least popularity-per-byte replacement algorithm for a proxy cache kyungbaek kim and daeyeon park....
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![Page 1: Least Popularity-per-Byte Replacement Algorithm for a Proxy Cache Kyungbaek Kim and Daeyeon Park. Korea Advances Institute of Science and Technology (KAIST)](https://reader036.vdocuments.net/reader036/viewer/2022062516/56649d795503460f94a5d2c1/html5/thumbnails/1.jpg)
Least Popularity-per-Byte Replacement Algorithm for a
Proxy Cache
Kyungbaek Kim and Daeyeon Park.
Korea Advances Institute of Science and Technology (KAIST)
Eighth International Conference on Parallel and Distributed Systems
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Outline
Introduction Related work Least popularity-per-byte replacement
algorithm Performance evaluation Conclusion
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Introduction
The correlation between the on-line fashion parameters and the object popularity in the proxy cache are weaken because of efficient client caches.
It use the long-term measurements of request frequency as popularity value in this paper.
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Least popularity-per-byte replacement algorithm
(LPPB-R) It is a function-based algorithm. The function of the LPPB-R is to make
the popularity per byte of the outgoing objects to be minimum.
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Least popularity-per-byte replacement algorithm
(LPPB-R) (cont.) How to set the popularity value
determines the performance of this LPPB-R algorithm? Using the reference count directly. Reference count as the power term
of the impact factor
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Some other consideration in LPPB-R algorithm
Using the multi queues to manage objects to decrease the complexity of calculation.
It consider the problem of cache pollution.
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Related work
The classification of replacement algorithm Traditional
LRU, LFU and FIFO Key-based
LFF and LOG2SIZE Function-based
GDS, Hybrid, LRV, SA-LRU
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Least popularity-per-byte replacement algorithm
The overview of LPPB-R
U(j)=P(j)/S(j)
P(j): the popularity value of object j
S(j): the size of object j
U(j): the popularity value per byte
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Getting the popularity value
Two model to get the popularity value P(j)=R(j)/T
R(j): the reference count of j
T: total requests through the proxy
cache P(j)=1/(ß)R(j) , (0<ß<1)
ß: impact factor
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Managing the objects
The LPPB-R has terrible overhead to calculate the utilization values. The operation needs O(k) time. (k is the object number in the cache)
It use multi queues to decrease the order of complexity of calculation.
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Multi queues
The ith queue manages the objects whose size is from 2i-1 to 2i-1. Thus, there will be
different queues of objects. Where M is the cache size.
The objects in each queue i are maintained as a separate LFU list.
Decreasing the order of complexity from O(k) to .
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Multi Queues (cont.)
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Avoiding the cache pollution phenomenon
It use LRU list for each LFU list to avoid the cache pollution.
Checking the LRU list periodically.
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Avoiding the cache pollution phenomenon (cont.)
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Performance evaluation
The traces are from pb and bo2 proxy server of NLANR.
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Performance metrics and algorithms
It consider three aspects of web caching benefits
hit rate, byte rate and reduced latency It compare the performance of LPPB-R
with LRU, LFU, LOG2SIZE, and SA-LRU.
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Hit rate in bo2 server
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Hit rate in pb server
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Byte hit rate in bo2 server
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Byte hit rate in pb server
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Reduced latency in bo2 server
![Page 22: Least Popularity-per-Byte Replacement Algorithm for a Proxy Cache Kyungbaek Kim and Daeyeon Park. Korea Advances Institute of Science and Technology (KAIST)](https://reader036.vdocuments.net/reader036/viewer/2022062516/56649d795503460f94a5d2c1/html5/thumbnails/22.jpg)
Reduced latency in pb server
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Conclusion
If the ß be set to the range from 0.3 to 0.5, LPPB-R will achieves the best hit rate.
On the other hand, closer to zero the ß is, better the performance of the cache is in the byte hit rate and reduced latency.