Configurable-Capacity Time-Series Tables for Dynamic Workload Adaptation
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Solution Overview
Problem
In storage-related service environments, initial workload estimates and resource allocations for database services often become inappropriate over time due to changing access patterns and data usage, leading to inefficiencies in resource management and increased costs.
Innovation Solution
Implementing configurable-capacity time-series tables with a provisioned-throughput model, where tables are created and modified based on triggering conditions, such as time intervals or data access patterns, to dynamically adjust throughput settings and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If initial workload estimates and resource allocations are set at storage object creation, then resource provisioning can be established upfront, but the allocations become inappropriate over time as access patterns and data usage change
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload metrics (read/write throughput, IOPS) and automatically adjusting storage capacity and performance parameters. The system transitions from static initial provisioning to dynamic adaptation, where resource allocations are modified in real-time based on actual usage patterns, thereby resolving the contradiction between upfront provisioning ease and long-term adaptability.
Solution Approach 2:
The system employs feedback mechanisms by monitoring workload metrics and performance characteristics of storage devices, then using this information to trigger capacity changes. The feedback loop compares actual usage against provisioned resources and initiates scaling operations when thresholds are met, enabling the system to adapt resource allocations dynamically while maintaining ease of management through automated control.
2Productivity
If more storage devices and nodes are provisioned to meet initial workload estimates, then throughput goals can be met initially, but resource utilization becomes inefficient when workloads change
Solution Approach 1:
The patent implements dynamic scaling of storage resources by monitoring actual workload metrics and adjusting capacity accordingly. When throughput demands decrease, the system automatically reduces provisioned resources, preventing waste. When demands increase, resources are scaled up to maintain productivity. This dynamic adjustment resolves the contradiction between maintaining high throughput and avoiding resource waste.
Solution Approach 2:
The system changes key parameters including storage capacity, IOPS limits, and throughput allocations based on monitored workload characteristics. By dynamically modifying these parameters rather than maintaining fixed allocations, the system optimizes the balance between productivity and resource utilization efficiency, scaling resources up or down to match actual demands.
3Quantity of substance
If storage capacity is increased to accommodate future data growth, then data storage needs can be met, but costs increase and resource management becomes less efficient
Solution Approach 1:
The patent implements dynamic capacity management by continuously monitoring data volume metrics and adjusting storage allocation in real-time. Instead of pre-provisioning excessive capacity, the system scales storage resources up or down based on actual data growth patterns, thereby meeting storage needs while minimizing costs and improving resource efficiency.
Solution Approach 2:
The system provides self-service capacity management by automatically monitoring workload metrics and triggering capacity changes without manual intervention. The storage system serves itself by detecting when capacity thresholds are met and autonomously provisioning or de-provisioning resources, eliminating the need for over-provisioning while ensuring storage needs are met.
4Productivity
If manual monitoring and adjustment of storage resources is performed, then resource allocation can be optimized, but operational complexity and time consumption increase
Solution Approach 1:
The patent implements automated feedback-driven resource management by continuously monitoring workload metrics and automatically triggering capacity adjustments. The system establishes feedback loops that detect when performance thresholds are met or exceeded and autonomously initiate scaling operations, thereby achieving resource optimization without manual intervention and eliminating the time loss associated with manual monitoring and adjustment.
Solution Approach 2:
The storage system performs self-service optimization by autonomously monitoring its own performance characteristics and workload metrics, then automatically adjusting capacity and resource allocations. This self-managing capability achieves productivity optimization while eliminating the operational time burden that would otherwise be required for manual resource management.
Data Source
AI summary
Methods and apparatus for configurable-capacity time-series tables are disclosed. A schedule of database table management operations, including at least an operation to change a throughput constraint associated with a table in response to a triggering event, is generated. The table is instantiated with an initial throughput constraint in accordance with the schedule. Work requests directed to the table are accepted based on the initial throughput constraint. The throughput constraint is modified in response to the triggering event. Subsequent work requests are accepted based on the modified throughput constraint.