Hi! Welcome everybody to this presentation on Oracle Cloud Infrastructure operations
insights and oci native service i am ashi sagarwal one of the product managers
in oracle enterprise and cloud manageability group
operations insights service enables business executives database and it administrator to make
informed data-driven database resource and performance management decisions it has two
applications capacity planning and oracle sql warehouse capacity planning helps with optimizing
operational costs and Oracle sql warehouse is a long term store for SQL performance data
which can help you to maximize performance capacity planning application does trending
and forecasting of resource demand using 25 months historical data for oracle autonomous databases
it has linear and machine learning forecasting models inbuilt into the service
it provides automated daily forecast which predicts near-term capacity utilization issues
this this helps the the personas to be proactive about capacity
it helps to quickly isolate largest most utilized and fastest growing databases it answers questions
about underutilized servers so that someone can repurpose and reduce the operational cost.
Oracle sql warehouse provides insights into SQL performance using
25 months worth of historical data in in the following dimensions one can proactively
identify sequels that are degrading in performance sequels that are unpredictable in performance
one can identify application sql performance issues and and improve performance
even more if they fix this issues it helps to identify sequels which are changing
execution plans and provides insights into top SQLs across fleet of Oracle
autonomous databases by cpu and I/O you can find common patterns of SQL performance issues
across fleet of oracle autonomous databases as well as compare sql performance across prod
dev and test let's look at the actual demo of the service so after logging in into the Oracle cloud.
Operations inside service is part of monitoring and diagnostics so you can see operations insights
under monitoring and diagnostics let's look at the capacity planning application.
So, as you can see that i am doing capacity planning of my e-sales compartment
i have 48 databases and currently my time selector is showing 30 day 30 days
analysis i have 48 databases currently that's being analyzed it shows me the breakdown of
my autonomous databases into various categories this is the aggregated information about
cpu giving me information that i have 960 cpus and i am using currently 442
average active cpus and the growth rate in the last 90 days is a 15.49 percent
and storage i have 30 terabytes currently allocated and the uh e sales compartment
is uh utilizing 52.80 percent and the growth rate in last night last 30 days is 4.44 percent
the insights. Now first the cpu insights it gives what is being utilized high and what is
being utilized now and the second information we get auto in an automated way is what's the 30 day
utilization forecast and we can see that there are three databases which are projected to go over 75
percent utilization and as well as four DBs are going to be lowly utilized that is less than 25
percent even after 30 days of even after 30 days bear with me a moment we will drill down into it
the second widget is the top cpu consumer so we show top five currently based on uh usage so you
can see this particular database inv ed is using about 84.77 average active CPUs you can change
the dimensions now for example a usage percent so I can see a different picture that HR prod
based on the usage percent HR prod is now the top database are the top cpu consumers.
The CPU usage trend shows me what's my current allocation that's 960 cpus and
how my utilization is there based on the last 30 days on the bottom part
storage insights are provided what is my current utilization what's my 30-day forecast and very
similar uh what are the top storage consumers of my storage and the storage usage trend what's
the allocation and how on a time series basis how it is being utilized over the last 30 days
now let's select last 90 days and see how the picture looks like so i can see that now
my 30-day utilization forecast i have two databases so this is based on the past
90 days uh utilization let's drill down into our forecast of 30 days.
I can see that sale city is one of the databases
that's forecasted to go over 75 percent of cpu utilization.
You see the chart below it gives the trend and forecast so the green line currently
is i am highlighting is the average active cpu utilization based on average usage and
the forecast is 57.96 on 15th of November now let's select what's the max usage the max
usage really is a little different picture so this is where your peak utilization is happening
and let's do what is the max usage forecast so i can see that
my max usage on 15th of November is 77.99 so there is a big difference in the max usage
and the average usage if my application is very critical and i cannot tolerate
any kind of deviation in the performance.
I will have a capacity, I would like to have a capacity of 77.99 cpu at least
and if my application is not that critical and the average and i can tolerate some kind of
performance deviation I would perhaps go based on my um average utilization that happens to be 57.96
now there is more capabilities here in the service so let's look it on on an aggregated databases how
does our analysis looks like so let me select max utilization so you can see that based on
max utilization and if i want to do a grouping based on different types of databases and select
a compartment you know and do a forecast for that entire particular compartment so currently i am
seeing a forecast of my ATP shared entire ATP Shared databases i am doing the forecast on how
the forecast looks like now let's try to do uh and see if i can see any seasonality so i see
that i have selected the machine learning linear the currently i have selected the linear model
and i can see some patterns of uh utilization happening so if I select the machine learning
linear i see that my forecast also is seasonal it has a pattern to it so I know when exactly
my peaks are occurring and where the bottoms of my average active CPU utilization is happening.