Redfish® and Swordfish®: The Data Source for Predictive Models and Decision Making

Mon Sep 16 | 9:30am
Location:
Lafayette/San Tomas
Abstract

In today’s rapidly changing datacenters, it is very difficult to visualize your current equipment utilization, much less be able to predict when and where to expect bottlenecks, issues and failures to occur. Particularly in a multi-vendor solution, DMTF Redfish® and SNIA Swordfish® can bridge the gap to provide the key instrumentation needed.
This presentation will provide an overview of techniques and examples to use Redfish/Swordfish instrumentation, such as metrics and counters, to populate graphs and create predictive models of Asset utilization, Power and Temp metrics, and Capacity and Performance statistics. The models can facilitate decision making, chargeback opportunities, infrastructure optimization, and refresh/upgrade cycles.

Learning Objectives

Tame the chaos in your data center and eliminate waste
Take control of your refresh and upgrade cycles
Constrain costs and encourage users to make good business decisions based on the realities of the models

Abstract

In today’s rapidly changing datacenters, it is very difficult to visualize your current equipment utilization, much less be able to predict when and where to expect bottlenecks, issues and failures to occur. Particularly in a multi-vendor solution, DMTF Redfish® and SNIA Swordfish® can bridge the gap to provide the key instrumentation needed.
This presentation will provide an overview of techniques and examples to use Redfish/Swordfish instrumentation, such as metrics and counters, to populate graphs and create predictive models of Asset utilization, Power and Temp metrics, and Capacity and Performance statistics. The models can facilitate decision making, chargeback opportunities, infrastructure optimization, and refresh/upgrade cycles.

Learning Objectives

Tame the chaos in your data center and eliminate waste
Take control of your refresh and upgrade cycles
Constrain costs and encourage users to make good business decisions based on the realities of the models


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