The presentation provided an overview of Cloud-TM goals and progress, including developing a simple programming model for distributed transactions and minimizing administration costs through self-optimizing data grids. Innovative transactional replication schemes and data-grid self-optimization techniques were discussed to further these goals. Overall, the talk focused on the Cloud-TM platform's architecture, achievements to date, and future work.

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2. About me
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3. Talk overview
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4. Cloud-TM at a glance
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5. Key Goals
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6. d#:X'.<()!.(!%+&!BN.<)CK"!*'.'#//3(!*#'#)3/e]!
TRANSACTIONAL MEMORIES
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7. Transactional Memories!
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8. !to Distributed Transactional Memories...
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9. !to Cloud-TM
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10. PROGRESSES SO FAR
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11. Main achievements
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12. Architecture Specification
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13. Preliminary prototype
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14. Innovative transactional
replication schemes
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15. Data-grid self-optimization
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16. Talk overview
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Cloudviews 2011, Porto, Portugal, Nov. 4 2011

17. Methodologies explored so far
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18. Analytical modeling
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19. Machine learning
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20. Hybrid techniques
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21. Case studies
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22. SELF-OPTIMIZING DATA
CONSISTENCY PROTOCOLS
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23. The search for the holy grail
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24. The Cloud-TM vision
• low #resources: • auto-scale up: • multi-master: • auto-scale down:
- minimum costs - new nodes hired for - minimum costs
read-only requests - hi % write
• primary-backup: primary overwhelmed • switch back to
• primary-backup:
- low % write: • higher scalability primary-backup
- low % write:
low load on primary
primary stands the load
: node processing read-only requests
: node processing read&write requests
• low traffic • hi traffic • hi traffic • low traffic
• read-dominated • read-dominated • write dominated • read dominated
• low conflict • low conflict • low conflict • low conflict
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25. Self-optimizing data replication:
key challenges
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26. Case studies
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27. !
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POLYCERT: POLYMORPHIC SELF-OPTIMIZING
REPLICATION FOR IN-MEMORY
TRANSACTIONAL GRIDS
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28. Where they fit in the picture
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29. Certification
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30. Certification
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31. Classic Replication Protocols
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BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

32. Non-voting
K^d!.-!KMh$! K^d!.-!KFh$!
'&#)!`!P'3%&$&%! '&#)!`!P'3%&$&%!
A[&:<J.(!
K'#($#:J.(!KM!
9M!
A[&:<J.(! n#N3)#J.(`B.//3%! n#N3)#J.(`V;.'%!
K'#($#:J.(!KF! KM! KF!
9F!
n#N3)#J.(`B.//3%! n#N3)#J.(`V;.'%!
KM! KF!
9W!
+ only validation executed at all replicas:
high scalability with write intensive workloads
- need to send also readset: often very large!
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

33. Classic Replication Protocols
• R.:<$!.(!-<NN!'&2N3:#J.(!2'.%.:.N$!
13(N&!/#$%&'!! "<NJ!/#$%&'!!
E2'3/#'8C;#:X<2I!
VdC;#$&)! F*BC;#$&)!
B&'Jf:#J.(! 1%#%&!/#:+3(&!'&2N3:#J.(!
@.(C7.J(! n.J(!
dRB!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

34. Voting
!KMh$!K^d! !KMh$!;'.#):#$%!
E&2I! !E!3*.01I!
!
A[&:<J.(! KMh$!!
K'#($#:J.(!KM! 7#N3)#J.(!
9M! !
P#3%!-.'!
9F! 9Mh$!7.%&!
+ sends only write-set (much smaller than read-sets normally)
- Additional communication phase to disseminate decision (vote)
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

35. Classic Replication Protocols
• R.:<$!.(!-<NN!'&2N3:#J.(!2'.%.:.N$!
13(N&!/#$%&'!! "<NJ!/#$%&'!!
E2'3/#'8C;#:X<2I!
VdC;#$&)! F*BC;#$&)!
B&'Jf:#J.(! 1%#%&!/#:+3(&!'&2N3:#J.(!
@.(C7.J(! n.J(!
dRB!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

