1. Telegraph Continuously Adaptive Dataflow Joe Hellerstein

2. Scenarios Ubiquitous computing: more than clients –sensors and their data feeds are key smart dust, biomedical (MEMS sensors) each consumer good records (mis)use –disposable computing video from surveillance cameras, broadcasts, etc. Global Data Federation –all the data is online – what are we waiting for? –The plumbing is coming XML/HTTP, etc. give LCD communication but how do you flow, summarize, query and analyze data robustly over many sources in the wide area?

3. Dataflow in Volatile Environments Federated query processors a reality –Cohera, IBM DataJoiner –No control over stats, performance, administration Large Cluster Systems “Scaling Out” –No control over “system balance” User “CONTROL” of running dataflows –Long-running dataflow apps are interactive –No control over user interaction Sensor Nets: the next killer app –E.g. “Smart Dust” –No control over anything! Telegraph –Dataflow Engine for these environments

4. Data Flood: Main Features What does it look like? –Never ends: interactivity required Online, controllable algorithms for all tasks! –Big: data reduction/aggregation is key –Volatile: this scale of devices and nets will not behave nicely

5. The Telegraph Dataflow Engine Key technologies –Interactive Control interactivity with early answers and examples online aggregation for data reduction –Dataflow programming via paths/iterators Elevate query processing frameworks out of DBMSs Long tradition of static optimization here –Suggestive, but not sufficient for volatile environments –Continuously adaptive flow optimization massively parallel, adaptive dataflow via Rivers and Eddies

6. CONTROL Continuous Output and Navigation Technology with Refinement On Line Data-intensive jobs are long-running. How to give early answers and interactivity? –online interactivity over feeds pipelining “online” operators, data “juggle” –online data correlation algs: ripple joins, online mining and aggregation –statistical estimators, and their performance implications Deliver data to satisfy statistical goals Appreciate interplay of massive data processing, stats, and HCI “ Of all men's miseries, the bitterest is this: to know so much and have control over nothing” –Herodotus

7. Performance Regime for CONTROL New “Greedy” Performance Regime –Maximize 1 st derivative of the user-happiness function Time 100% CONTROL Traditional

8. CONTROL Continuous Output and Navigation Technology with Refinement On Line

11. Potter’s Wheel Anomaly Detection

12. River We built the world’s fastest sorting machine –On the “NOW”: 100 Sun workstations + SAN –But it only beat the record under ideal conditions! River: performance adaptivity for data flows on clusters –simplifies management and programming –perfect for sensor-based streams

13. Declarative Dataflow: NOT new Database Systems have been doing this for years –Xlate declarative queries into an efficient dataflow plan –“query optimization” considers: Alternate data sources (“access methods”) Alternate implementations of operators Multiple orders of operators A space of alternatives defined by transformation rules Estimate costs and “data rates”, then search space But in a very static way! –Gather statistics once a week –Optimize query at submission time –Run a fixed plan for the life of the query And these ideas are ripe to elevate out of DBMSs –And outside of DBMSs, the world is very volatile –There are surely going to be lessons “outside the box”

14. Static Query Plans Volatile environments like sensors need to adapt at a much finer grain

15. Continuous Adaptivity: Eddies How to order and reorder operators over time – based on performance, economic/admin feedback Vs.River: –River optimizes each operator “horizontally” –Eddies optimize a pipeline “vertically” Eddy

16. Competitive Eddies Eddy R2R1 R3 S1S2 S3  hash blockindex1  index2

17. Telegraph: Putting it Together Scalable, adaptive dataflow infrastructure. Apps include… –sensor nets –massively parallel and wide-area query engines –net appliances: chaining xform8n/aggreg8n/compression/ etc. in proxies –any volatile dataflow scenario Technology: a marriage of… –CONTROL, Rivers & Eddies Many research questions here E.g. how to combine River and Eddy adaptivity E.g. how to tune Eddies for statistical performance goals –Combinations of browse/query/mine at UI –Storage management to handle new hardware realities Look for a live service this summer!

18. Integration with Endeavour Give –Be data-intensive backbone to diverse clients –Be replication/delivery dataflow engine for OceanStore –Telegraph Storage Manager provides storage (xactional/otherwise) for OceanStore –Provide platform for data-intensive “tacit info mining” Take –Leverage OceanStore to manage distributed metadata, security –Leverage protocols out of TinyOS for sensors

19. Connectivity & Heterogeneity Lots of folks working on data format translation, parsing –we will borrow, not build –currently using JDBC & Cohera Net Query commercial tool, donated by Cohera Corp. gateways XML/HTML (via http) to ODBC/JDBC –we may write “Teletalk” gateways from sensors Heterogeneity –never a simple problem –Control project developed interactive, online data transformation tool: ABC

20. More Info Collaborators: –Mike Franklin, Eric Brewer, Christos Papadimitriou –Sirish Chandrasekaran, Amol Deshpande, Kris Hildrum, Sam Madden, Vijayshankar Raman, Mehul Shah Me: jmh@cs.berkeley.edu Web: –http://db.cs.berkeley.edu/telegraph –http://control.cs.berkeley.edu

21. Extra slides for backup