The document outlines the DICE project, a Horizon 2020 initiative aimed at aiding SMEs in developing data-intensive cloud applications through improved tools and methodologies. It emphasizes the challenges faced by SMEs with legacy systems and the high failure rate of big data projects, while presenting solutions such as the DICE platform and TOSCA for deployment automation. The DICE tools aim to streamline the deployment process, enhance application quality, and enable continuous integration and delivery.

1. DICE
Horizon 2020 Research & Innovation Action
Grant Agreement no. 644869
http://www.dice-h2020.eu
Funded by the Horizon 2020
Framework Programme of the European Union
DICE:
quality Big Data made easy
Matej Artač @matej_artac
XLAB Research @xlab_research
Michele Guerriero, Damian A. Tamburri
Politecnico di Milano

2. Agenda
o Introduction
o About DICE
 Developing Data-Intensive Cloud Applications with
Iterative Quality Enhancements
o DICER
o TOSCA
o Cloudify
o DICE delivery tools
2

3. Introduction
3

4. Building blocks for DIAs today
4
Coordinator (Kafka)
Orchestrator (Hadoop Cluster)
Data
Store
Batch
Layer
Speed
Layer
Serving
Layer
Serving
Layer
Serving
Layer
Data
Source
Data
Source
Distributed
computation
Data streaming
HDFS
Distributed storage
Lambda architecture Cloud infrastructure

5. What problems EU SMEs face?
5
Traditional market:
Legacy software systems
Customers with legacy
data now ask for Big
Data technologies
Growth in sight, but …
Learning curves
Initial prototyping
Risk of failure
(+ others…)
Fast-paced market

6. Traditional approach to deployment
o Spend time studying
documentation
o Use trial and error to set up
a working cluster
o Use incompatible public
cookbooks
o Repeat for each change
o Keep the Big Data cluster
fixed for fear of breaking it
days

7. What is in the box?
o DICER
 model your application
 create a TOSCA blueprint
o Cloudify
 Pure-play orchestration & automation
o DICE deployment tool
 alternative front-end for Cloudify
o TOSCA library
 worries about deploying Big Data
services so that you don’t have to
7

8. DICE
Developing Data-Intensive Cloud Applications with
Iterative Quality Enhancements
8

9. The Rapid Growth of Big Data
9
o Software market rapidly shifting to Big data
 27% compound annual growth rate through 2017 (IDC)
 Popular technologies such as Spark, Hadoop, and NoSQL
boost Big Data adoption and revenues from new services
Business issue: 65% of Big data projects still fail (CapGemini’15)
Source: IDC Source: Wikibon

10. DevOps toolchains in innovation
10
Application Release Automation
Continuous Delivery
DevOps Toolchain

11. DICE Mission and Partners
 ICT 9 Call/2014 – Software engineering
 9 partners (Academia & SMEs), 7 EU countries
11
Mission: support SMEs in developing high-quality
cloud-based data-intensive applications (DIAs)
(IEAT)
(IMP)
(PMI)
(ZAR) (NETF)
(XLAB)
(ATC)
(FLEXI)(PRO)

12. Ingredients of the DICE approach
o DevOps
o Model-Driven Engineering
12
Dev Ops Dev Ops
Analysis
Deployment blueprint

13. DICE incremental modeling and analysis
13
DICE Platform Independent Model (DPIM)
DICE Technology Specific Model (DTSM)
DICE Deployment Specific Model (DDSM)
is implemented by
is deployed onto
TOSCA
blueprint
Analysis
Analysis
Analysis &
Optimization
M2M transformation
M2M transformation
M2T transformation
DICEMethodology

14. DICE deployment, monitoring and testing
14
Deployment
Testbed
Monitoring
Fault Injection
Quality
Testing
Trace
Checking
Enhancement
Anomaly
Detection
Running
DIA
Comp
MW
VM
Running
DIA
Comp
MW
VM
Configuration
optimization
TOSCA
blueprint
DICEMethodology

15. DICE architecture
15
IDE
based on
Eclipse
Profile
Simulat
ion
Optimiz
ation
Verifica
tion
Repository
& CI
Configuration
Optimization
Delivery
Running
DIA
Comp
Running
DIA
Comp
Monitoring
Trace
Checking
Enhancement
Anomaly
Detection
Fault Injection
(Resilience)
Quality
Testing
MW
VM
MW
VM
MW
VM
Running
DIA
Comp
DICER

16. XLAB delivery tools
16
Delivery
Running
DIA
Comp
Running
DIA
Comp
MW
VM
MW
VM
MW
VM
Running
DIA
Comp
DICER

17. DICER
Create actionable deployment diagrams of
Data Intensive Applications
17

18. DICER
o Assisted Component-based infrastructure design
o 100% automation of model transformations
18

19. Bordeaux Plenary
DICER in action: Assisted modelling!
19
1. <<Jerry: I’m modelling this DDSM thingie… I don’t know what I’m doing!>>
2. <<DICE: Hey Jerry! You’re
missing this piece here… And
there!>>
…

