BCI 2003 Aristotle University of Thessaloniki 1

November 22, 2003

Updating Web views distributed over wide area networks

Sidiropoulos AntonisKatsaros Dimitrios

Aristotle Univ. of Thessaloniki, Greece

Presentation by:Katsaros Dimitrios

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Content Distribution Networks

INTERNET 2

1

Origin Web server

Web client

4

4

1

3

2

3

CDN Cache Servers

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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

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Disseminating Updates

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• Related work & Motivation• Proposed method• Preliminary performance

evaluation• Conclusions & Future work

Outline

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• Related work & Motivation• Proposed method• Preliminary performance

evaluation• Conclusions & Future work

Presentation Outline

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• Lack of bandwidth to disseminate all updates

• Many caches• Single point of updates

generation

Best-effort cache coherency

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• 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

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• Related work & Motivation• Proposed method• Preliminary performance

evaluation• Conclusions & Future work

Presentation Outline

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Web object freshness

Freshness of object O over period [ti,tj] Freshness of database D with N objects

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• 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 Oi each with popularity fOi the freshness is defined as :

Weighted Web object freshness

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• 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

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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)

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• 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 cost PopularityRefresh1 4 5Refresh2 3 4Refresh3 1 2

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2-D Gantt chart for FIFO

popu

larit

y

2

8

11

6

8

4

2

64cost

1

2

3

Divergence = 1 - Freshness = Area under the thick polygonal line = 64

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Can we do better ?

popu

larit

y

2

8

11

6

8

4

2

64cost

1

2

3

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Can we do better ?

popu

larit

y

2

8

11

6

8

4

2

64cost

1

2

3

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Yes ! Schedule the max(pop/cost)

Divergence = 1 - Freshness = Area under the thick polygonal line = 58 (10% gains even for this small example)

popu

larit

y

2

8

11

6

8

4

2

64cost

1

2

3

pop/cost

Refresh1 5/4=1,25

Refresh2 4/3=1,33

Refresh3 2/1=2

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• 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

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• Related work & Motivation• Proposed method• Preliminary performance

evaluation• Conclusions & Future work

Presentation Outline

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Simulated System Hardware

MasterCDN

CDN server n

Routers/Gateways

Parasol NodeParasol CPUParasol Network Link

RouterRouter

Router

RouterRouterRouter

CPU:2 CPU:1

CPU:0

CDN server 1 CDN server 2

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Simulated System Model

DispatcherScheduleralgorithm

Relation updates

DBMS

ViewUpdater

CDN1updater

CDN2updater

CDNnupdater

CDN1 CDN2 CDNn

DB updates

Request for view update

Master CDN

1

2 3

4

5 6

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masterCDN components

DBMS

CPU:1ViewUpdater

Node:MasterCDN

CPU:0DispatcherCPU:2

Pool of views to

be updated

Scheduler

algorithm

CDN1updater

Pool of

views to

transmit CDN2

updater

Pool of views

to transmi

t CDNnupdater

Pool of

views to

transmit

Rel. Q

ueue

Relation update

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• 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

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Freshness vs. Update rate

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Freshness vs. Update rate

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Freshness vs. Update rate

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Freshness vs. #Relations

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LSR Freshness vs. update rate

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Freshness vs. (#Rel, dep_density)

Top: 100 Rels

Botom: 500 Rels

Left: Sparse dep. Right: Dense dep.

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• Related work & Motivation• Proposed method• Preliminary performance

evaluation• Conclusions & Future work

Presentation Outline

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• 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

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November 22, 2003

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

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Sidiropoulos AntonisDept. of InformaticsAristotle UniversityThessaloniki, 54124, Greeceasidirop@csd.auth.grhttp://users.auth.gr/~asidirop

Katsaros DimitriosDept. of InformaticsAristotle UniversityThessaloniki, 54124, Greecedkatsaro@csd.auth.grhttp://skyblue.csd.auth.gr

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