modaclouds decision support system for cloud service selection

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 © 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


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


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


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


MODAClouds DSS: Key features

§  Mul8ple Stakeholder par8cipa8on

§  Risk-‐analysis based Requirement genera8on

§  Mul8-‐Cloud Environment Compa8bility

§  Data gathering §  Progressive Learning


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

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Asset defini8on by mul8ple actors

Business Analyst

Assets

Product Innova8on and Quality

Legisla8on Compliance

Sales Rate Customer Loyalty

Market Awareness

Business-Oriented Intangible Assets

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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)


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

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Mul8-‐Cloud environment


Challenges in Mul8-‐Clouds

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• 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


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


Progressive Learning

Storage of User input

Storage of selec8on of services

Storage of thresholds

and benchmarks

Subsequent recommend-‐a8on on selec8on

Subsequent recommenda8on 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


Ground-‐up developed Prototype by CALabs


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


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?


Thank you for your amen8on!

Sr. Research Engineer [email protected]

Dr. Smra8 Gupta