1. November, 19th GDS meeting, LIP6, Paris 1 Hierarchical Synchronization and Consistency in GDS Sébastien Monnet IRISA, Rennes

2. November, 19thGDS meeting, LIP6, Paris2 JuxMem Consistency Protocol Currently Home Based  Home node responsible of a piece of data  Actions on the piece of data communication with the home node Home node Client Home

3. November, 19thGDS meeting, LIP6, Paris3 Replicated Home  The home node is replicated to tolerate failures  Thanks to active replications all replicas are up-to-date

4. November, 19thGDS meeting, LIP6, Paris4 Replication  Two layered architecture  Replication based on classical fault tolerant distributed algorithms  Implies a consensus between all nodes  Need for replicates in several clusters (locality) CommunicationsFailure detector Consensus Group communication and group membership Atomic multicast Adapter Fault tolerance Consistency Junction layer

5. November, 19thGDS meeting, LIP6, Paris5 Hierarchical Client GDG LDG GDG : Global Data Group LDG : Local Data Group

6. November, 19thGDS meeting, LIP6, Paris6 Synchronization Point of View  Naturally similar to data management  1 lock per piece of data  Pieces of data are strongly linked to their locks Synchronisation manager Client SM

7. November, 19thGDS meeting, LIP6, Paris7 Synchronization Point of View  The synchronization manager is replicated the same way

8. November, 19thGDS meeting, LIP6, Paris8 Synchronization Point of View Client

9. November, 19thGDS meeting, LIP6, Paris9 In Case of Failure  Failure of a provider (group member)  Held by the proactive group membership : the faulty provider is replaced by a new one  Failure of a client  With a lock => regenerate the token  Without a lock => do nothing  Failure of a whole local group  Very low probability  As if it was a client (as it is for the global group)

10. November, 19thGDS meeting, LIP6, Paris10 False Detection  Blocking unlocking with return code  To be sure that an operation as performed a client has to do something like: do{ lock(data) process(data) } while(unlock(data) is not ok) // here we’re sure that the action has been taken into account

11. November, 19thGDS meeting, LIP6, Paris11 Actual JuxMem’s Synchronization (Sum up)  Authorization based  Exclusive (acquire)  Non exclusive (acquireR)  Centralized (active replication)  Strongly coupled with data management  Hierarchical and fault tolerant

12. November, 19thGDS meeting, LIP6, Paris12 Data Updates : When?  Eager (current version) :  When a lock is released update all replicas  High fault tolerant level / Low performances Client

13. November, 19thGDS meeting, LIP6, Paris13 Data Updates : When?  Lazy (possible implementation) :  Update a local data group when a lock is acquired Client

14. November, 19thGDS meeting, LIP6, Paris14 Data Updates : When?  Intermediate (possible implementation) :  Allow a limited number of local update before propagating all the updates to the global level Client

15. November, 19thGDS meeting, LIP6, Paris15 Data Updates : When?  A hierarchical consistency model?  Local lock  Global lock

16. November, 19thGDS meeting, LIP6, Paris16 Distributed Synchronization Algorithms  Naïmi-Trehel’s  Token based  Mutual exclusion  Extented by REGAL  Hierarchical (Marin, Luciana, Pierre)  Fault tolerant (Julien)  Both?  A fault tolerant, grid aware synchronization module used by JuxMem?

17. November, 19thGDS meeting, LIP6, Paris17 Open Question and Future Work  Interface between JuxMem providers and synchronization module  Providers have to be informed of synchronization operations to perform updates  Future work (Julien & Sébastien)  Centralized data / distributed locks?  Data may become distributed in JuxMem (epidemic protocols, migratory replication, etc.)  Algorithms for token-based non-exclusive locks?  May allow more flexibility for replication techniques (passive or quorum based)

18. November, 19th GDS meeting, LIP6, Paris 18 Other Open Issues in JuxMem

19. November, 19thGDS meeting, LIP6, Paris19 Junction Layer  Decoupled design  Need to refine the junction layer Fault tolerance Consistency Junction layer Send Receive

20. November, 19thGDS meeting, LIP6, Paris20 Replication Degree  Actual features : the client specifies  The global data group cardinality (i.e number of clusters)  The local data groups cardinality (i.e number of replicas in each cluster)  Desirable features : the client specifies  The criticality degree of the piece of data  The access needs (model, required perfs)  A monitoring module  Integrated to Marin’s failure detectors?  Current MTBF, message losses, etc.  May allow JuxMem to dynamically deduce the replication degree for each piece of data

21. November, 19thGDS meeting, LIP6, Paris21 Application Needs  Access model  Data grain?  Access patterns  Multiple readers?  Locks shared across multiple clusters?  Data criticality  Are there different levels of criticality?  What kind of advice the application can give concerning those 2 aspects?  Duration of the application?  Traces : latency, crashes, message losses?