Database Scheduling Based on Peak Power Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient execution of operations, especially when handling large-scale data processing and concurrent queries.
Innovation Solution
A parallelized database system architecture that divides data into segments, utilizes a segment generator for cluster key-based grouping, and implements a query processing system with optimized query plans and energy utilization monitoring to enhance processing efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a database system processes large-scale data using traditional hardware and storage methods, then data storage capacity is sufficient, but processing speed is limited by hardware constraints
Solution Approach 1:
The patent divides data into segments and uses a segment generator to create cluster key-based groups, enabling parallel processing across multiple computing devices. This segmentation allows the system to overcome single-machine hardware limits by distributing work across a cluster, directly improving processing speed for large-scale data while managing complexity through automated segment generation.
Solution Approach 2:
The patent introduces a cloud computing dimension by receiving data from cloud-based sources and utilizing distributed computing resources. This transitions from traditional single-machine processing to a multi-dimensional distributed architecture, enabling faster processing speeds by leveraging the computational power and scalability of cloud infrastructure.
2Productivity
If a database system uses traditional data storage methods, then storage implementation is simple, but execution speed is limited by data storage structure
Solution Approach 1:
The patent implements a segment generator that divides data into segments and organizes them into cluster key-based groups. This segmented storage structure enables parallel query execution and improves execution speed by allowing multiple computing devices to process different segments simultaneously, while the automated generation process manages the complexity of the storage structure.
3Productivity
If a database system processes concurrent queries sequentially, then system implementation is simple, but processing efficiency decreases due to limited co-processing options
Solution Approach 1:
The patent segments queries and data into manageable units that can be processed in parallel by multiple computing devices. The segment generator creates structured data groups that enable concurrent query processing, improving processing efficiency by allowing simultaneous execution of multiple queries while the systematic segmentation approach manages the complexity of coordinating concurrent operations.
Solution Approach 2:
The patent creates a universal query processing framework that can handle various types of concurrent queries through a single segment generator and query execution architecture. This multi-functional system processes different query types and data structures using the same parallel processing mechanisms, improving efficiency across diverse workloads while avoiding the need for separate specialized systems for each query type.
Data Source
AI summary
A database system is operable to generate an energy utilization-based operation scheduling function based on power usage policy data for the database system. A storage-formatted data generation operation is determined for execution, and energy utilization-based operation scheduling input data is determined for the storage-formatted data generation operation. The storage-formatted data generation operation energy utilization-based operation scheduling function is performed upon the energy utilization-based operation scheduling input data to generate energy utilization-based operation scheduling data for the storage-formatted data generation operation. A plurality of storage-formatted data structures are generated from a plurality of records for storage based on executing the storage-formatted data generation operation in accordance with the energy utilization-based operation scheduling data.