Database Request Container Management for Dynamic Resource Allocation
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Solution Overview
Problem
Current database management systems face inefficiencies in processing large volumes of database requests due to static resource allocation and lack of dynamic adaptation to changing request rates, leading to suboptimal computational processing and inefficient resource utilization.
Innovation Solution
A container management system that dynamically allocates data containers and processor resources based on real-time request rates, using predictive models to adjust processor counts and memory allocation, and transfers filled containers to a processor hosting service for efficient processing, while utilizing blockchain for transaction logging and confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used in database management systems, then system simplicity is maintained, but resource utilization efficiency deteriorates under varying workloads
Solution Approach 1:
The patent implements dynamic resource allocation where the container management system continuously monitors database request rates and adjusts the number of data containers and processor resources in real-time. This transforms the static resource allocation into a dynamic system that adapts to varying workloads, improving resource utilization efficiency while managing complexity through automated control mechanisms.
Solution Approach 2:
The system employs feedback mechanisms by monitoring the rate of incoming database requests and using this information to adjust resource allocation. The container management system calculates receiving rates, compares them against thresholds, and dynamically provisions or de-provisions data containers and processor resources accordingly, creating a closed-loop control system that optimizes resource utilization.
2Adaptability or versatility
If dynamic allocation of data containers and processors is implemented, then adaptability to varying workloads is improved, but system complexity increases
Solution Approach 1:
The container management system operates autonomously by automatically monitoring database request rates, calculating receiving rates, determining the optimal number of data containers needed, and provisioning or de-provisioning resources without manual intervention. This self-service approach enables the system to adapt to varying workloads while managing the complexity of dynamic allocation through automation.
Solution Approach 2:
The system performs preliminary actions by pre-configuring data containers with necessary resources and preparing processor pools in advance. When workload changes are detected, the system can quickly activate or deactivate pre-prepared containers and processors, enabling rapid adaptation to varying workloads while reducing the operational complexity of on-the-fly resource creation and destruction.
3Productivity
If predictive models are used to calculate processor counts, then processing efficiency is optimized, but computational overhead increases
Solution Approach 1:
The system applies partial action by using predictive models only to calculate the number of processors needed, rather than continuously optimizing all system parameters. The predictive model forecasts processor requirements based on current and historical workload patterns, providing sufficient accuracy for resource allocation without requiring excessive computational resources for complex real-time optimization calculations.
Data Source
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AI summary
The invention provides for a database management system (100, 400) comprising a container management system (102). The container management system comprises: a container management system memory (106) for storing machine executable instructions (114) and a container management system processor (104). Execution of the machine executable instructions cause the container management system processor to repeatedly: receive (200) multiple database requests (116) via a first network connection (110), wherein each of the multiple database requests comprises metadata descriptive of the multiple database requests; calculate (202) a receiving rate (118) by counting the multiple database requests received in a predetermined duration; calculate (204) a processor count (120) using a predictive model and the receiving rate; determine (206) a number of data containers (122) using the receiving rate and a container memory model, wherein each of the data containers is a block of the container management system memory assigned by continuous address space; request (208) a processor pool from a processor hosting service (306) via a second network connection (112) to satisfy the processor count; allocate (210) data containers (128, 130, 132, 134) equal to the number of the data containers in the container management system memory; transfer (212) the multiple data base requests to the data containers as they are received, wherein each of the multiple data base requests is assigned to a chosen data container selected from the data containers using the metadata; and transfer (214) a filled data container selected from the number of data containers to the processor hosting service via the second network connection, wherein the filled data container is filled with the multiple data requests above a predetermined threshold.