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.
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
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. 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. 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. 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
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. 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. 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. 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. Graph
Par%%oning
id
name
age
salary
id
name
age
salary
id
name
age
salary
19
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.
32. 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
33. 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
Notas do Editor
Talk about the importance of relational databases and their legacy
Talk about the market and the viability of relational databases as a service in the cloud
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Challenge: workload exceeds capacity of single machine
- THE WAY TO SCALE THE WORKLOADS is to MINIMIZE # of MULTI-NODE TRANSACTIONS… why? OVERHEAD ON HOLDING LOCKS on the BACKEND
Detail how the provacy works and follow to exemplify on the next sliideKnow well homomrphismUses symetric encryption
Comparison between consolidated DBs in one machine versus DBs on Virtual Machines.Explained the difference between UNIFORM and SKEWED: uniform load and skewed (50% of the requests goes to one of the 20 DBs)Consolidated 20 databases to one physical machine
Explain what is TPC-C (benchmarks for databases…. Etc)