Networked Storage Resource Management Through Capacity Prediction
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Solution Overview
Problem
Existing storage systems face challenges in efficiently managing resources as they expand in size and complexity, necessitating improved methods to monitor and manage resource usage to maintain performance without increasing latency.
Innovation Solution
A performance manager module interfaces with a storage operating system to collect QoS data, determining available performance capacity by analyzing the relationship between latency and utilization, using an optimal utilization point to predict resource capacity, and adjusting for workload changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If storage systems expand in size and complexity to handle more workloads, then processing capacity increases, but resource management efficiency deteriorates and latency increases
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring resource utilization metrics and using this information to dynamically adjust resource allocation and workload distribution. The system collects performance data, analyzes it against thresholds, and automatically triggers remediation actions, creating a closed-loop control system that manages complexity while maintaining efficiency in expanded storage environments.
2Productivity
If resource utilization is increased to maximize throughput, then processing efficiency improves, but latency increases beyond acceptable levels
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting resource allocation parameters based on monitored utilization levels. When utilization approaches thresholds that predict excessive latency, the system modifies parameters such as workload distribution, resource provisioning, or priority settings to maintain throughput while preventing latency degradation. This involves changing operational parameters in response to measured system state.
3Productivity
If more resources are allocated to handle workload increases, then throughput capacity improves, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamics by creating a flexible, adaptive resource allocation system that continuously adjusts resource provisioning based on actual workload demands. Rather than static allocation, the system dynamically scales resources up or down, redistributes workloads, and optimizes utilization in real-time, allowing throughput capacity to expand while maintaining high resource utilization efficiency through adaptive behavior.
Data Source
AI summary
Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.