A Survey of Web Cache Replacement Strategies

1. A Survey of Web Cache Replacement Strategies Stefan Podlipnig, Laszlo Boszormenyl University Klagenfurt ACM Computing Surveys, December 2003 Presenter: Junghwan Song 2012.04.25

2. Outline • Introduction • Classification • Recency-based • Frequency-based • Recency/frequency-based • Function-based • Randomized • Discussions • Importance in nowadays • Future research topics • Conclusions

3. Why was caching born? • Web has been growing • Load on the Internet and web servers increase Caching have been introduced

4. Caching effect • Reducing network bandwidth usage • Reducing user- perceived delays • Reducing loads on the origin server • Increasing robustness of web services • Providing a chance to analyze an organization’s usage pattern

5. When cache becomes full.. • To insert new objects, old objects must be removed • Which objects do we select? Cache replacement strategy

6. General cache operation • Cache miss  Cache stores new object • Cache hit  Cache serves requested objects • Cache full  Cache evicts old objects

7. Outline • Introduction • Classification • Recency-based • Frequency-based • Recency/frequency-based • Function-based • Randomized • Discussions • Importance in nowadays • Future research topics • Conclusions

8. Classification factors • Important factors for classification • Recency • Time of since the last reference • Frequency • The number of requests • Size • Modification • Time of since last modification • Expiration time • Time when an object gets stale

9. Classification • Recency-based strategy • Frequency-based strategy • Recency/frequency-based strategy • Function-based strategy • Randomized strategy

10. Recency-based strategy • Recency is a main factor • Based on the temporal locality • Temporal locality: Burst accesses in short time period • There are well-known LRU and extension of it

11. Recency-based schemes • LRU • Remove the least recently used object • LRU-Threshold • Don’t cache when size of new object is exceeds the threshold • SIZE • Remove the biggest one • LRU is used as a tie breaker

12. Recency-based schemes • PSS • Classify objects depending upon their size • Range: 2i-1 ~ 2i-1 • Each class has a separate LRU list • Whenever there is a replacement • Choose largest among the least recently used objects of each class • : Size of the object • : The number of accesses since the last request

13. Characteristics • Pros • Suited when web request streams exhibit temporal locality • Simple to implement and fast • Cons • In general, size is not combined with recency well • Except PSS

14. Frequency-based strategy • Use frequency as a main factor • Based on popularity of web objects • Frequency represents popularity • There are well-known LFU and extension of it

15. Two forms of LFU • Perfect LFU • Count all requests to an object i • Request counts persist across replacement • Represent all requests from the past • Space overhead • In-cache LFU (We assume this) • Count requests to cached objects only • Cannot represent all requests in the past • Less space overhead

16. Frequency-based schemes • LFU • Remove the least frequently used object • LFU-Aging • If avg(all frequency) exceeds certain threshold, all frequency counter/2 • LFU-DA • Each request for object i, calculate following • L is an aging factor, initialized to zero • Smallest Ki-value object is replaced • The value of this object is assigned to L

17. Characteristics • Pros • Valuable in static environments • Popularity does not change over a time period • Cons • Complex to implement • Cache pollution • Old, popular objects don’t be removed • Overcome with aging

18. Recency/frequency-based strategy • Use recency and frequency(+@) • LRU* • If least recently used object’s counter is zero, replace it • Otherwise, decrease its counter and move it to the beginning of list(Most recently used position)

19. Characteristics • Pros • Can take advantages of both recency and frequency • Cons • Additional complexity is added • Simple scheme(ex. LRU*) neglects size

20. Function-based strategy • Use a potentially general function • GD-Size • , where L is a running aging factor • Smallest-value object is selected • HYBRID • cs: time to contact server, bs: bandwidth to server, Wb&Wn: parameters

21. Characteristics • Pros • Can control weighting parameters • Optimization is possible • Consider many factors • Can handle different workload situations • Cons • Choosing appropriate parameters is difficult • Using latency as a factor is danger • Latency changes depending upon time

22. Randomized strategy • Use randomized decisions • RAND • Remove a random object • HARMONIC • Give probability inversely proportional to cost, ci/si(ci: cost to fetch, si: size of object)

23. Characteristics • Pros • Simple to implement • Cons • Hard to evaluate • Results of simulations that is run on the same Web server are slightly different

24. Outline • Introduction • Classification • Recency-based • Frequency-based • Recency/frequency-based • Function-based • Randomized • Discussions • Importance in nowadays • Future research topics • Conclusions

25. Importance in nowadays • Questions on importance of cache replacement strategies • Large cache • Reduction of cacheable traffic • Good-enough algorithms • Alternative models

26. Large cache • The capacity of caches grows steadily • Replacement strategies are not seen as a limiting factor • Working set for clients<<Cached objects[1] • Basic LRU is sufficient • But, cacheable object will grow in future • Multimedia files [1]. Web caching and replication, Rabinovich and Spatscheck [2002]

27. Reduction of cacheable traffic • Non-cacheable data is of a significant percentage of the total data • Around 40% of all requests • Overcome with active cache, server accelerator • Active cache: Let proxy cache applets • Server accelerator: Provide an API which control cached data explicitly

28. Good-enough algorithms • There are already many algorithms that are considered as good enough • Give good results in different evaluations • PSS, etc • Some function-based strategies with weighting parameters can be optimized

29. Alternative models • Static caching • Content of the cache is updated periodically • Popularity of objects is determined in prior period • Give TTL to cached objects • Simple to implement • Large TTL causes large cache storage usage

30. Future research topics • Adaptive replacement • Coordinated replacement • Replacement + coherence • Multimedia cache replacement • Differentiated cache replacement

31. Adaptive replacement • Change replacement strategies(or parameters in function-based) depending on actual workload • Strong temporal locality workload LRU • Workload with no request fluctuations LFU • Problems • Need smooth change of strategies • Wrong changes make performance worse

32. Coordinated replacement • Make decision with considering other caches’ status • Cooperative caching • There are some papers of cooperative caching in ICN • WAVE(2012)[2] • Age-based cooperative caching(2012)[3] [2] WAVE: Popularity-based and Collaborative In-network Caching for Content-Oriented Networks, K Cho et al, 2012 [3] Age-based Cooperative Caching in Information-Centric Networks, Z minget al, 2012

33. Multimedia cache replacement • Multimedia caching research will be dominated by video • Videos are the biggest objects • How to cache this big file • Chunks, partial caching, quality-adjust, etc

34. Differentiated cache replacement • Support QoS in caching • Ex) Classify caches into different classes • Two kindsof differentiation • Using information given by servers • Handled by only proxy • Add some overhead • How to simplify?

35. Conclusions • Give an exhaustive survey of various cache replacement strategies • Show that there are future research areas of cache replacement strategies

36. APPENDiX

37. Large cache • A cache’s handleable rate: 1000 req/sec • Average size of objects: 10KB • Request rate of above: 82Mbps • 60% are cacheable, 40% hit rate: 16.4Mbps (2.05 MBps) • Disk capacity 200 GB: 21 millions objects • Working set’s mMaximum stack distance: 15 millions