Cloud Manager Automated Load Balancing for Storage Arrays
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Solution Overview
Problem
Current cloud data storage systems rely on manual performance management, which is inefficient and lacks automated load balancing capabilities, leading to suboptimal I/O workload distribution across data storage arrays.
Innovation Solution
A system with a cloud manager that monitors performance issues across multiple data storage arrays, determines the need for LUN migration, and configures resources to balance I/O loads automatically, minimizing user intervention and ensuring seamless migration without disrupting applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual performance management is used, then system simplicity is maintained, but automation level and I/O workload distribution efficiency deteriorate
Solution Approach 1:
A cloud manager is introduced as an intermediary component between hosts and data storage arrays. The cloud manager monitors performance metrics, detects performance issues, and automatically makes load balancing decisions by migrating LUNs between storage arrays, thereby automating performance management without requiring complex configurations by users.
Solution Approach 2:
The system enables self-service automation where the cloud manager autonomously monitors storage array performance, identifies performance issues, and executes load balancing operations by migrating LUNs without human intervention. This self-managing capability improves automation while keeping the user interface simple.
2Productivity
If manual load balancing management is used, then device complexity is reduced, but productivity and I/O workload distribution efficiency deteriorate
Solution Approach 1:
The cloud manager continuously monitors performance metrics from data storage arrays and uses this feedback to automatically detect performance issues and trigger load balancing operations. When performance degradation is detected, the system automatically migrates LUNs to balance I/O workloads, improving productivity through data-driven automated decision-making.
Solution Approach 2:
Manual mechanical management operations are replaced with automated software-based management. The cloud manager uses software agents to monitor performance and automatically executes LUN migration operations, replacing manual administrative tasks with automated computational processes that improve productivity.
3Reliability
If automated monitoring and LUN migration is implemented, then I/O workload distribution is optimized, but system complexity increases
Solution Approach 1:
The cloud manager serves multiple functions within a single integrated system: it monitors performance metrics across storage arrays, detects performance issues, makes load balancing decisions, and executes LUN migration operations. This multi-functional approach improves system performance while consolidating complexity into a single management platform rather than distributing it across multiple separate systems.
Data Source
AI summary
A system, computer program product, and computer-executable method of balancing Input/Output (I/O) loads for cloud data storage systems including a plurality of hosts and a plurality of data storage arrays, the system, computer program product, and computer-executable method including monitoring, via a first host of the plurality of hosts, a status of a first data storage array of the plurality of data storage arrays, upon detecting a performance issue with the first data storage array, notifying a cloud manager of the first data storage array, wherein the cloud manager is in communication with each of the plurality of hosts and each of the plurality of data storage arrays, monitoring, via the cloud manager, the performance issue, and determining, via the cloud manager, whether to move at least one LUN from the first data storage array.