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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	
  
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	
   ©	
  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	
   ©	
  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	
   ©	
  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	
   ©	
  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	
   ©	
  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	
   ©	
  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	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Mul8-­‐Cloud	
  environment	
  	
  
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	
   ©	
  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	
   ©	
  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	
   ©	
  2015	
  CA.	
  ALL	
  RIGHTS	
  RESERVED.	
  
Ground-­‐up	
  developed	
  Prototype	
  by	
  CALabs	
  
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	
   ©	
  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	
   ©	
  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

  • 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