Capture, Monitor and Analysis of Real World Edge Workloads for Server and Application Optimization

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Publish Date: 
Wednesday, September 23, 2020
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Abstract: 

Real World Edge workloads, from IoT to servers, edge servers, nodes and datacenter storage servers, are highly effective for optimizing server storage and applications. See how edge workloads are monitored and captured for workload balancing, curation and test script creation. Edge workloads are also monitored in real time to balance server loads and provide real time alerts for Key Performance Indicators. Curated Edge workloads are also used to optimize storage and applications and to provide Training for AI Machine Learning Long Short Term Memory Recurrent Neural Networks (AI ML LSTM RNN).

Learning Objectives

See what edge workloads look like,Observer real time monitoring and alerts for Key Performance Indicators,Curate workloads to create relevant test workloads,Run curated workload tests for storage & application optimization

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