1. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 1 Updating Web views distributed over wide area networks Sidiropoulos Antonis Katsaros Dimitrios Aristotle Univ. of Thessaloniki, Greece Presentation by: Katsaros Dimitrios

2. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 2 Content Distribution Networks INTERNET 2 1 Origin Web server Web client 4 4 1 3 2 3 CDN Cache Servers

3. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 3 Content Distribution Networks Advantages –prevention of the flush crowd problem –avoidance of network congestion –reduction of user-perceived latency e.g., Akamai –launced in early 1999 –12,000 servers –in 1,000 networks

4. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 4 Disseminating Updates

5. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 5 Related work & Motivation Proposed method Preliminary performance evaluation Conclusions & Future work Outline

6. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 6 Related work & Motivation Proposed method Preliminary performance evaluation Conclusions & Future work Presentation Outline

7. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 7 Lack of bandwidth to disseminate all updates Many caches Single point of updates generation Best-effort cache coherency

8. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 8 Static Web object caching/prefetching (Katsaros & Manolopoulos, ACM SAC’04) (Nanopoulos, Katsaros & Manolopoulos, IEEE TKDE’03) Dynamic Web object caching/prefetching –cache plays the central role i.e., prefetching (Cho & Garcia-Molina, SIGMOD’00) and (Gal & Eckstein, J.ACM’01) –minimizing the bandwidth consumption and query latency in the presence of constraints on the age or accuracy of cached objects (Bright & Raschid, VLDB’02; Cohen & Kaplan, Computer Networks’02; Olston & Widom, SIGMOD’01) –strong cache coherence maintenance (Challenger, Iyengar & Dantzig, INFOCOM’99) –update dissemination, best-effort but with a single cache (Labrinidis & Roussopoulos, VLDB’01) –caches and sources cooperate, best effort caching, (Olston & Widom, SIGMOD’02) –optimal tranmission of updates, but fixed assumptions about update rates and transmission capabilities (Wang, Evans & Kwok, Information Systems Frontiers,’03) Related work

9. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 9 Related work & Motivation Proposed method Preliminary performance evaluation Conclusions & Future work Presentation Outline

10. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 10 Web object freshness Freshness of object O over period [t i,t j ]Freshness of database D with N objects

11. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 11 The access pattern of Web objects is skewed Objects with higher access rates contribute more to what is perceived as database freshness For a database with N objects O i each with popularity f Oi the freshness is defined as : Weighted Web object freshness

12. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 12 Devise a sequence of update disseminations so as to maximize F(D,T) Hence: The “best-effort” cache coherence maintenance is a nonpreemptive scheduling problem Maintain best-effort coherency

13. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 13 FIFO scheduling Assume that there are sufficient –network resources –processing resources Use of the FIFO scheduling (First-Come- first-Served) Visualize our scheduling problem with the 2-dimensional Gantt charts (Goemans & Williamson, SIAM Journal on Discrete Mathematics’00)

14. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 14 We have three pending refreshes in the server's queue, i.e., Refresh1, Refresh2 and Refresh3, which occurred with the order mentioned Example of updates Total costPopularity Refresh145 Refresh234 Refresh312

15. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 15 2-D Gantt chart for FIFO popularity 2 8 11 6 8 4 2 64 cost 1 2 3 Divergence = 1 - Freshness = Area under the thick polygonal line = 64

16. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 16 Can we do better ? popularity 2 8 11 6 8 4 2 64 cost 1 2 3

17. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 17 Can we do better ? popularity 2 8 11 6 8 4 2 64 cost 1 2 3

18. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 18 Yes ! Schedule the max(pop/cost) Divergence = 1 - Freshness = Area under the thick polygonal line = 58 (10% gains even for this small example) popularity 2 8 11 6 8 4 2 64 cost 1 2 3 pop/cost Refresh15/4=1,25 Refresh24/3=1,33 Refresh32/1=2

19. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 19 Select for dissemination the update with the largest popularity/cost ratio It can be proved that this rule is optimal No longer optimal in the presence of dependencies Very efficient heuristic even when there exist dependencies Largest Slope Rule scheduling

20. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 20 Related work & Motivation Proposed method Preliminary performance evaluation Conclusions & Future work Presentation Outline

21. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 21 Simulated System Hardware MasterCDN CDN server n Routers/Gateways Parasol Node Parasol CPU Parasol Network Link Router CPU:2 CPU:1 CPU:0 CDN server 1CDN server 2

22. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 22 Simulated System Model Dispatcher Scheduler algorithm Relation updates DBMS ViewUpdater CDN1 updater CDN2 updater CDNn updater CDN1CDN2 CDNn DB updates Request for view update Master CDN 1 2 3 4 56

23. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 23 masterCDN components DBMS CPU:1 ViewUpdater Node:MasterCDN CPU:0 Dispatcher CPU:2 Pool of views to be updated Schedul er algorith m CDN1 updater Pool of views to trans mit CDN2 updater Pool of views to transmi t CDNn updater Pool of views to trans mit Rel. Queue Relation update

24. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 24 Synthetic (sample CDN with 10 edge servers) –Synthetic data generator Modeling network nodes, network bandwidth, size of documents, relations, views, view derivation hierarchy, update rates, popularity Examine the impact of: –update rate –number of relations Methodology

25. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 25 Freshness vs. Update rate

26. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 26 Freshness vs. Update rate

27. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 27 Freshness vs. Update rate

28. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 28 Freshness vs. #Relations

29. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 29 LSR Freshness vs. update rate

30. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 30 Freshness vs. (#Rel, dep_density) Top: 100 Rels Botom: 500 Rels Left: Sparse dep.Right: Dense dep.

31. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 31 Related work & Motivation Proposed method Preliminary performance evaluation Conclusions & Future work Presentation Outline

32. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 32 Conclusions –we proposed a best-effort cache coherence maintenance scheme for the edge servers of a CDN –it is a pure push-based dissemination method –the scheme is based on the LSR scheduling algorithm –we presented preliminary results to justify its efficiency Future work –Organize the edge serves into a (possibly) deep hierarchy, so as to parallelize the update dissemination Conclusions & Future work

33. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 33 1.L. Bright and L. Raschid, Using Latency-Recency Profiles for Data Delivery on the Web, Proc. of the VLDB, pp. 550-561, 2002. 2.J. Challenger, A. Iyengar, and P. Dantzig, A Scalable System for Consistently Caching Dynamic Web Data, Proc. of the IEEE INFOCOM, 1999. 3.J. Cho and H. Garcia-Molina, Synchronizing a Database to Improve Freshness, Proc. of the ACM SIGMOD, pp. 117-128, 2000. 4.E. Cohen and H. Kaplan, Refreshment Policies for Web Content Caches, Computer Networks, 38(6), 795-808, 2002. 5.A. Gal and J. Eckstein, Managing Periodically Updated Data in Relational Databases: A Stochastic Modeling Approach, Journal of the ACM, 48(6), pp. 1141-1183, 2001. 6.M.X. Goemans and D.P. Williamson, Two-Dimensional Gantt Charts and a Scheduling Algorithm of Lawler, SIAM Journal on Discrete Mathematics, 13(3), pp. 281-294, 2000. 7.D. Katsaros and Y. Manolopoulos, Caching in Web Memory Hierarchies, Proc. of the ACM SAC, 2004. 8.A. Labrinidis and N. Roussopoulos, Update Propagation Strategies for Improving the Quality of Data on the Web, Proc. of the VLDB, 2001. 9.A. Nanopoulos, D. Katsaros and Y. Manolopoulos, A Data Mining Algorithm for Generalized Web Prefetching, IEEE Trans. on Knowledge and Data Engineering, 15(5), pp.1155-1169, 2003. 10.C. Olston and J. Widom, Adaptive Precision Setting for Cached Approximate Values, Proc. of the ACM SIGMOD, pp. 355-366, 2001. 11.C. Olston and J. Widom, Best-Effort Cache Synchronization with Source Cooperation, Proc. of the ACM SIGMOD, pp. 73-84, 2002. 12.J.W. Wang, D. Evans and M. Kwok, On Staleness and the Delivery of Web Pages, Information Systems Frontiers, 5(2), pp. 129-136, 2003. References

34. November 22, 2003 BCI 2003 Aristotle University of Thessaloniki 34 Sidiropoulos Antonis Dept. of Informatics Aristotle University Thessaloniki, 54124, Greece asidirop@csd.auth.gr http://users.auth.gr/~asidirop Katsaros Dimitrios Dept. of Informatics Aristotle University Thessaloniki, 54124, Greece dkatsaro@csd.auth.gr http://skyblue.csd.auth.gr Contact information