Abstract
In today’s competitive business environment, storage management providers are continually working towards improving the business value to their customers. The enterprises are deploying large scale distributed storage subsystems to cater high workload demands. The challenge for the storage management services is proactively finding performance bottlenecks, health checks, notify risks and prevent before they occur. The prior knowledge of the known issues from other customer storage deployments for correlation is a challenge. To address these challenges, they are implementing solutions based on the Artificial Intelligence including Machine Learning (& Deep Learning) which require large data sets to derive insights and run predictive analytics. The data from different customer deployments will give even better predictive insights. It requires architectural changes to the storage management services to deploy on the cloud designed by cloud native services those are reliable and auto scalable. The cloud native services such as kubernetes cluster, docker, lambda functions, object storage, elastic search, API gateway services and NoSQL e.g. dynamodb/cassandra for data lake are helping to manage storage infrastructure seamlessly. The cloud based AI services such as Amazon Sagemaker/ IBM Watson/MS Azure ML are used for integrating with data lake to run predictive analytics. The experience of addressing different storage management challenges using cloud native services will be shared.
Learning Outcomes
a. Inevitable need of Artificial Intelligence (AI) application in storage management
b. Understanding and choosing the right set of cloud native services
c. Future of storage management service architecture