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Relational Cloud

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Academic presentation about the Relational Cloud system based on the paper "Relational Cloud: A Database-as-a-Service for the Cloud" by Carlo Curino et al.

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Relational Cloud

  1. 1. Rela%onal  Cloud  A  Database-­‐as-­‐a-­‐Service  for  the  Cloud   Paper  by  Carlo  Curino  et  al.  @mit.edu     Presenta%on  by  Antonio  Severien   severien@kth.se    
  2. 2. Overview  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø   Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   2  
  3. 3. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   3  
  4. 4. Rela%onal  Databases  Ø 1970  by  Edgar  Codd,  IBM  research  San  Jose  Ø Tables   Ø Rows  à  Tuples   Ø Columns  à  AEributes  Ø Rela%onal  Database  Management  Systems   (RDBMS)   4  
  5. 5. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   5  
  6. 6. Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Cloud  Ø Reduce  management,  opera%onal     and  energy  costs  Ø Elas%city  and  scale  economy   amazon  RDS  Ø Pay-­‐per-­‐use   6  
  7. 7. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   7  
  8. 8. Problems  AEacked     Efficient     Mul%-­‐tenancy  Elas%c  Scalability   Privacy   8  
  9. 9. Efficient    Mul%-­‐tenancy   9  
  10. 10. Efficient  Mul%-­‐tenancy  Ø Reduce  costs  Ø Efficient  usage  of  resources  Ø Maximize  hardware  u%liza%on  Ø Single  database  server  on  each  machine  Ø Maintain  applica%on  query  performance   10  
  11. 11. Efficient  Mul%-­‐tenancy  Ø Reduce  costs  Ø Efficient  usage  of  resources  Ø Maximize  hardware  u%liza%on  Ø Single  database  server  on  each  machine?  Ø Maintain  applica%on  query  performance     Virtual  Machine   11  
  12. 12. Efficient  Mul%-­‐tenancy  Ø Problems   Ø Monitoring  resource  requirements  for  workloads   Ø Predic%ng  the  load  generated   Ø Assigning  workloads  to  physical  machines   Ø Migra%ng  workloads  between  nodes   Ø Live  migra*on   12  
  13. 13. Efficient  Mul%-­‐tenancy  Ø Kairos  (Monitoring  and  consolida%on  engine)   Ø Resource  Monitor   Disk  ac%vity  and  RAM  requirements   Ø Combined  Load  Predictor   CPU,  RAM,  Disk  model  that  predicts  the  combined   resource  requirements   Ø Consolida%on  Engine   Non-­‐linear  op%miza%on  techniques  to…    …  minimize  the  number  of  machines  needed    …  balance  load  between  back-­‐end  machines   13  
  14. 14. Elas%c  Scalability   14  
  15. 15. Elas%c  Scalability  Ø Workload  exceeds  single  machine  capacity     Ø Scale  a  single  database  to  mul%ple  nodes   Ø Scale-­‐out  by  query  processing  par%%oning   Ø Granular  placement  and  load  balance  on  backend   15  
  16. 16. Elas%c  Scalability  Ø Strategy  well  suited  for  OLTP  and  Web   workloads…  but  can  extend  to  OLAP  Ø Minimize  cross-­‐node  distributed  transac%ons    Ø Workload-­‐aware  par**oner   Ø Par%%on  data  to  minimize  mul%-­‐node  transac%ons   Ø Front-­‐end  analyses  execu%on  traces  represented   as  a  graph   16  
  17. 17. Graph  Par%%oning   we=2 id   name   age   salary   id   name   age   salary   we=1 we=10 id   name   age   salary  we :  weight  of  edge 17  
  18. 18. Graph  Par%%oning   we=2 id   name   age   salary   id   name   age   salary   we=1 we=10 id   name   age   salary  we :  weight  of  edge 18  
  19. 19. Graph  Par%%oning   id   name   age   salary  id   name   age   salary   id   name   age   salary   19  
  20. 20. Privacy   20  
  21. 21. Privacy  Ø Adjustable  security   Ø Onion  ring  encryp%on  design   2  onion  layer  and  1  homomorphic  encryp%on  of  integer   Ø SQL  query  on  encrypted  data   Ø Security  level  dynamically  adap%ve   Converge  to  an  overall  security  level   21  
  22. 22. Onion  Layers  of  Encryp%on  6.  RND:  no  func%onality   5.  RND:  no  func%onality   HOM:  addi%on   int  value  4.  DET:  equality  selec%on   3.  OPE:  inequality  select,   min,  max,  sort,  group-­‐by   2.  DET:  equality  join   or   1.  OPE:  inequality  join   String  search   Value   Value   string  value   Strong   RND  =  Randomized  Encryp%on  (no  opera%ons  allowed)   DET  =  Determinis%c  Encryp%on     OPE  =  Order-­‐preserving  Encryp%on   HOM  =  Homomorphic  Encryp%on  (opera%ons  over  encrypted  data)   Weak   22  
  23. 23. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   23  
  24. 24. Rela%onal  Cloud  Architecture   Client  Nodes   Users   User  Applica%on   JDBC-­‐client  (CryptoDB  enabled)   Trusted  Pla,orm  (Private/Secured)   Privacy-­‐preserving   Privacy-­‐preserving   Untrusted  Pla,orm  (Public)   Queries   Results   Admin  Nodes   Frontend  Nodes   Router   Distributed  Transac%onal  Coordina%on   Par%%oning  Engine   Placement  and   Migra%on  Engine   Backend  Nodes   Backend  Nodes   Database     CryptoDB   CryptoDB  Par**ons   load  stats   Encryp%on  Engine   Encryp%on  Engine  Placement   24  
  25. 25. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   25  
  26. 26. Experiments   26  
  27. 27. Experiments   Bad  results?  Tradeoff  for  be=er  privacy   27  
  28. 28. Experiments   Scaling  TPC-­‐C   28  
  29. 29. Rela%onal  Cloud  Ø Rela%onal  Databases  Ø Database-­‐as-­‐a-­‐Service  (DBaaS)  Ø Problems  AEacked   Ø Efficient  Mul%-­‐tenancy   Ø Elas%c  Scalability   Ø Database  Privacy  Ø Rela%onal  Cloud  Ø Experiments  Ø Conclusion   29  
  30. 30. Conclusion  Ø Presented  Rela%onal  Cloud  Ø Efficient  Mul%-­‐tenancy   Ø Novel  resource  es%ma%on   Ø Non-­‐linear  op%miza%on-­‐based  consolida%on  technique  Ø Scalability   Ø Graph-­‐based  par%%oning  Ø Privacy     Ø Adjustable  privacy   Ø SQL  queries  on  encrypted  data  Ø DBaaS  is  a  viable  cloud  service     30  
  31. 31. References  Ø  "Rela%onal  Cloud:  a  Database  Service  for  the  cloud"  Carlo   Curino,  Evan  Jones,  Raluca  Popa,  Nirmesh  Malviya,  Eugene   Wu,  Sam  Madden,  Har  Balakrishnan,  Nickolai  Zeldovich  Ø  hEp://rela%onalcloud.com     32  
  32. 32. Privacy  CryptoDB  Example   DET-­‐encrypted     Return  to  JDBC  client  decrypted     cyphertext   RND  cyphertexts   SELECT i_price, ... FROM item WHERE i_id = N JDBC  client  decrypts     DET  level  4   33  

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