the application specific operating system.