Dynamic Token Pool Adjustment for Storage Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining the correct token pool size for a storage system's processing capacity is challenging due to ever-changing workloads and internal background processing jobs, leading to issues with resource allocation and quality-of-service (QoS) enforcement between volume priorities.
Innovation Solution
A method to dynamically adjust the token pool size based on changes in input/output (IO) processing load, including determining data path latency, IO rate, and IO queue time, and adjusting the token pool size accordingly to ensure optimal resource allocation for IO requests based on priority levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed token pool size is used for storage system resource allocation, then the system structure is simple and easy to implement, but the resource allocation accuracy deteriorates under changing workload conditions
Solution Approach 1:
The patent implements dynamic adjustment of the token pool size based on real-time monitoring of IO processing load metrics (data path latency, IO rate, IO queue time). The token pool transitions from a static fixed value to a dynamic variable that adapts to changing workload conditions, ensuring the token pool size always reflects the current processing capacity of the storage array.
Solution Approach 2:
The system continuously monitors IO processing load metrics and uses this feedback to adjust the token pool size. When metrics indicate increased processing load, the token pool size is increased; when metrics indicate decreased load, the token pool size is decreased. This closed-loop feedback mechanism ensures accurate representation of processing capacity under varying workload conditions.
2Productivity
If the token pool size is increased to handle peak workloads, then the system can handle maximum processing capacity, but resource waste occurs during low workload periods
Solution Approach 1:
The token pool size dynamically scales with workload demands. During peak workloads, the system increases the token pool size to handle maximum processing capacity. During low workload periods, the system decreases the token pool size to match actual demands, eliminating resource waste while maintaining the ability to handle peak loads when they occur.
3Ease of manufacture
If conventional algorithms are used to determine token pool size, then the implementation is straightforward, but the system cannot adapt to ever-changing workloads and background processing jobs
Solution Approach 1:
The system replaces conventional static algorithms with a dynamic feedback-based approach. Real-time monitoring of IO processing load metrics provides continuous feedback that drives automatic adjustment of the token pool size. This enables the system to adapt to ever-changing workloads and background processing jobs while maintaining straightforward implementation through automated control logic.
Solution Approach 2:
The system performs self-adjustment of the token pool size based on its own monitored performance metrics. No external intervention or complex configuration is needed; the system automatically detects workload changes and adjusts its resource allocation accordingly, making it highly adaptable to changing conditions.
Data Source
AI summary
A method, computer program product, and computing system for defining a token pool size for a storage system token pool associated with a storage system. An input/output (IO) processing load for the storage system may be determined. The token pool size for the storage system token pool associated with the storage system may be dynamically adjusted based upon, at least in part, one or more changes in the IO processing load determined for the storage system.