This document discusses using graphs to model relationships between cloud services from different providers to help with service selection for multi-cloud environments. It presents graphs as a way to capture compatibility, replaceability, and other conditional relationships between services. Centrality metrics, community detection algorithms, and graph pattern matching could then be applied to the modeled graphs to analyze service configurations and identify options that meet requirements for adaptability, quality, and cost. An example graph instantiation is provided to demonstrate compatibility modeling between specific database and application services from Amazon, Google, Microsoft, and other cloud providers.

1. Use of Graphs for Cloud Service Selection
in Multi-Cloud Environments
Victor Muntés Mulero, CA Strategic Research Labs, CA Technologies
Smrati Gupta, CA Strategic Research Labs,CA Technologies
Jaume Ferrarons Llagostera,CA Strategic Research Labs,CA Technologies
March 4th
, 2016

2. 2 © 2016 CA. ALL RIGHTS RESERVED.
Why Multi-Cloud?
§ Avoid vendor lock-in.
§ Reduce cloud outages and
service downtime.
§ Higher flexibility and cost
reduction.
§ Easier to meet governmental
policies.
§ Obtain more features.
§ Monopoly reduction in the
cloud market.

3. 3 © 2016 CA. ALL RIGHTS RESERVED.
Analysts see that Cloud World is Moving from (a) to (b)
(a) Different instances running on
different provides.
(b) A single instance using
services form multiple providers.

4. 4 © 2016 CA. ALL RIGHTS RESERVED.
Problem Description
How to choose the right multi-cloud environment?
OPTION 1: I need to
add another service
to my application,
but it comes from a
different supplier
OPTION 2: I need
to create an
identical set of
services from a
different supplier
I need to be
flexible and
adaptive
I need to
ensure quality
I need to
reduce costs

5. 5 © 2016 CA. ALL RIGHTS RESERVED.
Problem Modeling Using Graphs
§ Capture the dynamics between pairs of cloud services using
labeled weighted multigraphs with attributes on edges and
nodes:
1. Compatibility among instances of different types.
2. Factors conditioning the compatibility such as latency.
3. How easy it is to replace it by other services.
Service
A
Service
B
structural compatibility
migration compatibility
conditional compatibility

6. 6 © 2016 CA. ALL RIGHTS RESERVED.
Instantiation Example
Google
Apps Eng.
App Engine
DataStore
Google
Cloud SQL
NuoDB
Xeround
Amazon
DynamoDB
Azure
Table Store
Amazon
RDS
Azure SQL
Database
Amazon
EC2
Task
Queue
Amazon
EC2
m=0.95
m=0.8
m=1
m=1
m=1
m=0.5
m=0.9
m=0.9
m=0.8
m=0.7
m=0.4
m=0.6
m=0.8
m=0.8
Response time > 1s
Cost > 2500 €
structural compatibility
migration compatibility
conditional compatibility

7. 7 © 2016 CA. ALL RIGHTS RESERVED.
Exploring the Use of:
§ Centrality metrics to create service goodness measurements,
describing adaptability aspects in multi-cloud environments.
§ Community search algorithms to find the subset of services
that are compatiblewith a given service under a certain set of
restrictions set up by the user.
§ Graph pattern search to find configurations that satisfies the
architectural requirements of the application.

8. Associate Research Engineer & PhDCandidate
jaume.ferraronsllagostera@ca.com
Jaume Ferrarons Llagostera
@cainc
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linkedin.com/company/ca-technologies
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