MODAClouds Decision Support System for Cloud Service Selection
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The document discusses MODAClouds' Decision Support System (DSS) for cloud service selection. Some key points: - The DSS helps users select cloud services by considering multiple dimensions like cost, quality, risks, and technical/business constraints. - It allows multiple stakeholders like architects, operators, managers to provide input on tangible/intangible assets and risks. - The DSS performs risk analysis and generates requirements. It also considers issues around multi-cloud environments like interoperability and migration challenges. - Other features include automatic data gathering from various sources, and progressive learning over time from user inputs and service selections.
MODAClouds Decision Support System for Cloud Service Selection
- 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