multicache-based content management for web caching
DESCRIPTION
Multicache-Based Content Management for Web Caching. Kai Cheng and Yahiko Kambayashi Graduate School of Informatics, Kyoto University Kyoto JAPAN. Outline of the Presentation. Introduction Localizing Web Contents Why Content Management Contributions of Our Work - PowerPoint PPT PresentationTRANSCRIPT
Multicache-Based Content Management
for Web Caching
Kai Cheng and Yahiko Kambayashi
Graduate School of Informatics, Kyoto University
Kyoto JAPAN
WISE'2000 (C)[email protected] 2
Outline of the Presentation
• Introduction– Localizing Web Contents– Why Content Management– Contributions of Our Work
• Multicache-Based Content Management• Content Management Scheme for LRU-SP• Experimental Evaluation• Concluding Remarks
WISE'2000 (C)[email protected] 3
Web Caching For Localizing Web Contents
19%
40%
75%
0%10%20%30%40%50%60%70%80%
1994 1996 1997
Internet Traffic for WWW• World Wide Content
Access/Delivery– Bandwidth Constraints– “Hot-Spot” Servers– Inherent Latency
(200300ms)
• Web Caching For Localizing Web Contents – Reduce Network Traffic– Distribute Server Load– Reduce Response Times– Can We Expect More ?
WISE'2000 (C)[email protected] 4
Characteristics and Implications
Traditional Caching
Web Caching Implications
Process OrientedHuman-User
OrientedUser Preferences
System-Level Application-Level Semantic Information
Data Block Based Document-Based Varying Sizes, Types
Memory-Based Disk-BasedPersistent Storage,
Large Size,
WISE'2000 (C)[email protected] 5
Limitations of Current Caching Schemes
• Document Managed As Physical Unit, Not Semantic Unit.
• Only Physical Properties Being Used
• Less Organized, Less Structured
• Only Support Simple Control Logic
Beyond Simple Priority Queues, Towards Sophisticated Content Management
WISE'2000 (C)[email protected] 6
Content Management
• Basic Features– Larger Cache Space– Sophisticated Control Logic
• More Challenging – Sophisticated Replacement Policies With
• User-Oriented Performance Metrics
• Document Managed as Semantic Unit
WISE'2000 (C)[email protected] 7
Contributions of This Work
• A Multicache Architecture for Implementing Sophisticated Content Management
• A Study of Content Management for LRU-SP
• Simulations to Compare LRU-SP Against Others
WISE'2000 (C)[email protected] 8
Previous Work• Classifications (Cache Data )
– LRV, LNC-W3-U, etc.
• Segmentation (Cache Space)– Segmented FIFO, FBR, 2Q etc.
• Features– Differentiating Data With Different Properties
• Shortages: – No Sophisticated Category
– No Semantic-Based Classification
WISE'2000 (C)[email protected] 9
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
Hit
Outs
Outs
First In First Out Order
Ref
eren
ces
A(10) B(8) C(6) D(3) E(2) F(2) F(1) G(1) H(1)
WISE'2000 (C)[email protected] 10
Basics of Cache
• Space– Limit Storage Space
• Contents– Objects Selected for Caching
• Policies– Replacement Policies
• Constraints– Special Conditions
Space
Contents Policies
Constraints
SpaceSpace
WISE'2000 (C)[email protected] 11
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)
WISE'2000 (C)[email protected] 12
Multicache Architecture
SUBCACHE SUBCACHE SUBCACHE SUBCACHE SUBCACHESUBCACHE
CENTRAL
ROUTER
CENTRAL
ROUTER
Client WWW
Web Cache With Multiple Subcaches
JUDGE
CONSTRAINTSCONSTRAINTS
CKBCKB
IN-CACHEIN-CACHE
Request/Response
Cache Knowledge
Base
WISE'2000 (C)[email protected] 13
Components of the Architecture
• Central Router – Control and Mediate the Cache
• Cache Knowledge Base (CKB)– A Set of Rule Based To Allocate ObjectsR1. Allocate(X, 1):-url(X, U), match(U, *.jp),content(X, baseball)
• Subcaches– Keep Objects With Special Characteristics
• Cache Judge – Make Final Decisions From A Set of Eviction Candidat
es
WISE'2000 (C)[email protected] 14
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.
The Procedural Description
WISE'2000 (C)[email protected] 15
Content Management for LRU-SP
• LRU (Least Recently Used)– Primarily Designed for Equal Sized Objects, an
d 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
WISE'2000 (C)[email protected] 16
Probability of Re-ReferenceAs a Function of Current Reference Times
00.10.2
0.30.4
0.50.6
0.70.8
0.9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Next Reference Next K References After First
WISE'2000 (C)[email protected] 17
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
WISE'2000 (C)[email protected] 18
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
WISE'2000 (C)[email protected] 19
Multicache Architecture for LRU-SP
LRU Subcache ①
LRU Subcache ②
LRU Subcache ③
CKB
Hits A, B
A
B
C
Judge
a
b
c
Ca
Computational Complexity O(1)
WISE'2000 (C)[email protected] 20
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, given a similar popularity, because smaller documents contribute less to bytes of hit data.
WISE'2000 (C)[email protected] 21
Experiment Results Better Than Expected
0
0.05
0.1
0.15
0.2
0.25
0.15 0.3 0.5 0.8 1.5 2 3 4 5 6 7 8
LRU-SP SzLRU SgLRU LRV
0
0.05
0.1
0.15
0.2
0.25
0.3RU-SP SzLRU SgLRU LRV
* *
WISE'2000 (C)[email protected] 22
Results & Explanations
• LRU-SP really obtained a much higher Hit Rate than SzLRU, SgLRU and LRV.
• LRU-SP also obtained a high Byte Hit Rate, especially when cache space exceeds 3% of total required space.
• Really Popular Objects Are Saved, So Both Hit Rate and Byte Hit Rate are Improved.
• LRU-SP only incurs O(1) time complexity in content management.
WISE'2000 (C)[email protected] 23
Concluding Remarks
• Multicahe-Based Architecture Has Proved Well-Performed In Balancing High Performance and Low Overhead
• Possible To Incorporate Semantic Information as Well as User Preference In Caching
• It Can Work With General Database Systems to Support Web Information Integration. (Future Work)
Thank You !And Welcome To
http://www.isse.kuis.kyoto-u.ac.jp