1. POWER-AWARE MULTIDATACENTER MANAGEMENT
USING MACHINE LEARNING
Presented by: Omar Sulca
CLOUD
COMPUTING

2. CONTENT
1.
Introduction
2.
What they looking for?
3.
What is Multi Data Center?
4.
Managing Multi DCs
5.
Modeling the System
6.
Conclusions

3. 1. INTRODUCTION
 Cloud Computing, has become crucial for the externalization of IT resources for
business, organizations and people.
“everything as a service”
(plataform, infrastructure and service)
 Providers want in turn to optimize the use of the resources they have deployed
with their own metrics

4. 1. INTRODUCTION
 Factors to be optimized
Revenues
Costs
• came from servicing the clients of the hosted
web-services with reasonable Quality of
Service (QoS)
• operational costs for the infrastructure
(Energy-realeted cost)
 Consolidation - Set the maximum number of services in the least viable amount
of hosting machines, so the number of on-line machines and resources is
minimized.
 Virtualization technology has made consolidation easier,

5. 2. WHAT THEY LOOKING FOR?
“Build a model to automate (AC) an improve the process of achieve
allocation of virtualized web-services, using a Machine Learning (ML)
and Data Mining, to predict behavior and select “policies” to be applied
in a multi-DC”
Energy Saving in Cloud Self-management

6. 3. WHAT IS MULTI DATA CENTER?
• Its a Networking of Data Centers (DCs) interconected
Must be considerate
Migration overheads
Service-Client proximity
Energy cost at diferent locations
Modularity between inter-DC relations an
information

7. 4. MANAGING MULTI DCS
3
Multi-DataCenter Business Model
SLA (Service Level Agreement)
2
1
4
ensure the agreed QoS for
de VM, while minimizing the
cost by reducing the
resorces usage

8. 5. MODELING THE SYSTEM
 In this case
Quality of Service = Response Time
Mathematical Model
(monitoring PM resources
and adjusting VM placements
and quotas)
Using
Machine Learning + Data Mining
to
Around the world
Predict behavior and
Scheduling the VM
Across de DC networks

9. 5. MODELING THE SYSTEM
 The Machine Learning decisive
factors
 Energy consumption
 Resource allocation
 QoS
 Questions to predict
 How good will each VM behave?
 How much CPU/Mem/IO… will each
VM demand?

10. 6. CONCLUSIONS
1.
Optimizing the schedule and management of multi-DC systems requires balancing
several factors, like economic revenues, Quality of Service and operational costs
such as energy.
2.
Using virtualization technology is presented a model to solve a multi-DC
scheduling problem which balances and optimizes the economic factors above.
3.
A few issues for future study are:
a)
How decide which VMs are excluded from inter-DC scheduling or which PMs are offered
as host candidates for scheduling;
b)
The inclusion of more operational costs (networking, bandwidth management,etc.)
c)
The green energy into the scheme and the environmental impact of computation.
d)
The use of online learning methods to make the system react quickly to changes
(application behavior, hardware or middleware changes, or workload characteristics

11. Thanks