1. Multicache-Based Content Management for Web Caching Kai Cheng and Yahiko Kambayashi Graduate School of Informatics, Kyoto University Kyoto JAPAN

2. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp2 Outline of the Presentation Introduction –Why Content Management –Contributions of Our Work Multicache-Based Content Management Content Management Scheme for LRU-SP Experimental Evaluation Concluding Remarks

3. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp3 1.1. Why Content Management UserNetworkServers ② ① ③ ④ Maximize Hit Rates (r = ② / ① ) (or Weighted HR)

4. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp4 Can Web Do Without Caching? Bandwidth Scarcity= Weakest Part –Unrealistic to Update All Resources “Hot-Spot” Servers –Unpredictable of Server Overload Inherent Latency = Light Speed  Distance –Even Sufficient Bandwidth and Server Capacity –Transoceanic Data Transfer: 200ms  300ms Caching Is Necessary To Adaptively Reduce Remote Data Requests

5. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp5 1.2. Why Content Management Traditional Caching Web CachingImplications Process Oriented Human-User Oriented User Preferences System-LevelApplication-Level Semantic Information Data Block BasedDocument-Based Varying Sizes, Types Memory-BasedDisk-Based Persistent Storage, Large Size, Replacement policies based on empirical formula are difficult to deal with these!

6. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp6 Deploying Content Management To Support –Larger Cache Space –Sophisticated Control Logic To Support –Sophisticated Replacement Policies With User-Oriented Performance Metrics Document Treated as Semantic Unit

7. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp7 1.3. Contributions of This Work A Multicache Architecture for Implementing Sophisticated Content Management, Including a New Cache Definition A Study of Content Management for LRU-SP Simulations to Compare LRU-SP Against Others

8. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp8 Previous Work Classifications in Approximate Implementations of Complicated Caching Schemes –LRV, LNC-W3-U, etc. Segmentation in Traditional Caching As Tradeoffs Between Performance and Complexity –Segmented FIFO, FBR, 2Q etc. Disadvantages –Both Are Built-in Ad hoc Implementation, Rather than An Independent Mechanism –Can Not Support Sophisticated Category nor Semantic- Based Classification

9. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp9 Managing LFU Contents in Multiple Priority Queues 2 1 >2 B(8)C(6)D(3) A(10)E(2)F(2) F(1)G(1)H(1) Hit Outs First In First Out Order References A(10) B(8)C(6) D(3) E(2) F(2) F(1)G(1) H(1)

10. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp10 Cache Components Space –Limit Storage Space Contents –Objects Selected for Caching Policies –Replacement Policies Constraints –Special Conditions Space Contents Policies Constraints Space

11. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp11 Constraints for Cache Admission Constraints –Define Conditions for Objects Eligible For Caching e.g. (size < 2MB) && !(Source = local) Freshness Constraints –Define Conditions for Objects Fresh Enough For Re- Use e.g. (Type = news) && (Last-Modified < 1week) Miscellaneous Constraints e.g. (Time= end-of-day)  (Total-Size< 95%*Cache- Size)

12. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp12 Multicache Architecture SUBCACHE CENTRAL ROUTER CENTRAL ROUTER Client Web Servers Web Cache With Multiple Subcaches JUDGE CONSTRAINTS CKB IN-CACHE Request/Response Cache Knowledge Base

13. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp13 Components of the Architecture Central Router – Control and Mediate the Cache Cache Knowledge Base (CKB) –A Set of Rule Based To Allocate Objects R1. Allocate(X, 1):-url(X, U), match(U, *.jp),content(X, baseball) Subcaches –Cache for Keeping Objects With Special Properties Cache Judge –Make Final Decisions From A Set of Eviction Candidates

14. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp14 The Procedural Description Central Router services each request. Suppose current request is for document p; 1.Locating p by In-cache Index 2.If p is not in cache, download p; i.Validate Constraints, if false, loop; ii.Fire rules in CKB, let subcache ID = K ; iii.While no enough space in subcache K for p –Subcache K selects an eviction ; – If space sharing, other subcaches do same; –Judge assesses the eviction candidates; –Purge the victim; iv. Cache p in subcache K 3.If p is in subcache, do i) - iv) re-cache p.

15. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp15 Content Management for LRU-SP LRU (Least Recently Used) –Primarily Designed for Equal Sized Objects, and Only Recency of Reference In Use Extended LRUs –Size-Adjusted LRU (SzLRU) –Segmented LRU (SgLRU) LRU-SP (Size-Adjusted and Popularity-Aware LRU) –Make SzLRU Aware of Popularity Degree

16. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp16 Probability of Re-Reference As a Function of Current Reference Times

17. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp17 Cost –To-Size Ratio Model An Object A In Cache Saves Cost nref * (1/atime) –nref is the frequency of reference –atime is the time since last access, (1/atime) is the dynamic frequency of A When Put In Cache, It Takes Up Space size –Cost-to-size ratio = nref /(size*atime) The Object With Least Ratio Is Least Beneficial One

18. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp18 Content Management of LRU-SP CKB Rule: –Allocate(X, log(size/nref)):-Size(X, size), Freq(X, nref) Subcaches –Least Recently Used (LRU) Judge –Find the One With Largest (size*atime)/nref –The Larger and Older and Colder, the Fast An Object Will Be Purged

19. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp19 Predicted Results A higher Hit Rate is expectable for LRU-SP, because it utilizes three indicators to document popularity. However, higher Hit Rates are usually at the cost of lower Byte Hit Rates, because smaller documents contribute less to bytes of hit data.

20. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp20 Experiment Results * *

21. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp21 Explanations LRU-SP really obtained a much higher Hit Rate than either SzLRU, SgLRU or LRV. LRU-SP also obtained a higher Byte Hit Rate, when cache space exceeds 3% of total required space. LRU-SP only incurs O(1) time complexity in content management. LRU-SP a significantly improved algorithm

22. WISE'2000(C)chengk@kuis.kyoto-u.ac.jp22 Concluding Remarks Multicahe-Based Architecture Has Proved Ideal To Realize Good Balance Between High Performance and Low Overhead It Is Capable of Incorporating Semantic Information as Well as User Preference In Caching It Can Work With Data Management Systems to Support Web Information Integration