36. Bloom Filter Certification (BFC)
• 6$&!#!$3(N&!K^d!#$!3(!(.(C7.J(>!;<%!&(:.)&!'&#)$&%!
3(!#!dN../!fN%&'!
– dN../!fN%&'$Q!
• $2#:&C&c:3&(%>!2'.;#;3N3$J:!)#%#!$%'<:%<'&!-.'!%&$%!/&/;&'$+32!
• :./2'&$$3.(!3$!#!-<(:J.(!.-!#!E%<(#;N&I!-#N$&!2.$3J7&!'#%&!
• V!%'#($#:J.(!K!3$!:&'Jf&)!$<::&$$-<NN8!.(N8!3-Q!
– (.(&!.-!%+&!3%&/$!P'3o&(!;8!:.(:<''&(%!%'#($#:J.($!3$!
2'&$&(%!3(!%+&!dR!<$&)!%.!&(:.)&!Kh$!'&#)$&)!
– $%'.(N8!'&)<:&!(&%P.'X!%'#c:!#%!%+&!:.$%!.-!(&N33;N&!
#;.'%!3(:'&#$&!
• Mt!-#N$&!2.$3J7&!83&N)$!<2!%.!WG[!:./2'&$$3.(!!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

37. BFC vs Voting vs Non-Voting
("
!#'"
!"#$%&'()*+,-#"./-0.1+
!#&"
*+,"
!#%" -.,"
+,"
!#$"
!"
(" "()!!!"" "(!!)!!!""
2)%*+3)1+3'()+
Fig. 2. Throughput of three certification
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM! strategies with different read-set sizes.

38. Self-optimizing data replication:
key challenges
M] #NN.P!&c:3&(%!$P3%:+!#/.(!/<NJ2N&!'&2N3:#J.(!
2'.%.:.N$Q!
– 3&"I*2/",3"!.-!/<NJ2N&!:&'Jf:#J.(!$:+&/&$!73#!%+&!
*.N8/.'+3:!B&'Jf:#J.(!E*.N8B&'%I!2'.%.:.N!
F] )&%&'/3(&!%+&!.2J/#N!'&2N3:#J.(!$%'#%&8!
37&(!%+&!:<''&(%!P.'XN.#)!:+#'#:%&'3$J:$Q!
– &(J'&N8!;#$&)!.(!)13@*,".#"10,*,-.%&:+(3_<&$!
• .lCN3(&Q!)&:3$3.(C%'&&$>!(&<'#N!(&%P.'X>!1n"!
• .(CN3(&Q!'&3(-.':&/&(%!N&#'(3(!E6BdI!
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

39. PolyCert
• *.N8/.'2+3:!1&N-C^2J/343(!B&'Jf:#J.(!!
• B.C&[3$%&(:&!.-!%+&!W!:&'Jf:#J.(!$:+&/&$Q!
– &[2N.3%!:.//.(!'&N3#(:&!.(!%.%#N!.')&'!;'.#):#$%!
• "#:+3(&CN&#'(3(!%&:+(3_<&$!%.!)&%&'/3(&!
%+&!.2J/#N!:&'Jf:#J.(!$%'#%&8!2&'!
%'#($#:J.(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

40. Replication Protocol
Selector Oracle
• KP.!3/2N&/&(%#J.($Q!
– ^lCN3(&!"#:+3(&!D&#'(3(!K&:+(3_<&$!
– ^(CN3(&!9&3(-.':&/&(%!D&#'(3(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

41. Off-line Machine Learning
Techniques
• R.'!&#:+!%'#($#:J.(Q!
– ,&%&'/3(&!$34&!.-!&[:+#(&)!/&$$#&$!-.'!&#:+!
:&'Jf:#J.(!$:+&/&!
– R.'&:#$%!Vd!N#%&(:8!-.'!&#:+!/&$$#&!$34&]!u&!
&7#N<#%&)!$&7&'#N!"D!#22'.#:+&$Q!
• 9&'&$$3.(!)&:3$3.(!%'&&$!E;&$%!'&$<N%$I!!
• @&<'#N!(&%P.'X$!!
• 1<22.'%!7&:%.'!/#:+3(&!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

