Relational Cloud: A Database-as-a-Service for the Cloud Carlo Curino, Evan Jones, Raluca Ada Popa, Nirmesh Malaviya, Eugene Wu, Sam Madden, Hari Balakrishnan, Nickolai Zeldovich Presented by Arka Bhattacharya (for CS 294,Berkeley) (some slides are taken from the CIDR ‘11 talk)
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Relational Cloud: A Database-as-a-Service for the Cloud
Relational Cloud: A Database-as-a-Service for the Cloud. Carlo Curino , Evan Jones, Raluca Ada Popa , Nirmesh Malaviya , Eugene Wu, Sam Madden, Hari Balakrishnan , Nickolai Zeldovich. Presented by Arka Bhattacharya (for CS 294,Berkeley) (some slides are taken from the CIDR ‘11 talk). - PowerPoint PPT Presentation
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Relational Cloud: A Database-as-a-Service for the Cloud
Carlo Curino, Evan Jones, Raluca Ada Popa, Nirmesh Malaviya, Eugene Wu, Sam Madden, Hari Balakrishnan,
Key insight : Single database server per machine + logical databases ; (as opposed to DB in VM , or multiple DB servers per machine ) Reduces redundant work, group commits, lower RAM
wastage, code sharing, cheaper context switches
Kairos ….cntd Measure RAM,CPU & Disk usage of a database, and estimate
combined load RAM : Probe table to gauge working set size ; additive Disk : Deduce model by testing DBMS with different write rates
& working set size & measuring amount of IO CPU : additive
Frame optimization problem (non-linear programming) Solving takes time After lots of heuristics, optimization solutions terminate in 8
minutes for 20 servers & 100 workloads !
2. Elastic ScalabilityDatabase Partitioning
Problem : Partition an OLTP database into N chunks so as to maximize performance
Solution : Schism , VLDB 2010 Close to optimal
Key insight : Minimize number of distributed transactions Advantage over Hashing, round-robin
Use workload trace to find good partitions
Schism …cntd
Schism …. cntd Use a classifier to capture partitioning in compact
form , for efficient query routing
Lots of heuristics to choose good workload sample Sampling , blanket state filtering, etc
Graph Partitioning in fast ( < 40 sec )
Achieves almost linear scalability !
3. Privacy Problem :
Prevent DBA from snooping on data ensure data security during application and DBMS
Unmodified DB backends Workload-aware consolidation Workload-aware sharding High availability via replication of front-end servers SQL over encrypted data