Intelligent Automated Cloud Resource Management
Key Points
- Traditional resource estimation fails because it can’t guarantee performance for complex, cloud‑native apps, often leads to costly over‑provisioning, and is unmanageable at human scale in multi‑cloud environments.
- Turbonomic for IBM Cloud Pak automates resource allocation by continuously analyzing application metrics across compute, network, and storage layers and adjusting capacity in real time without human intervention.
- The platform uses an AI‑driven supply‑and‑demand model that respects policy and cost constraints, automatically generating and executing balancing actions to meet target user response times while minimizing waste.
- By providing visibility, insights, and autonomous actions at every stack layer, Turbonomic enables applications to self‑select resources, absorb demand spikes, and eliminate guesswork in resource management.
Full Transcript
# Intelligent Automated Cloud Resource Management **Source:** [https://www.youtube.com/watch?v=wbgvAIuTiq4](https://www.youtube.com/watch?v=wbgvAIuTiq4) **Duration:** 00:03:17 ## Summary - Traditional resource estimation fails because it can’t guarantee performance for complex, cloud‑native apps, often leads to costly over‑provisioning, and is unmanageable at human scale in multi‑cloud environments. - Turbonomic for IBM Cloud Pak automates resource allocation by continuously analyzing application metrics across compute, network, and storage layers and adjusting capacity in real time without human intervention. - The platform uses an AI‑driven supply‑and‑demand model that respects policy and cost constraints, automatically generating and executing balancing actions to meet target user response times while minimizing waste. - By providing visibility, insights, and autonomous actions at every stack layer, Turbonomic enables applications to self‑select resources, absorb demand spikes, and eliminate guesswork in resource management. ## Sections - [00:00:00](https://www.youtube.com/watch?v=wbgvAIuTiq4&t=0s) **Intelligent Automated Cloud Resource Management** - It explains why manual or estimation‑based resource planning fails in modern multi‑cloud environments and how IBM’s Turbonomic automates metrics‑driven allocation to prevent over‑provisioning and app performance issues. ## Full Transcript
when it comes to application resource
management for cloud you don't want to
pay for over-provisioning on the other
hand you don't want to risk angry
customers if your applications start
failing due to lack of resources
this begs the question is intelligent
and automated resource management
critical for app performance for modern
organizations the answer is an easy yes
hi i'm dan keen from ibm cloud
when your apps run well your customers
have a great experience and your
development operations team remain
focused on their top initiatives but
before we dive into smarter resource
allocation i'll give three reasons why
traditional estimations simply don't
work
one
estimates can't assure the performance
of increasingly complex
applications two
estimates frequently try to assure
performance by over-provisioning
resource allocations
three
in multi-cloud and cloud-native
environments managing application
resources manually is beyond human scale
so what's the key to overcome these
barriers you need to automate the
allocation of application resources to
absorb shifting user demands and deliver
target response times that's why
turbonomic arm for ibm cloud pack exists
terminomic provides a top-down metrics
driven approach that continuously
analyzes application resource needs
resources are allocated through
visibility insights and actions at every
layer of the application and
infrastructure stack best of all these
decisions are made automatically without
human intervention
let's take a look
in addition to ibm observability by
instant apm terminomic integrates with
other apm systems they retrieve key
application metrics such as
infrastructure compute network and
storage all the resources your
application depends upon
terminomic then determines which
resources contribute to user response
time and provision appropriate capacity
to avoid contention
next let's turn our attention to
insights how can you assure resource
allocations perform in the smartest way
possible
it's all about the ai
terminomic maintains the necessary app
resources to deliver expected end-user
response times while respecting
configuration policies and minimizing
waste
think about the economic principles of
supply and demand
demand is based on production
application metrics the supplies based
on known resources and their policy and
cost constraints
smarter resource allocation means taking
intelligent action
an analytics engine automatically
generates and executes resource
balancing actions when and how you want
them it's done in real time manually
scheduled or part of a workflow
okay let's wrap this up
with terminomic applications choose
their own resources through visibility
insights and actions at every layer of
the stack
applications are dynamically resourced
for performance absorbing peak demands
and adhering your business policies
best of all you can abandon guesswork
and embrace smarter application resource
management thanks for watching if you'd
like to see more videos like this in the
future please click like and subscribe
and if you want to learn more about ibm
terminomic arm for cloudbacks make sure
to check out the links in the
description