42. Off-line Machine Learning
Techniques
• 6$&$!<2!%.!YW!/.(3%.'&)!$8$%&/!#o'3;<%&$Q!!
– B*6!
– "&/.'8!!
– @&%P.'X!!
– K3/&C$&'3&$!
• 9&_<3'&$!:./2<%#J.(#N!3(%&($37&!-&#%<'&!
$&N&:J.(!#()!%'#3(3(!2+#$&!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

43. On-line Reinforcement Learning
• A#:+!'&2N3:#!;<3N)$!.(CN3(&!&[2&:%#J.($!.(!%+&!
'&P#')$!.-!&#:+!2'.%.:.NQ!
– (.!#$$</2J.(!.(!'&P#')$h!)3$%'3;<J.($!
!
• 622&'!B.(f)&(:&!d.<()!E6BdI!#N.'3%+/Q!!
– N3+%P&3+%!#()!2'.7#;N8!.2J/#N!$.N<J.(!%.!%+&!
&[2N.'#J.(C&[2N.3%#J.(!)3N&//#Q!
• )3)!?!%&$%!%+3$!.2J.(!$<c:3&(%N8!3(!%+3$!$:&(#'3.v!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

44. On-line Reinforcement Learning
• ,3$J(<3$+&$!P.'XN.#)!$:&(#'3.!$.N&N8!;#$&)!
.(!'&#)C$&%h$!$34&!
– &[2.(&(J#N!)3$:'&J4#J.(!3(%&'7#N$!%.!/3(3/34&!
%'#3(3(!J/&!
• 9&2N3:#$!&[:+#(&!$%#J$J:#N!3(-.'/#J.(!
2&'3.)3:#NN8!%.!;..$%!N&#'(3(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

45. Chasing the optimum!
("
!#'"
!"#$%&'()*+,-#"./-0.1+
!#&" *+,"
-.,"
!#%" +,"
/0123,-"
!#$"
/4"
!"
(" "()!!!"" "(!!)!!!""
2)%*+3)1+3'()+
Fig. 5. Normalized throughput of the adaptive and non-adaptive protocols (Bank benchmark).
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!
transactions, thus allowing us to focus on the performance of the transactions that re-

46. !and beating it!
(#%"
(#$"
!"#$%&'()*+,-#"./-0.1+
("
)*"
!#'" +,"
!#&" -*."
+/01-*."
!#%"
!#$"
!"
Fig. 8. Normalized throughput of the adaptive and VC protocols. NVC and BFC not reported as
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!
they caused the collapse of the GCS layer. (STMBench7 benchmark)

47. Case studies
• ,8(#/3:!$&N&:J.(!#()!$P3%:+3(!;&%P&&(!
'&2N3:#J.(!2'.%.:.N$Q!
– %.%#N!.')&'!;#$&)!'&2N3:#J.(!2'.%.:.N$!EB#$&!$%<)8!MIQ!
• 2<'&N8!;#$&)!.(!"#:+3(&!D&#'(3(!%&:+(3_<&$!
– $3(N&C/#$%&'!7$!/<NJC/#$%&'!EB#$&!$%<)8!FIQ!
• +8;'3)!"DCV"!$.N<J.(!q!)373)&C#()C:.(_<&'!
• ='.<2!B.//<(3:#J.(!18$%&/!$&N-C.2J/34#J.(Q!
– ;#%:+3(!3(!%.%#N!.')&'!2'.%.:.N$!EB#$&!$%<)8!WI!
• +8;'3)!"DCV"!q!"D!;..%$%'#22&)!P3%+!V"!X(.PN&)&!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

48. ,3&.!,3).(#>!1&;#$J#(.!*&N<$.>!*#.N.!9./#(.!#()!R'#(:&$:.!b<#N3#>!!
1&N-C%<(3(!'&2N3:#J.(!.-!&N#$J:!3(C/&/.'8!%'#($#:J.(#N!)#%#!2N#a.'/$>!
!?@A1BC?,!K&:]!9&2]!FYgFGMM>!!"#8!FGMM]!
SINGLE VS MULTI-MASTER
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

49. Classic Replication Protocols
• R.:<$!.(!-<NN!'&2N3:#J.(!2'.%.:.N$!
13(N&!/#$%&'!! "<NJ!/#$%&'!!
E2'3/#'8C;#:X<2I!
K.%#N!.')&'!;#$&)! F*BC;#$&)!
B&'Jf:#J.(! 1%#%&!/#:+3(&!'&2N3:#J.(!
@.(C7.J(! n.J(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

