Multicache-Based%20Content%20Management%20for%20Web%20Caching

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Multicache-Based Content Management for Web
Caching

• Kai Cheng and Yahiko Kambayashi
• Graduate School of Informatics, Kyoto University
• Kyoto JAPAN

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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

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Web Caching For Localizing Web Contents

• World Wide Content Access/Delivery
• Bandwidth Constraints
• Hot-Spot Servers
• Inherent Latency (200?300ms)
• Web Caching For Localizing Web Contents
• Reduce Network Traffic
• Distribute Server Load
• Reduce Response Times
• Can We Expect More ?

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Characteristics and Implications
Traditional Caching Web Caching Implications
Process Oriented Human-User Oriented User Preferences
System-Level Application-Level Semantic Information
Data Block Based Document-Based Varying Sizes, Types
Memory-Based Disk-Based Persistent Storage, Large Size,
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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

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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

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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

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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

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Managing LFU Contents in Multiple Priority Queues
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Basics of Cache

• Space
• Limit Storage Space
• Contents
• Objects Selected for Caching
• Policies
• Replacement Policies
• Constraints
• Special Conditions

Space
Space
Constraints
Policies
Contents
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Constraints for Cache

• Admission Constraints
• Define Conditions for Objects Eligible For
  Caching
• e.g. (size lt 2MB) !(Source local)
• Freshness Constraints
• Define Conditions for Objects Fresh Enough For
  Re-Use
• e.g. (Type news) (Last-Modified lt 1week)
• Miscellaneous Constraints
• e.g. (Time end-of-day)? (Total-Sizelt
  95Cache-Size)

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Multicache Architecture
Web Cache With Multiple Subcaches
IN-CACHE
CONSTRAINTS
CENTRAL ROUTER
Request/Response
CKB
Client
WWW
SUBCACHE
SUBCACHE
SUBCACHE
JUDGE
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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
• Keep Objects With Special Characteristics
• Cache Judge
• Make Final Decisions From A Set of Eviction
  Candidates

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The Procedural Description

• Central Router services each request. Suppose
  current request is for document p
• Locating p by In-cache Index
• If p is not in cache, download p
• Validate Constraints, if false, loop
• Fire rules in CKB, let subcache ID K
• 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
• Cache p in subcache K
• If p is in subcache , do i) - iv) re-cache p.

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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

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Probability of Re-ReferenceAs a Function of
Current Reference Times
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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 /(sizeatime)
• The Object With Least Ratio Is Least Beneficial
  One

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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 (sizeatime)/nref
• The Larger and Older and Colder, the Fast An
  Object Will Be Purged

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Multicache Architecture for LRU-SP
A
a
LRU Subcache ?
B
b
LRU Subcache ?
CKB
Judge
C
a
C
Hits A, B
c
LRU Subcache ?
Computational Complexity O(1)
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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.

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Experiment Results Better Than Expected


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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.

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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)

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Thank You !

• And Welcome To

http//www.isse.kuis.kyoto-u.ac.jp