Web Caching Schemes For The Internet

1
Web Caching Schemes For The Internet cont.

• By Jia Wang

2
Topics

• Cache resolution/routing
• Prefetching
• Cache replacement
• Cache coherency
• Other topics

3
Cache Resolution/Routing

• Most Web caching schemes many Web caches
  scattered over the Internet
• Main challenge how to quickly locate the
  appropriate cache
• No necessary location among documents cache
  location
• Unmanageably large cache routing tables

4
Cache Resolution/Routing

• Out-of-date cache routing information leads to
  cache miss
• Minimize the cost of a cache miss ideal cache
  routing algorithm

5
Cache Resolution/Routing

• Common approach caching distribution tree from
  popular servers towards high demand sources
• Resolution via cache routing table/ hash
  functions
• Works well for popular documents

6
Cache Resolution/Routing

• What about less popular documents?

7
Cache Resolution/Routing

• Hit rate on web caches lt 50

8
Cache Routing Table

• Malpani make a group of caches function as one
• Cache is selected arbitrary
• In case of miss use IP multicast (why?)
• Redirection

9
Cache Routing Table
10
(No Transcript)
11
Cache Routing Table

• Advantages
• No bottlenecks
• No single point of failure

12
Cache Routing Table

• Disadvantage
• Overhead
• Solutions?

13
(No Transcript)
14
Cache Routing Table

• Harvest organize caches in hierarchy
• Internet Cache protocol ICP
• In case of miss siblings and upward

15
Cache Routing Table

• Adaptive Web Caching mesh of caches
• Distribution trees are built
• Overlapping multicast groups
• No root node will be overloaded
• For less popular objects long journey

16
(No Transcript)
17
Hashing Function

• Cache Array Routing Protocol CARP
• query-less caching by hash function
• Based on array membership list and URL for
  exact cache location
• Proxy removal reassign 1/n URLs and distribute
  new hash function

18
Prefetching

• Caching documents at proxies improve Web
  performance with limited benefit
• Maximum cache hit rate lt 50

19
Prefetching

• One way to increase hit rate anticipate future
  requests and preload or prefetch

20
Prefetching

• Prefetching must be effective (why?)
• Prefetching can be applied in 3 ways
• Between browser clients and Web Servers
• Between proxies and Web Servers
• Between browser clients and proxies

21
Between browser clients and Web Servers

• Cunha use a collection of Web clients
• How to predict users future Web accesses from
  his past Web accesses
• Two types of users net surfer and conservative

22
(No Transcript)
23
(No Transcript)
24
Between browser clients and Web Servers

• Conservative easy to guess which document will
  access next
• Prefetching is well paid off
• Net surfer all documents have equal probability
  of being accessed
• Price to be paid in terms of extra bandwidth is
  too high

25
Between proxies and Web Servers

• Markatos
• Web servers push popular documents to Web proxies
  (top-10)
• Web proxies push popular documents to Web clients
• Web servers can anticipate gt 40 clients request
• Requires cooperation from Web servers

26
(No Transcript)
27
(No Transcript)
28
Between proxies and Web Servers

• Performance
• Top-10 manages to prefetch (up to) 60 of future
  requests
• less than 20 corresponding increase in traffic

29
Between browser clients and proxies

• Fan reduce latency by prefetching between
  caching proxies and browsers
• Relies on the proxy predict which cached
  documents a user might reference next
• Use idle time between user requests to push
  documents to the user
• Reduce client latency by 23

30
Prefetching - summary

• First two approaches increase WAN traffic
• Last approach affects traffic over modems/LANs

31
Cache placement/replacement

• Document placement/replacement algorithm can
  yield high hit rate
• Cache placement not been well studied
• Cache replacement can be classified into 3
  categories

32
Cache placement/replacement

• Traditional policies
• Key-based policies
• Cost-based policies

33
Cache replacement traditional policies

• Least Recently Used
• LRU
• Least Frequently Used
• LFU
• Pitkow/Recker LRU
• except if all objects are
• accessed within the
• same day

34
Cache replacement key-based policies

• Size evicts the
• largest object (why?)
• LRU-MIN biased in favor the smaller objects
• Evicts the LRU object which has size gt S, S/2,
  S/4 etc.

35
Cache replacement key-based policies

• LRU-Threshold LRU but objects which have size gt
  Threshold are never cached
• Lowest Latency First

36
Cache replacement cost-based policies

• GreedyDual-Size associates a cost with each
  object
• Evicts object with lowest cost/size
• Server-assisted models the value of caching an
  object in terms of its fetching cost, size and
  cache prices
• Evicts object with lowest value

37
Cache coherency

• Caches provides lower access latency
• Side effect stale pages
• Every Web cache must update pages in its cache

38
Cache coherency

• HTTP commands that assist Web proxies in
  maintaining cache coherence
• HTTP GET
• Conditional GET HTTP GET combined with the
  header IF-Modified-Since
• Pragmano-cache
• Last-Modifieddate

39
Cache coherence mechanisms

• Current cache coherency schemes provides two
  types of consistency
• Strong cache
• consistency
• Weak cache
• consistency

40
Strong cache consistency

• Client validation polling every time
• Cached resources are potentially out-of-date
• If-Modified-Since with each access to proxy
• Many 304 responses

41
Strong cache consistency

• Server invalidation
• Upon detecting a resource change, send
  invalidation message
• Server must keep track
• of lists of clients
• The lists can become
• out-of-date

42
Weak cache consistency

• Piggyback invalidation three invalidation
  mechanisms are proposed

43
Weak cache consistency

• The Piggyback Cache Validation on every
  communication between proxy to server, the proxy
  piggybacks a list of cached resources for
  validation

44
Weak cache consistency

• The Piggyback Server Invalidation on every
  communication between server to proxy, the server
  piggybacks a list of resources that have changed
  since the last access

45
Weak cache consistency

• Combination of PSI and PCV depends on the time
  since the proxy last requested invalidation
• If the time is small PSI
• For longer gaps PCV

46
More topics

• Load balancing
• Hot-spot
• Dynamic data caching
• Active cache

47
Conclusion

• Web service becomes more popular
• More network congestion
• More server overloading
• Web caching one of the effective techniques
• Open problems proxy placement, cache routing,
  dynamic data caching, fault tolerance, security
  etc.