50. Single Master
• u'3%&!%'#($#:J.($!#'&!&[&:<%&)!&(J'&N8!3(!#!$3(N&!
'&2N3:#!E%+&!2'3/#'8I!
• ?-!#!P'3%&!%'#($#:J.(!3$!'&#)8!%.!:.//3%>!:..')3(#J.(!
3$!'&_<3'&)!%.!<2)#%&!#NN!%+&!.%+&'!'&2N3:#$!E;#:X<2$I]!
• 9&#)!%'#($#:J.($!:#(!;&!&[&:<%&)!.(!;#:X<2!'&2N3:#$]!
• ;&.4*2/0*G5/"4.4"14#&3J2.
• ;&.4*2/0*G5/"4.3&&04*,1(&,.450*,-.3&))*/.
• K@0&5-@'5/.&$.H0*/"./I2.4&"2,L/.231#".5'.H*/@.,5)G"0.&$.,&4"2.
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

51. Classic Replication Protocols
• R.:<$!.(!-<NN!'&2N3:#J.(!2'.%.:.N$!
13(N&!/#$%&'!! "<NJ!/#$%&'!!
E2'3/#'8C;#:X<2I!
K.%#N!.')&'!;#$&)! F*BC;#$&)!
B&'Jf:#J.(! 1%#%&!/#:+3(&!'&2N3:#J.(!
@.(C7.J(! n.J(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

52. 2PC-based replication
• Transactions executed at all nodes w/o coordination till
commit time
• Acquire atomically locks at all nodes using two phase
commit (2PC):
• 2PC materializes conflicts among concurrent remote transactions
generating:
DISTRIBUTED DEADLOCKS
+ good scalability at low conflict
- thrashes at high conflict
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

53. Goals
• V<%.(./3:#NN8!$&N&:%!%+&!;&$%!$<3%&)!2'.%.:.N!
%+#%!
– "3(3/34&$!%'#($#:J.($w!$&'73:&!J/&!
– "#[3/34&$!#:+3&7#;N&!%+'.<+2<%!
• V<%./#%&!&N#$J:!$:#N3(!
– 1:#N&!<2!3-!%+&!$8$%&/!(&&)$!/.'&!:./2<%#J.(#N!
2.P&'!
– 1:#N&!).P(!3-!%+&!$8$%&/!3$!.7&'$34&)!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

54. Self-optimizing data replication:
key challenges
M] #NN.P!&c:3&(%!$P3%:+!#/.(!/<NJ2N&!'&2N3:#J.(!
2'.%.:.N$Q!
– +&'&!:.&[3$%&(:&!.-!%+&!F!$:+&/&$!3$!3/2.$$3;N&Q!
• )&$3(!.-!#(!(.(C;N.:X3(!2'.%.:.N!$P3%:+3(!$%'#%&8!
!
F] )&%&'/3(&!%+&!.2J/#N!'&2N3:#J.(!$%'#%&8!
37&(!%+&!:<''&(%!P.'XN.#)!:+#'#:%&'3$J:$Q!
– #(#N8J:#N!/.)&N$!.-!&l&:%$!.-!)#%#!:.(%&(J.(!
– /#:+3(&!N&#'(3(!/&%+.)$!%.!2'&)3:%!+PC)&2&()#(%!
N#%&(:3&$!
• B*6!&[&:<J.(!J/&>!(&%P.'X!9KK!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

55. Key Technical Problem
• x.P!%.!-.'&:#$%Q!
– *&'-.'/#(:&$!.-!2'.%.:.N!d!P+3N&!'<((3(!
2'.%.:.N!Vv!
– *&'-.'/#(:&$!P3%+!r!(.)&$!P+3N&!'<((3(!.(!j!
(.)&$v!
37&(!%+#%!'&2N3:#J.(!2'.%.:.Ng$:#N&!:+#(&$!
#l&:%Q!
– K+&!%'#($#:J.(!:.(s3:%!2'.;#;3N3%8!!
– K+&!%'#($2.'%!N#8&'!N#%&(:8!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

