Predictive Analytics and heterogeneous storage systems management using modernized services

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caroline
Published
The challenge for storage management administrators is monitoring large scale distributed storage subsystems deployed in data centers to cater high workload demands. The management platform that does forecasting capacity usage patterns, proactively finding data center element performance bottlenecks in drives, ports, IO rate, response times etc, health checks, notify SLA violations and cyber resiliency issues, helps administrators to plan the resources and prevent risks before they occur. The telemetry data from different data centers streaming to the data-lake service will give even better predictive insights in the finding issues. As millions of telemetry data points need to collect for analytics, the management platform has to be modernized using the scalable cloud native services based microservice architecture that is built on the services such as Cloud Object Storage, Cassandra databse, Kubernetes docker container, Kafka event streams, Spark etc. The experience of addressing different storage management monitoring challenges in performance, health and capacity using microserice based management platform will be shared.

Presented by: Ramakrishna Vadla, IBM
Category
Network Storage
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