Load Balanced Storage Provisioning Using Performance Metrics
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Solution Overview
Problem
Conventional storage provisioning methods do not consider performance impact and resource utilization, leading to unbalanced deployments where some systems are heavily loaded while others are underutilized.
Innovation Solution
A storage allocation system that manages allocation of storage space from aggregates, taking into account resource utilization and maximum performance capacities to provision storage in a load-balanced manner, even for virtual storage entities with unknown performance properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If storage provisioning is performed based purely on storage capacity without considering performance impact and resource utilization, then storage capacity utilization is improved, but system performance balance deteriorates
Solution Approach 1:
The patent changes the provisioning parameters from purely storage capacity-based to a multi-parameter approach that includes performance metrics (IOPS, throughput, latency) and resource utilization (CPU, memory, network). This allows the system to evaluate both storage capacity and performance impact simultaneously, resolving the contradiction between capacity utilization and performance balance.
Solution Approach 2:
The system performs preliminary assessment of aggregate performance characteristics and resource utilization before provisioning storage. By evaluating performance metrics and resource availability in advance, the system can make informed provisioning decisions that prevent performance imbalance, rather than reacting to imbalance after it occurs.
2Ease of operation
If conventional storage provisioning methods are used that ignore resource utilization, then provisioning simplicity is improved, but system efficiency deteriorates
Solution Approach 1:
The system implements self-service provisioning by automatically gathering performance metrics, analyzing resource utilization, and making intelligent provisioning decisions without manual intervention. This maintains ease of operation while dramatically improving system efficiency through automated, data-driven allocation that optimizes resource utilization across the storage infrastructure.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor performance metrics and resource utilization, using this information to dynamically adjust provisioning decisions. This feedback mechanism enables the system to maintain both simplicity (through automation) and efficiency (through continuous optimization based on actual system state).
Data Source
AI summary
Available performance capacities of storage servers and storage devices in a storage infrastructure are determined using a plurality of metrics, wherein each of the storage devices is managed by one of the storage servers. Each aggregate is hosted by a storage server and includes multiple storage devices. A relationship between the plurality of metrics is analyzed. An aggregate from which to allocate storage capacity to a volume from a plurality of aggregates is selected based on the available performance capacities and the relationship between the plurality of metrics. The selection is performed without information about properties of the volume. Storage capacity of the selected aggregate is automatically allocated to the volume. After said allocation, the plurality of aggregates is approximately load balanced.