56. Methodology
T.3(%!<$#&!.-!#(#N8J:#N!/.)&N3(!#()!/#:+3(&!
N&#'(3(!%&:+(3_<&$Q!
– #(#N8J:#N!/.)&N!.-!'&2N3:#J.(!#N.'3%+/!
)8(#/3:$Q!
• N.:X!:.(%&(J.(>!)3$%'3;<%&)!)&#)N.:X!2'.;#;3N3%8!
• /&$$#&!&[:+#(&!2#o&'(!
– /#:+3(&!N&#'(3(!%.!-.'&:#$%!2&'-.'/#(:&!.-!
'.<2!:.//<(3:#J.(!N#8&'Q!
• 9KK!#$!#!-<(:J.(!.-!/$!$34&>!%+'.<+2<%>!y(.)&$!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

57. Analytical Model
• ,3$%'3;<%&)!N.:X!:.(%&(J.(!)8(#/3:$!:#2%<'&)!73#!#(!
#(#N8J:#N!/.)&NQ!
– %+&!'&2N3:#J.(!#N.'3%+/$w!;&+#73.'!3$!-<NN8!$2&:3f&)!
– 3%!3$!2.$$3;N&!%.!/#%+&/#J:#NN8!/.)&N!%+&/!
!!!!!!4'''5).!*+6!17*.10528)9:1;+.1.!5.<2157*.!0=12.*=9>1?1
• z&8!/&%+.).N.3&$Q!
– "&#(!7#N<&!#(#N8$3$!`!b<&<3(!%+&.'8!
• 9&N8!.(!"#:+3(&!D&#'(3(!%.!-.'&:#$%!+#')P#'&!
)&2&()&(%!/&%'3:$>!3(!2#'J:<N#'!(&%P.'X!N#%&(:3&$e!
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

58. Machine learning techniques
• 9&$.<':&!73'%<#N34#J.(!/#X&$!/#%+&/#J:#N!
/.)&N3(!<(-&#$3;N&Q!
– @.!X(.PN&)&!.-!#:%<#N!N.#)!!
– @.!X(.PN&)&!.-!#:%<#N!2+8$3:#N!'&$.<':&$!
!
• K'#($2.'%!N#8&'!N#%&(:8!E9KKI!.-!%+&!%P.!
2'.%.:.N$!2'&)3:%&)!73#!)&:3$3.(!%'&&!
'&'&$$.'$!
!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

59. Inputs for the ML
• @</;&'!.-!(.)&$!
• 9KK!3(!%+&!:<''&(%!:.(f<'#J.(!
• 134&!.-!&[:+#(&)!/&$$#&$!
• K+'.<+2<%!.-!%+&!:<''&(%!:.(f<'#J.(!
– M,J,&H,NNN.
– =<&$$&)!<$3(!%+&!#(#N8J:#N!/.)&N!E/.'&!(&[%I!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

60. Statistical Model Accuracy
• B.''&N#J.(!;&%P&&(!G]{Z!#()!G]{H!
• 9&N#J7&!&''.'!;&%P&&(!G]M{!#()!G]FF!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

61. Models Coupling
E,1#D(31#.)&4"#!-.'&:#$%$!%+&!41/1.-0*4.
/@0&5-@'5/!%#X3(!#$!3(2<%!%+&!8KK!3(!%+&!%#'&%!
:.(f<'#J.(]!
.
O13@*,".#"10,*,-.-.'&:#$%$!%+&!8KK!%#X3(!#$!3(2<%!
%+&!41/1.-0*4./@0&5-@'5/!3(!%+&!%#'&%!
:.(f<'#J.(!
!!!R3[&)!2.3(%!$.N<J.(!-.<()!<$3(!'&:<'$3.(!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

62. Global Model Accuracy
20000
2PC-est-low conflict
2PC-low conflict
2PC-est-high conflict
2PC-high conflict
PB-est-low conflict
PB-low conflict
15000
Throughput (Tx/sec)
10000
5000
0
4 5 6 7 8 9 10
# Nodes
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

63. !and now in action!
7000
’th.txt’ u 1:2
6500
6000
5500
throughput (tx/sec)
5000
4500
4000
3500
3000
PQR. T:UT. PQR.
2500
=Q;SP:=K. =Q;SP:=K. =Q;SP:=K.
2000
0 100 200 300 400 500 600 700 800 900
time (sec)
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

