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MODAClouds	
  Decision	
  Support	
  System	
  for	
  
Cloud	
  Service	
  Selec8on	
  
Smra8	
  Gupta	
  
	
  
CA	
  Labs...
2	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Outline	
  
Objec8ve	
  of	
  the	
  talk	
  
Need	
  for	
  Deci...
3	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Why	
  are	
  we	
  here?	
  
Decision	
  Support	
  System	
  an...
4	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Need	
  for	
  Decision	
  Support	
  System	
  in	
  cloud	
  se...
5	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
What	
  DSS	
  does	
  for	
  the	
  users?	
  
MODA	
  
Clouds	
...
6	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
MODAClouds	
  DSS:	
  Key	
  features	
  
§  Mul8ple	
  Stakehol...
7	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Mul8ple	
  actors,	
  mul8ple	
  perspec8ves	
  
§  Different	
  ...
8	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Asset	
  defini8on	
  by	
  mul8ple	
  actors	
  	
  
Business
Ana...
9	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Risk	
  analysis	
  methodology	
  
Business	
  Oriented	
  
Inta...
10	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Mul8-­‐Cloud	
  environment	
  	
  
11	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Challenges	
  in	
  Mul8-­‐Clouds	
  
11	
  
• Interoperability:...
12	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
DSS	
  –	
  Automa8c	
  Data	
  Gathering	
  Concept	
  
DSS	
  ...
13	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Progressive	
  Learning	
  
Storage	
  of	
  
User	
  input	
  
...
14	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Ground-­‐up	
  developed	
  Prototype	
  by	
  CALabs	
  
15	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Open	
  Source	
  Technology	
  Support	
  for	
  DSS	
  
•  hmp...
16	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Open	
  Discussion	
  
-­‐	
  What	
  are	
  the	
  characteris8...
17	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Thank	
  you	
  for	
  your	
  amen8on!	
  	
  
Sr.	
  Research	
  Engineer	
  
Smra8.Gupta@ca.com	
  
Dr.	
  Smra8	
  Gupta 	
  	
  
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MODAClouds Decision Support System for Cloud Service Selection

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

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MODAClouds Decision Support System for Cloud Service Selection

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 10   ©  2015  CA.  ALL  RIGHTS  RESERVED.   Mul8-­‐Cloud  environment    
  11. 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. 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. 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. 14   ©  2015  CA.  ALL  RIGHTS  RESERVED.   Ground-­‐up  developed  Prototype  by  CALabs  
  15. 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. 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. 17   ©  2015  CA.  ALL  RIGHTS  RESERVED.   Thank  you  for  your  amen8on!    
  18. 18. Sr.  Research  Engineer   Smra8.Gupta@ca.com   Dr.  Smra8  Gupta    

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