20. Bordeaux Plenary
DICER in action: Assisted modelling!
20
3. <<DICE: ok that’s better now… Carry on…>>

21. OASIS TOSCA
OASIS Topology and Orchestration Specification
for Cloud Applications
21

22. What is TOSCA?
o Open standard
o Enabling a unique Cloud
eco-system
o Supported by a large
and growing number of
international industry
leaders
22
Associated Companies

23. TOSCA is an Intent Model which is declarative
(integration points for imperative)
TOSCA Domain-Specific Language
Information Models
Typically, used to model a constrained
domain that can be described by a
closed set of entity types, properties,
relationships and operations.
Data Models
Typically, describe the structure
(format), enabling manipulation (via
interfaces) of the data stored in data
management systems assuring
integrity.
• Topology
• Composition
• Requirements - Capabilities
• State (Nodes, Relationships)
• Lifecycle (Management)
• Policy
Intent Model Adds:
• Structure
• Format
• interfaces
• Types, Relationships
• Properties
• Operations
 TOSCA can work with
imperative scripts
(e.g., Ansible, Chef,
Bash, Ant, etc.)
 TOSCA can include
other data models
(e.g., JSON, YANG)

24. Tier (Group Type)
TOSCA is used first and foremost to describe the topology of the deployment view for
cloud applications and services
Topology – Nodes and Relationships
24
source_resource
Node_Type_A
target_resource
Node_Type_B
Requirement
connect_relationship
ConnectsTo
Capability
Nodes - are the resources
or components that will be
materialized or consumed
in the deployment
topology
Relationships
express the dependencies
between the nodes (not the
traffic flow)
Requirement - Capability
Relationships can be
customized to match specific
source requirements to target
capabilities
Groups
Create Logical,
Management or Policy
groups (1 or more nodes)
 Node templates to describe components in the topology
structure
 Relationship templates to describe connections,
dependencies, deployment ordering

25. Application Tier
(container)
Application
Tier
(container)
Composition – different service templates can be
“wired” together
25
Logging/Monitoring Tier (ELK)
nodejs
WebServer
app_server
Compute
paypal_pizza
store
WebApplication
collectd
logstash
SoftwareComponent
Requirements
Container
Capabilities
log_endpoint
logstash_server
Compute
Capabilities
Container
elasticsearch
SoftwareComponent
Requirements
Container
Capabilities
search_endpoint
elasticsearch
_server
Compute
Capabilities
kibana
SoftwareComponent
Requirements
Container
kibana_server
Compute
Capabilities
search_endpoint
ConnectsTo
HostedOn HostedOn HostedOn
ConnectsTo
mongo_dbms
DBMS
mongo_server
Compute
mongo_db
Database
rsyslog
search_endpoint
ContainerContainer
ConnectsTo
Enabling the description of complex, multi-tier (hybrid) Cloud applications

26. Cloudify
26

27. 27
Introducing Cloudify
Open | Extensible | Simple
REPEATABLE

28. 28
Application
Blueprint
(TOSCA)
IaaS
Plugins
Container
Plugins
Conf Mgmt
Plugins
● Provision
● Configure
● Monitor
● Manage Monitoring &
Alarming

29. 29

30. 30
Cloudify Key Aspects
Open Source
Open source is key
to drive innovation
and create superb
quality software.
Open Standard
Open standard-based
TOSCA Spec for
application blueprints
allows vendor
neutrality, and
enables collaboration.
Future Proof
Try new emerging
technologies while
using stable in place
existing ones.

31. 31
The only constant is change”
-
Unknown

32. DICE Delivery Tools
32

33. Components of Delivery Tools
33
RESTful API
IaaS
Web GUI
Technology
Library

34. Delivery Service
34
Container 1
Blueprint
A
Platform
params
Container 2
Blueprint
B
Blueprint
B.2
Platform
params
Container 15
Blueprint
B.2

35. TOSCA technology library
o A plug-in for Cloudify
o A single import line in the TOSCA blueprint
o Node types + Chef cookbooks for Big Data services
o Unified across supported IaaS vendors
35©DICE

36. Deploy your own Big Data services
DevOps approach:
o Describe your Big Data
cluster and application in a
blueprint
o Store and maintain the
blueprint in your VCS with
the application’s code: IasC
o Rely on orchestrators and
configuration managers for
executing deployments
36
hours

37. Demo – DevOps
42

38. Conclusion
43

39. Conclusion
o DICE tools remove barriers to Big Data
o DICE technology library simplifies blueprints
o TOSCA blueprints describe infrastructure as code
o Enabled Continuous Integration and Continuous
Delivery
44

40. Links
o Cloudify: http://getcloudify.org
o DICE H2020: http://www.dice-h2020.eu/
o DICE deployment service:
https://github.com/dice-project/DICE-Deployment-Service
o Big Data blueprint examples:
https://github.com/dice-project/DICE-Deployment-Examples
o DICER:
https://github.com/dice-project/DICER
45

41. Follow us
o DICE project: @diceh2020
o Cloudify:
@CloudifySource – ilanadl@getcloudify.org
o User groups:
https://groups.google.com/forum/#!forum/cloudify-users
o Webinars:
http://getcloudify.org/webinars.html
o Matej Artač: @matej_artac – matej.artac@xlab.si
o XLAB Research: @xlab_research
46

42. Q & A
47