How Federator.ai Meets Gartner Cloud Cost Management Guidance Framework and More? ProphetStor's solution Federator.ai aims to solve the problems by using AI/ML technology and to produce recommendations that optimize the cost and performance. With predictive analytics, we can understand application behaviors from the metric data we collect from Prometheus, and other metric data sources. Utilizing the ability to know the trend and workload demands pattern, Federator.ai can autoscale applications in a more proactive and intelligent approach. For more information, please visit https://www.prophetstor.com Check out ProphetStor's demo video, please visit https://prophetstor.com/2022/03/02/federator-ai-feature-demo/

1. Cloud Cost Optimization and Application
Resilience with ProphetStor
How Federator.ai Meets Gartner Cloud Cost
Management Guidance Framework and More
Ming Sheu, EVP of Products
ming.sheu@prophetstor.com

2. Technology and Comparison
1

3. Federator.ai -
Machine Learning for Proactive Operation Optimization
r.■2
ProphetStor
»
「 Dynamic Workload Metrics]
»
Just-in-Time Fitted resource
recommendations for
applications and clusters
旦
s
u
e
O
三
。
r
P
vmware
sysdig
Amazon CloudWatch
Time-Series Forecasting
Reinforcement Learning
for Decision-making
Predictive Analytics
Multi-Layer Correlation for
tremendous computing
resources savmg
辶
Prediction-based resource
orchestration to ensure
application performance
辶
Intelligent autoscaling and
auto resource provisioning
for dynamic workloads
Copyright©2022 ProphetStor 2

4. Copyright©2022 ProphetStor 3
ProphetStor: Proac�ve Opera�on Cures the Pains
Federator.ai u�lizes data already collected to provide further values to automate
opera�on and op�mize performance and cost
Application Smart
Directives for
Optimization
Operation
Simplicity
How ProphetStor Can Help
Workloads
Dynamics
Applica�ons
KPIs
ML-based Analy�cs
Private Cloud Public Cloud
Multi-Layer
Correlation and
Impact Analysis
Monitoring Performance
Resilience
Cost
Optimization

5. Data Collection
(Input)
Recommendations
(Output)
Recommendations
(Output)
Data Path
Metrics
Virtualiza�on / Containeriza�on Layer
Validation / Benchmarking
Device Discovery, Inventory
and Management
Storage
Firmware & OS
Deployment
Networking
SW Repository
Cloud
Instances
Server # 1 Server # 2
Operation Data
(APIs)
Recommendations
(APIs)
Recommendations
(APIs)
Copyright©2022 ProphetStor 4
Management
(APIs)
SAI
HPE Ezmeral

6. Data Collection
(Input)
Recommendations
(Output)
Recommendations
(Output)
Data Path
Metrics
Virtualiza�on / Containeriza�on Layer
Validation / Benchmarking
Device Discovery, Inventory
and Management
Storage
Firmware & OS
Deployment
Networking
SW Repository
Cloud
Instances
Server # 1 Server # 2
Operation Data
(APIs)
Recommendations
(APIs)
Recommendations
(APIs)
Copyright©2022 ProphetStor 5
Management
(APIs)
SAI
HPE Ezmeral
Pubic Clouds (AWS)
On-Prem and BareMetal Cloud Infra
Pla�orms (Virtualiza�on/Containeriza�on)
VMware/Kubernetes/OpenShi�
Applica�ons
Monitoring
Patented Applica�on-Aware AI-Centric Opera�on Op�miza�on

7. How Federator.ai Meets and Exceeds the
Expectation Set by Gartner
6

8. Gartner's Guidance Framework
Figures shown as Gartner's recommendation and insights are taken
from "How to Manage and Optimize Costs of Public Cloud IaaS and
PaaS," Gartner Research Report, March 2020 by Marco Meinardi
and Traverse Clayton 7

9. Gartner Cloud Cost Guidance
Framework: Plan and Track Forecast
Federator.ai: Machine learning-based resource
usage predictions and recommendation
Federator.ai: Forecast and recommend resource
allocation for the next 24 hours, 7 days, and/or 30
days
Federator.ai
8

10. Gartner Cloud Cost Guidance
Framework: Plan and Track Forecast
Federator.ai: Apply to resources from
containers to clusters
Federator.ai
9

11. Gartner Cloud Cost Guidance Framework:
Track -- Cost Breakdown
10
Gartner: cost breakdown for show-back and
charge-back
Federator.ai: cost breakdown based on
applications or projects with trending and predicted
costs
Federator.ai

12. Gartner: Building a weekly utilization pattern for
compute instance
Gartner Cloud Cost Guidance Framework:
Reduce -- Understanding Utilization
Observed Utilization Predicted Utilization
Federator.ai: building weekly/monthly utilization
pattern for clusters, cluster nodes, applications
and containers. Predict future resource utilization.
Federator.ai
11

13. Gartner Cloud Cost Guidance Framework:
Reduce -- Continuous Rightsizing
12
Gartner: Resource utilization chart with
continuous rightsizing.
Federator.ai: Resource utilization charts for
cluster, cluster nodes, applications, and containers
for continuous rightsizing at different levels.
Federator.ai

