Case 1: VMs performance can suffer due to resource constraints/surges
Case 2: Inefficient usage of resources due to reserving capacity for peak loads.
-Move VMs after contention occurs
-Statically reserve more resource
-Learn workload pattern, and move before VMs spike
What is the best solution? Predictive DRS
What is Predictive DRS?
-DRS enabled with predictions
-DRS scheduling + vROPs analytics
How does it work?
-Resource usage from vCenter
-vROPs consumes the data
-Predictions are made
-DRS invoked to perform optimizations
vROps Dynamic Thresholds (DT)
-Sophisticated analytics – 10 algorithms
-Learns normal behavior
-Detects hourly, daily, monthly patterns
-Generates upper and lower dynamic thresholds
-Predictions are then sent to vCenter
-vSphere 6.5 Enterprise Plus
-vROps 6.4 or 6.5
-Time sync between vCenter and vROps needs to be less than 5 minutes
Speaker shows a demo of a ‘follow the sun’ scenario with workloads spiking at different times on a regular pattern. pDRS learned the pattern, and vMotioned VMs to make sure VMs had enough resources. He shows a performance graph, where pDRS headed off performance issues and it resulted in consistent VM performance.
DPM with Predictions
–Speaker asks audience to raise hands if anyone is using DPM. Two people raise their hands.
-Predictions can proactively power up ESXi hosts to absorb the workload demand
-Workloads it can predict: Periodic usage pattern
-Short spikes of a few minutes will not be predicted
-The more consistent the workload, the more accurate it will be
-Set to 14 days by default
-The longer the period, the better the accuracy
-Predictions only happen after 14 days
-Compute dynamic thresholds – Calculated once a day, or push a button to force a new calculation.
-Lookahead interval – Amount of time DRS looks ahead while accounting for predictions – default is 1 hour
Identify vMotions due to Preditions
-Not a clear answer as there can be a mix of VMs with predictions and those without
-pDRS moves are only in logs