64. Case studies
• ,8(#/3:!$&N&:J.(!#()!$P3%:+3(!;&%P&&(!
'&2N3:#J.(!2'.%.:.N$Q!
– %.%#N!.')&'!;#$&)!'&2N3:#J.(!2'.%.:.N$!EB#$&!$%<)8!MIQ!
• 2<'&N8!;#$&)!.(!"#:+3(&!D&#'(3(!%&:+(3_<&$!
– $3(N&C/#$%&'!7$!/<NJC/#$%&'!EB#$&!$%<)8!FIQ!
• +8;'3)!"DCV"!$.N<J.(!q!)373)&C#()C:.(_<&'!
• ='.<2!B.//<(3:#J.(!18$%&/!$&N-C.2J/34#J.(Q!
– ;#%:+3(!3(!%.%#N!.')&'!2'.%.:.N$!EB#$&!$%<)8!WI!
• +8;'3)!"DCV"!q!"D!;..%$%'#22&)!P3%+!V"!X(.PN&)&!
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

65. *#.N.!9./#(.!#()!"#o&.!D&.(&|!
@0)A-.+78761B5.(!8761871C*.5)1D=,0=1B=*5,(52.1E=*.*(*)213851F75)9G(5)1
H*,0))876157,1I087A*=(0/07.1J05=78761
?AAA!?(%&'(#J.(#N!B.(-&'&(:&!.(!B./2<J(>!@&%P.'X3(!#()!
B.//<(3:#J.($!E?B@BwMFI>!T#(]!FGMF!
!
BATCHING IN TOTAL ORDER
BROADCAST PROTOCOLS
BN.<)73&P$!FGMM>!*.'%.>!*.'%<#N>!@.7]!U!FGMM!

66. Sequencer based TOB (STOB)
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67. Batching in STOB protocols
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68. Analytical model
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69. 60
Optimal B
Avg Msg. Arriv
4000
40
20 Model Accuracy 2000
0
0 2000 4000 6000 8000 10000 12000 14000 0 2
Average Msg. Arrival Rate (msgs/sec)
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Average Msg. Arrival Rate (msgs/sec)
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Figure 3. Validating the accuracy of the analytical model. 0
16 1

70. 2000
A 0
0 2 4 6 8 10 12 14 16 18 20 22 24
Peak period analysis
Hour of the day (3 Sept. 2010)
Figure 4. Traffic at the FenixEDU system (Sept. 3 2010)
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71. What about a pure ML approach?
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72. Combining the two approaches
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73. Combining the two approaches
lytical Model
ry ex- 100
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ired to
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the reward of each possible decision, and lowers it as more

