MODAClouds Decision Support System for Cloud Service Selection by Smrati Gupta (CA technologies)

1. MODAClouds
Decision
Support
System
for
Cloud
Service
Selec8on
Smra8
Gupta
CA
Labs,
CA
Technologies
20th
of
March
2015
LDBC
Sixth
TUC
Mee8ng,
UPC,
Barcelona

2. 2
©
2015
CA.
ALL
RIGHTS
RESERVED.
Outline
Objec8ve
of
the
talk
Need
for
Decision
Support
System
in
Cloud
service
selec8on
Overview
of
MODAClouds
DSS
Key
Features
of
DSS
Open
Discussions
for
DSS
in
graph
database
community

3. 3
©
2015
CA.
ALL
RIGHTS
RESERVED.
Why
are
we
here?
Decision
Support
System
and
graph
databases
CALabs
Barcelona
team
has
organically
developed
a
novel
technology
in
the
form
of
Decision
Support
System
as
a
part
of
MODAClouds
project.
Graph
database
community
is
evolving
and
there
lies
poten8al
to
use
the
DSS
technology
in
addressing
the
graph
database
selec8on
problem
Objec8ve
of
this
talk
is
to
start
brainstorming
in
the
community
about
possible
usage
of
the
technology
to
assist
and
enhance
the
use
of
graph
databases
in
enterprises

4. 4
©
2015
CA.
ALL
RIGHTS
RESERVED.
Need
for
Decision
Support
System
in
cloud
service
selec8on
Mul8ple
dimensions
of
choices
• Trustworthy
Vendors
• Financial,
Legal,
Organiza8onal
and
Technical
constraints
Mul8-­‐cloud
environment
compa8bility
issues
• Interoperability
• Ease
of
migra8on
• Vendor
lock-­‐in
Recommenda8on
based
on
different
dimensions
• Cost
• Quality
• Risk

5. 5
©
2015
CA.
ALL
RIGHTS
RESERVED.
What
DSS
does
for
the
users?
MODA
Clouds
DSS
Architectural
model
of
deployment
(Tangible
Assets)
Architectural
deployment
model
enriched
with
user
selected
cloud
services
MODAClouds
User
Cloud
Service
Recommenda8ons
Technical
and
Business
oriented
Intangible
assets
and
Risk
Acceptability
level
per
asset
Relevant
Risks
and
Treatments
Selected
cloud
service
alterna8ves

6. 6
©
2015
CA.
ALL
RIGHTS
RESERVED.
MODAClouds
DSS:
Key
features
§ Mul8ple
Stakeholder
par8cipa8on
§ Risk-­‐analysis
based
Requirement
genera8on
§ Mul8-­‐Cloud
Environment
Compa8bility
§ Data
gathering
§ Progressive
Learning

7. 7
©
2015
CA.
ALL
RIGHTS
RESERVED.
Mul8ple
actors,
mul8ple
perspec8ves
§ Different
stakeholders
may
influence
Cloud
Service
selec8on
in
different
ways
Risk Policy
Manager
Decision
Owner
Architect
System
Operator
Feasibility
Study
Engineer
7

8. 8
©
2015
CA.
ALL
RIGHTS
RESERVED.
Asset
defini8on
by
mul8ple
actors
Business
Analyst
Assets
Product
Innova8on
and
Quality
Legisla8on
Compliance
Sales
Rate
Customer
Loyalty
Market
Awareness
Business-Oriented
Intangible Assets
8
Technical-Oriented
Intangible Assets
Assets
Data
Privacy
Data
Integrity
End
User
Performance
Maintainability
Service
Availability
Cost
stability
Technical
Team
Assets
Compute
(IaaS)
File
System
(IaaS)
Blob
storage
(IaaS)
Rela8onal
(PaaS)
Middleware
(PaaS)
NoSQL
(PaaS)
Backend
(PaaS)
Frontend
(PaaS)

9. 9
©
2015
CA.
ALL
RIGHTS
RESERVED.
Risk
analysis
methodology
Business
Oriented
Intangible
Asset
Defini8on
Technical
Oriented
Intangible
Asset
Defini8on
Tangible
Assets
Defini8on
Risk
defini8on
Treatments
Defini8on
§ Risks
are
iden8fied
on
the
basis
of
protec8ng
the
assets
§ Treatments
are
defined
to
mi8gate
one
or
more
risks
§ The
outputs
can
be
refined
itera8vely
allowing
users
to
go
back
in
the
methodology
and
update
informa8on
9

