1. Building a Large Location Table to Find Replicas of Physics Objects Koen Holtman Heinz Stockinger CERN/CMS CHEP ’2000 Feb 7-11, 2000

2. Replication models Different replication models: File based: –FTP run files, Objectivity DB files,... –Import/attach them to local data store –(Objectivity DRO/FTO) Linkable Objectivity AMS server tricks: –Transparent local / remote file access –Page-level replication?? Object based (this talk): –Can replicate every single object / set of objects –Transparent local / remote object access –Requires large Object Location Table (OLT) In Objectivity, go from logical OID to physical OID

3. The problem >10 10 physics objects (event related objects), all potentially replicated Lookup: ‘Find the best replica of the physics object with (logical) OID xyz’ –Automatic (grid-like) –Best replica is not always nearest –Lookup operation needs to be fast, scalable Regional Centre Regional Centre Regional Centre

4. Goal of this talk Goal: show that an OLT can be built By showing one viable implementation –This implementation is a spin-off of the work on automatic reclustering >10 10 scalability problem can be solved by –Exploiting specific properties of physics analysis –Limiting freedom of lookup operations No Objectivity-style ‘on demand’ navigation

5. Divide and conquer Partition events into chunks, 1 subjob per chunk Definition of logical OID: (chunk ID, event sequence number, type/version ID)

6. Job and storage model Job model: job consists of –set of event IDs (chunk ID, event seq nr) –1 or more type/version IDs –physics code code is run using subjobs, these get handed iterators Storage model: –unit of storage is collection –subset of horizontal bar above

7. Structure… Insert and delete collections... Next slide: 1 subjob, 1 type...

8. 1 subjob, 1 type Say subjob at CERN wants to read objects (1,3,1),(1,4,1),(1,7,1),... –At start of subjob, global schedule is computed, say: try collection 1 first, then try collection 3. Subjob iteration is in increasing sequence number, step through indices alongside with iteration

9. Computing the schedule Optimal schedule computed at start of subjob –Optimiser finds schedule with minimal cost –Cost(‘read 3,4,7,…’,[1,3]) = C coll (‘read 3,4,… from 1’) + C coll (‘read 7,… from 3) = …. –Value of C coll ( ) depends on location of collection, network load, access method, etc. –Calculations use collection contents indices only Finding optimal schedule is an NP-complete problem, exponential time complexity, but in practice....

10. Runtime of scheduler Time to compute optimal schedule (micro sec) Can also timeout, gives good schedule 1 sec

11. Conclusions Large object location tables can be built! –They are an important component in implementing the grid-like aims of the CMS CTP –Implementation shown is spinoff from work on reclustering Scalability by using specific properties of physics analysis, and by limiting the freedom of lookup operations –No Objectivity-style ‘on demand’ navigation, all objects needs to be requested from the start Cost functions can model any access method –Optimisation is both generic and cheap Long paper will appear in future