74. Talk structure
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75. Open research issues
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76. Future work
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77. THANKS FOR THE ATTENTION
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78. References (I)
iB=MGk!*]!,3!1#(4.!d]!B3:3#(3!R]!b#N3#!9]!*#N/3'3!#()!*#.N.!9./#(.!V(#N8J:#N!.)NN3(!.-!B.//3%CK3/CD.:X3(!VN.'3%+/$!-.'!
1.mP#'!K'#($#:J.(#N!/.'3$!*'.:]!WY%+!?(%'(#J.(#N!B./2%'!#$'/(%!='.2!B.(-'(:!EB=I!^'N#().!RN.'3)#!B./2%'!
#$'/(%!='.2!,:/;'!FGMG!
!
i*AnVMMk!*]!,3!1#(4.!d]!B3:3#(3!R]!b#N3#!9]!*#N/3'3!#()!*#.N.!9./#(.!^(!%+!V(#N8J:#N!.)N3(!.-!B.(:''(:8!B.(%'.N!VN.'3%+/$!-.'!
1.mP#'!K'#($#:J.(#N!/.'3$Q!%+!B#$!.-!B.//3%CK3/CD.:X3(!AN$73'!*'-.'/#(:!A7#N#J.(!T.'(#N!%.!#22#'!
!
i3))NP#'FGMGk!@]!B#'7#N+.!*#.N.!9./#(.!#()!D]!9.)'3$!V$8(:+'.(.$!D#$C;#$)!92N3:#J.(!.-!1.mP#'!K'#($#:J.(#N!/.'8!
*'.:)3($!.-!%+!VBg?R?*g61A@?r!MM%+!3))NP#'!B.(-'(:!E3))NP#'I!d#(#N.'!?()3#!VB!*'$$!@.7/;'!FGMG!
!
i3))NP#'FGMMk!*.N8B'%Q!*.N8/.'2+3:!1N-C^2J/343(!92N3:#J.(!-.'!?(C/.'8!K'#($#:J.(#N!='3)$!]!B.:3'.!*]!9./#(.!#()!D]!
9.)'3$!VBg?R?*g61A@?r!MF%+!?(%'(#J.(#N!3))NP#'!B.(-'(:!E3))NP#'!FGMMI!!
!
i1*VVFGMGk!*#.N.!9./#(.!9]!*#N/3'3!R]!b#N3#!@]!B#'7#N+.!#()!D]!9.)'3$!^(!12:N#J7!92N3:#J.(!.-!K'#($#:J.(#N!18$%/$!Ed'3-!
V((.(:/(%I!*'.:]!FF()!VB!18/2.$3/!.(!*#'#NNN3$/!3(!VN.'3%+/$!#()!V':+3%:%'$!E1*VVI!1#(%.'3(3!=':!VB!*'$$!T(!FGMG!
!
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*'.:]!H%+!?AAA!?(%'(#J.(#N!18/2.$3/!.(!*#'#NNN!#()!,3$%'3;%)!*'.:$$3(!P3%+!V22N3:#J.($!E?1*VI!K#3P#(!K#323!?AAA!B./2%'!1.:3%8!
*'$$!12%/;'!FGMG!
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*'.:]!{%+!?AAA!?(%'(#J.(#N!18/2.$3/!.(!@%P.'X!B./2J(!#()!V22N3:#J.($!E@BVI!B#/;'3)!#$$#:+$$%$!61V!?AAA!B./2%'!
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79. References (II)
i1j1K^9FGMMk!@]!B#'7#N+.!*#.N.!9./#(.!#()!D]!9.)'3$!1B'%Q!12:N#J7!B'Jf:#J.(!3(!92N3:#%)!1.mP#'!K'#($#:J.(#N!/.'3$!
*'.:)3($!.-!%+!U%+!V((#N!?(%'(#J.(#N!18$%/$!#()!1%.'#!B.(-'(:!E1j1K^9!FGMMI!x#3-#!?$'#N!T(!FGMM]!
!
i19,1FGMMk!9]!*#N/3'3!R]!b#N3#!#()!*#.N.!9./#(.!^1V9AQ!^22.'%(3$J:!12:N#J.(!3(!V:J7N8!9A2N3:#%)!K'#($#:J.(#N!18$%/$!E1+.'%!
*#2'I!K+!WG%+!?AAA!18/2.$3/!.(!9N3#;N!,3$%'3;%)!18$%/$!E19,1!FGMMI!#)'3)!12#3(!^:%]!FGMM!
!
i1V1^MGk!]!B.:3'.!*#.N.!9./#(.!#()!D]!9.)'3$!V!#:+3(!D#'(3(!V22'.#:+!%.!*'-.'/#(:!*')3:J.(!.-!K.%#N!^')'!d'.#):#$%!
*'.%.:.N$!!*'.:]!U%+!?AAA!?(%'(#J.(#N!B.(-'(:!.(!1N-CV)#2J7!#()!1N-C^'#(343(!18$%/$!E1V1^I!d)#2$%!x(#'8!?AAA!B./2%'!
1.:3%8!*'$$!12%/;'!FGMG!
!
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#()!93(-.':/(%!D#'(3(!VB!*'-.'/#(:!A7#N#J.(!973P!%.!#22#'!E#N$.!2'$(%)!#%!?R?*!*'-.'/#(:!FGMM!18/2.$3/I!
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D#'(3(!?AAA!?(%'(#J.(#N!B.(-'(:!.(!B./2J(!@%P.'X3(!#()!B.//(3:#J.($!@%P.'X!VN.'3%+/!`!*'-.'/#(:!A7#N#J.(!
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