10. 10
©
2015
CA.
ALL
RIGHTS
RESERVED.
Mul8-­‐Cloud
environment

11. 11
©
2015
CA.
ALL
RIGHTS
RESERVED.
Challenges
in
Mul8-­‐Clouds
11
• Interoperability:
Risk
of
unexpected
lack
of
replacement
and
consequent
vendor
lock-­‐in
• Migra8on:
Risk
of
non-­‐viable
migra8on
due
to
migra8on
costs
and
complexity
Vendor
lock-­‐in
• Risk
of
new
security
breaches
due
to
the
increased
complexity
of
the
system
and
new
communica8ons
Security
• Risk
of
unavailability
of
evidences
in
case
of
fraudulent
ac8ons
Forensic
Evidences
• Risk
of
costs
unpredictability
Cost
unpredictability
• Risk
of
lack
of
provider
interest
in
collabora8on
Lack
of
interest
of
CSPs
• SME
or
companies
using
mul8ple
services
from
mul8ple
vendors
are
unlikely
to
have
the
power
or
the
8me
to
nego8ate.
Increasingly
unstable
cost
and
T&C
problem.
Lack
of
nego8a8on
on
SLAs
capacity

12. 12
©
2015
CA.
ALL
RIGHTS
RESERVED.
DSS
–
Automa8c
Data
Gathering
Concept
DSS
Database
Graph
building
and
data
transforma8on
Structured
flat
data
fetch
JSON
Database
Interface
XML
REST
JSON
XLSX
WSDL
NoSQL
SQL
Internet
Flat
files
Databases
Graph

13. 13
©
2015
CA.
ALL
RIGHTS
RESERVED.
Progressive
Learning
Storage
of
User
input
Storage
of
selec8on
of
services
Storage
of
thresholds
and
benchmarks
Subsequent
recommend
-­‐a8on
on
selec8on
Subsequent
recommend
a8on
on
services
• With
repeated
use
of
DSS,
the
previous
user
logs
and
stored
and
simple
analysis
is
performed
• The
recurring
users
are
recommended
possible
assets
that
might
be
crucial
to
their
firm
• The
users
are
also
recommended
certain
risks
that
have
been
chosen
by
other
users
• The
users
are
also
recommended
the
value
of
each
cloud
service
property
based
on
previous
use
of
DSS
• With
the
repeated
usage,
DSS
learns
and
improves
its
recommenda8ons

14. 14
©
2015
CA.
ALL
RIGHTS
RESERVED.
Ground-­‐up
developed
Prototype
by
CALabs

15. 15
©
2015
CA.
ALL
RIGHTS
RESERVED.
Open
Source
Technology
Support
for
DSS
• hmp://dss.tools.modaclouds.eu/
DSS
open
source
tool
available
at:
• hmps://github.com/CA-­‐Labs/DSS
Documented
and
available
in
github
repository
at:
• hmp://www.modaclouds.eu/
MODAClouds
Documenta8on

16. 16
©
2015
CA.
ALL
RIGHTS
RESERVED.
Open
Discussion
-­‐
What
are
the
characteris8cs
that
would
define
the
quality
of
a
cloud
graph
database?
-­‐
What
criteria
are
important
in
the
selec8on
of
(cloud)
graph
databases?
Who
makes
the
decisions
in
industry
to
select
a
par8cular
graph
database
technology
for
a
company?
How
does
the
graph
database
community
plan
to
manage
legi8mate
customer
concerns
such
as
preven8on
of
vendor
lock-­‐in
and
cloud
outages?
Is
the
synchroniza8on
of
mul8ple
graph
databases
provided
by
different
vendors
possible?
Is
gathering
data
with
respect
to
different
characteris8cs
that
define
the
quality
of
the
graph
database
an
important
concern?
How
could
a
DSS
help
for
cloud
graph
database
selec8on?

17. 17
©
2015
CA.
ALL
RIGHTS
RESERVED.
Thank
you
for
your
amen8on!

18. Sr.
Research
Engineer
Smra8.Gupta@ca.com
Dr.
Smra8
Gupta