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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 Why Content Management Contributions of Our Work Multicache-Based Content Management - PowerPoint PPT Presentation
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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– 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
1.1. Why Content Management
User Network Servers
②① ③ ④
Maximize Hit Rates (r = / )② ① (or Weighted HR)
WISE'2000 (C)[email protected] 4
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: 200ms300ms
Caching Is Necessary To AdaptivelyReduce Remote Data Requests
WISE'2000 (C)[email protected] 5
1.2. Why Content Management
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,
Replacement policies based on empirical formula are difficult to deal with these!
WISE'2000 (C)[email protected] 6
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
WISE'2000 (C)[email protected] 7
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
WISE'2000 (C)[email protected] 8
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
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
Cache Components
• 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
Cli
ent
Web S
ervers
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– Cache for Keeping Objects With Special Properties
• Cache Judge – Make Final Decisions From A Set of Eviction Candidat
es
WISE'2000 (C)[email protected] 14
The Procedural Description
Central Router services each request. Suppose current request is for doc
ument 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.
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
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.
WISE'2000 (C)[email protected] 20
Experiment Results
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
0.3
0.15 0.3 0.5 0.8 1.52 3 4 5 6 7 8
RU-SP SzLRU SgLRU LRV
* *
WISE'2000 (C)[email protected] 21
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
WISE'2000 (C)[email protected] 22
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