Hierarchical Token Bucket Database Workload Prioritization
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Solution Overview
Problem
Database management systems (DBMS) face challenges in managing capacity consumption, leading to potential service outages and inefficiencies due to uneven prioritization of requests, where high-importance requests are rejected alongside low-importance tasks that consume significant resources.
Innovation Solution
Implementing a token bucket model with hierarchical token buckets and admittance policies to manage capacity consumption by categorizing requests into classes and associating them with specific token buckets, allowing for controlled access and resource allocation based on request importance and type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a token bucket model with hierarchical token buckets and admittance policies is implemented to prioritize high-importance requests, then service reliability and responsiveness are improved, but device complexity increases
Solution Approach 1:
The patent segments the token bucket system into a hierarchical structure with multiple levels (parent buckets and child buckets). Each bucket can independently manage tokens for different request classes, allowing fine-grained control over resource allocation. This segmentation enables the system to handle different request types (e.g., high-importance vs. low-importance) with different priority levels, thereby improving service reliability without requiring a complete redesign of the entire system.
Solution Approach 2:
The admittance policy dynamically adjusts request handling based on current system state and request characteristics. The system can adaptively allocate tokens from different buckets depending on request class, current capacity availability, and priority levels. This dynamic behavior allows the system to respond flexibly to varying workload conditions while maintaining reliability for critical operations.
2Productivity
If capacity consumption is strictly limited using token buckets, then resource allocation efficiency is improved, but service responsiveness to high-importance requests may deteriorate when capacity is exhausted
Solution Approach 1:
By dividing the token bucket system into hierarchical levels with different capacity allocations, the patent ensures that high-importance requests have dedicated capacity guarantees through higher-priority buckets. This segmentation prevents low-importance requests from completely consuming available resources, thereby maintaining both resource allocation efficiency and responsiveness for critical operations simultaneously.
Solution Approach 2:
The hierarchical bucket structure acts as an intermediary layer between resource capacity and request processing. The admittance policy mediates request admission decisions by evaluating token availability across multiple bucket levels, allowing the system to enforce capacity limits while providing preferential treatment to high-importance requests through the intermediary admission control mechanism.
3Device complexity
If all requests are treated equally without prioritization, then device complexity is reduced, but service reliability deteriorates due to rejection of high-importance requests alongside low-importance tasks
Solution Approach 1:
The patent introduces segmentation of requests into different classes (high-importance, low-importance) with corresponding priority levels. This segmentation is implemented through the hierarchical bucket structure where different buckets or bucket combinations are associated with different request classes. The segmentation enables differentiated handling without requiring complex external prioritization mechanisms, thus improving reliability with minimal added complexity.
Solution Approach 2:
The admittance policy applies local quality control by treating different request classes differently based on their importance. High-importance requests can be configured to draw from specific high-priority buckets or have preferential access to available capacity, while low-importance requests are subject to stricter capacity constraints. This local differentiation ensures that critical operations maintain reliability while the overall system remains manageable.
Data Source
AI summary
A database management system may be operated by a third-party provider that hosts the system in a datacenter and provides access to the system to end users on behalf of various entities. Limits on total capacity consumption may be imposed, but may result in service outages when capacity consumption exceeds those limits. Requests to perform operations on the system may be classified. The request classifications may be associated with policies for admitting or rejecting the request. One or more token buckets representative of capacity available to the request to perform the operation may be used to determine to admit the request and updated based on the cost of performing the operation.