AI Based Ethernet Storage Management

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Publish Date: 
Friday, May 24, 2019
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Abstract: 
The proliferation of applications for storing their data has increased adoption of Ethernet Storages operating over iSCSI/iSER/NVMe protocol. Its critical for business to maximize their availability implying the need to consistently monitor the performance of these storages to improve their efficiency. But debugging performance problems for Ethernet storages has below challenges
 
  1. Instead of monitoring the storage in isolation, there should be broader IP monitoring including other infrastructural devices like ports, Switches and host ports. 
  2. Predictive analysis considering whole of Ethernet storage fabric can proactively help identify the issues before their occurrence, this is because a configuration change in one component may impact performance of another component.
 
We provide the solution for Ethernet Storage management
 
  • Meta-data collector agent at the client data center: A small footprint agent, installed on the client data center that,at regular intervals gathers performance metrics for storage that it is monitoring. For further processing uploads metrics to cloud storage management service 
  • Cloud Storage management software: The Cloud-hosted SaaS service processes the uploaded from the agent and stores them to performs analytics on them. We are looking at applying data-science to understand the current status of environment and for any alarming situations it publishes alerts to the client, admins and support teams for further actions.
 
Learning Outcomes
 
a. Move a step ahead from single dimensional analysis of storage performance management from considering just stored-time-series data to multi-dimensional analysis
 
b. Understand how data-science is helping evolve storage management
 
c. Understand the challenges of Ethernet storage management domain

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