14. Gartner Cloud Cost Guidance Framework:
Optimize Cloud Cost with Federator.ai
Gartner: Optimize Public Cloud Cost
Federator.ai: Application-aware prediction-based
intelligent Auto Scaling in full layers for optimizing
cloud cost without sacrificing performance.
Different Instance types (On-Demand, Reserved,
and Spot) can be scheduled based on the
application workload types and the matching of the
instances locations and costs to maximize the
saving and achieve resilience.
Federator.ai

15. 14
Federator.ai: Recommend most cost-effective
instances combinations including on-demand,
reserved, and Spot instances for leading public
cloud service providers based on predicted
workload.
Gartner: Optimize Public Cloud Cost
Federator.ai
Gartner Cloud Cost Guidance Framework:
Optimize Cloud Cost with Federator.ai

16. Further Enhancements for Cloud Cost Management:
Federator.ai vs Conventional Tools
Conventional Cost Management Tools
- Use billing information of Cloud usages to analyze
cost trends and cost breakdown
- Cluster node utilization analysis only, very few
with application-level analysis/insight
Federator.ai Cloud Cost Optimization
- Machine learning-based workload predictions for
cloud cost optimization
- More fine-grained cost and multi-layer correlation
analysis, from containers and applications to
namespaces and clusters, to facilitate planning
with precision
- Auto-provision container/application resource
allocations based on foresight and not data in the
past, making it possible to scale intelligently.
- Autoscaling container based on workload
predictions
- Applicable to MultiCloud deployments 15

17. Federator.ai Values to Enterprise Customers
F·2
ProphetStor
➔>>
➔>>
➔>>
Prediction-based Cost Optimization - Easily 35% to 80%
savings 」
Green IT - Reduced idle compute resources for more
efficient energy consumption
/
Application Insight - Machine-learning model for
application correlation; predicted resource analytics
strengthen resiliency
Copyright©2022 ProphetStor 16

18. Business with MSPs and Cloud
Service Providers
17

19. Copyright©2022 ProphetStor
Federator.ai Helps Customers Maximize and Automate
Federator.ai uses the exis�ng metrics collected from end-user workloads
Federator.ai runs in the Management Cluster, remotely collec�ng workload metrics from mul�ple clusters in
an Mul�Cloud environment
Federator.ai knows workload on diff layer for future
Continuous
workload
metadata
collecting
automatically
(including
historical)
On-Prem
end-user’s
Kubernetes
Cloud
end-user’s
Kubernetes
CloudWatch
Cloud
end-user’s
AWS Instances
User/
MSP
Management
Cluster
Continuous
Right-Sizing
and
Optimization
18

20. Copyright©2022 ProphetStor
Federator.ai Helps Customers Maximize and Automate
Federator.ai uses the existing metrics collected from end-user workloads
Federator.ai runs in the Management Cluster, remotely collecting workload metrics from multiple clusters in
an MultiCloud environment
Federator.ai knows workload on diff layer for future
Continuous
workload
metadata
collecting
automatically
(including
historical)
On-Prem
end-user’s
Kubernetes
Cloud
end-user’s
Kubernetes
CloudWatch
Cloud
end-user’s
AWS Instances
User/
MSP
Management
Cluster
Continuous
Right-Sizing
and
Optimization
19

21. Copyright©2022 ProphetStor
Federator.ai Helps Customers Maximize and Automate
Federator.ai uses the exis�ng metrics collected from end-user workloads
Federator.ai runs in the Management Cluster, remotely collec�ng workload metrics from mul�ple clusters in
an Mul�Cloud environment
Federator.ai knows workload on diff layer for future
Right sizing for cluster based
on workload prediction
Continuous
workload
metadata
collecting
automatically
(including
historical)
On-Prem
end-user’s
Kubernetes
Cloud
end-user’s
Kubernetes
CloudWatch
Cloud
end-user’s
AWS Instances
User/
MSP
Management
Cluster
Continuous
Right-Sizing
and
Optimization
20

22. Copyright©2022 ProphetStor
Federator.ai Helps Customers Maximize and Automate
Federator.ai uses the exis�ng metrics collected from end-user workloads
Federator.ai runs in the Management Cluster, remotely collec�ng workload metrics from mul�ple clusters in
an Mul�Cloud environment
Federator.ai knows workload on diff layer for future
Recommended for RI + On Demand
Continuous
workload
metadata
collecting
automatically
(including
historical)
On-Prem
end-user’s
Kubernetes
Cloud
end-user’s
Kubernetes
CloudWatch
Cloud
end-user’s
AWS Instances
User/
MSP
Management
Cluster
Continuous
Right-Sizing
and
Optimization
21

23. Copyright©2022 ProphetStor 22
Federator.ai Helps Customers Maximize and Automate
Federator.ai uses the existing metrics collected from end-user workloads
Federator.ai runs in the Management Cluster, remotely collecting workload metrics from multiple clusters in
an MultiCloud environment
Federator.ai knows workload on diff layer for future
Recommended for RI + On Demand
Recommended for RI + Spot
Continuous
workload
metadata
collecting
automatically
(including
historical)
On-Prem
end-user’s
Kubernetes
Cloud
end-user’s
Kubernetes
CloudWatch
Cloud
end-user’s
AWS Instances
User/
MSP
Management
Cluster
Continuous
Right-Sizing
and
Optimization