Dynamic Storage Connectivity Workload Balancing
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Solution Overview
Problem
Data storage systems face challenges in dynamically managing connectivity between initiators and target ports to optimize workload distribution and performance, leading to potential overloading and unbalanced workload across components.
Innovation Solution
A method and system for determining connectivity by receiving statistics on data storage system components, adjusting existing connectivity based on performance and workload thresholds, and modifying states or adding/removing connectivity between initiators and target ports using masking information and network fabric communication to redistribute workload and balance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If connectivity between initiators and target ports is statically configured, then system simplicity is maintained, but workload distribution becomes unbalanced and performance deteriorates
Solution Approach 1:
The patent implements dynamic connectivity management by continuously monitoring workload statistics and automatically adjusting initiator-to-target-port mappings based on current system conditions, transforming static connectivity configuration into a dynamic, adaptive system that optimizes workload distribution in real-time
Solution Approach 2:
The system employs feedback mechanisms by collecting workload statistics from target ports and using this information to make informed decisions about connectivity modifications, creating a closed-loop control system that continuously optimizes performance based on actual system state
2Productivity
If connectivity is dynamically adjusted to optimize workload distribution, then performance is improved, but system complexity and risk of instability increase
Solution Approach 1:
The patent applies preliminary action by evaluating multiple potential connectivity configurations and selecting optimal mappings before implementing changes, ensuring that transitions are carefully planned and validated to maintain system stability during reconfiguration
Solution Approach 2:
The system uses an intermediary optimization module that acts as a mediator between workload monitoring and connectivity configuration, processing statistical information and translating it into safe, validated connectivity changes that balance performance improvement with system reliability
3Device complexity
If workload is concentrated on fewer target ports, then connectivity complexity is reduced, but those components become overloaded and performance degrades
Solution Approach 1:
The patent applies local quality by tailoring connectivity assignments to specific target port capabilities and current workload conditions, allowing different target ports to serve different functions based on their individual characteristics and real-time performance metrics rather than using a uniform connectivity scheme
Data Source
AI summary
Described are techniques for determining connectivity. Statistics are received regarding components of the data storage system including any of a target port, a front end adapter, and a device. It is determined in accordance with inputs whether to modify existing connectivity between an initiator set of one or more initiators and a target set of one or more target ports of the data storage system. The inputs include the one or more statistics and one or more adjustment criteria. Responsive to determining to modify the existing connectivity, first processing makes modification(s) to the existing connectivity including any of: modifying an access state associated with a target port over which a device is exposed to an initiator, and adding or removing connectivity between the initiator and another target port of the data storage system where a device is exposed to the initiator over the another target port.