Hybrid Latency Utilization Curve for Storage Capacity
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Solution Overview
Problem
In networked storage environments, managing resources efficiently to maintain optimal performance capacity and ensure Quality of Service (QOS) is challenging as storage systems grow in size and complexity, making it difficult to monitor and utilize resources effectively.
Innovation Solution
A performance manager module is introduced that collects QOS data, filters and groups performance data, and generates a hybrid latency versus utilization curve to determine available performance capacity, allowing for optimal resource utilization and headroom management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If storage systems expand in size and complexity to handle more data and clients, then storage capacity and functionality improve, but resource monitoring and performance management become more difficult
Solution Approach 1:
The patent introduces a performance manager module as an intermediary component that centralizes resource monitoring and performance capacity determination. This module collects QOS data, filters performance data, generates latency versus utilization curves, and determines available performance capacity for resources. By placing this intermediary layer between the complex storage infrastructure and management functions, the system can handle expanded storage capacity without proportionally increasing management complexity.
2Productivity
If resource utilization is increased to maximize throughput, then productivity improves, but performance capacity may be exceeded causing latency violations
Solution Approach 1:
The patent implements a feedback mechanism where the performance manager continuously monitors resource utilization, collects QOS data including latency measurements, and compares actual performance against available performance capacity. The system generates latency versus utilization curves that show the relationship between resource usage and performance metrics. This feedback loop allows the system to maintain high throughput while preventing QOS violations by identifying when resource utilization approaches capacity limits.
Solution Approach 2:
The performance manager determines available performance capacity in advance by analyzing historical performance data and generating latency versus utilization curves before workloads are executed. This preliminary determination of capacity allows the system to proactively manage resource allocation and prevent QOS violations rather than reacting after performance degradation occurs.
3Measurement precision
If performance data collection is comprehensive to ensure accurate monitoring, then measurement precision improves, but data processing complexity and time increase
Solution Approach 1:
The performance manager implements selective data collection and filtering by focusing on specific QOS parameters relevant to performance capacity determination. The system filters performance data to remove observations beyond certain service time values and groups filtered data into utilization bins, processing only the necessary subset of data rather than analyzing every possible metric. This partial action approach maintains measurement precision for critical parameters while reducing overall data processing time.
Data Source
AI summary
Methods and systems for a network storage environment are provided. One method includes retrieving stored performance data associated with a resource used in the networked storage environment, where the stored performance data includes latency data, utilization data and a service time; filtering the stored performance data by removing any observations that are beyond a certain value of the service time; grouping the filtered stored performance into utilization bins and identifying a representative of each utilization bin; generating by the processor a hybrid latency versus utilization curve comprising a first portion that is based on the representative of each utilization bin and a second portion generated using a model based technique and determining by the processor available performance capacity of the resource using the hybrid latency versus utilization curve; where the available performance capacity is based on optimum utilization of the resource and actual utilization of the resource.