Evaluating Content Management Techniques for Web Proxy Caches Martin Arlitt, Ludmila Cherkasova, John Dilley, Rich Friedrich and Tai Jin Hewlett-Packard Laboratories 4th International WWW Caching Workshop 元智大學資訊工程所 系統實驗室 陳桂慧

Outline Key workload characteristic Experimental design Simulation results Conclusion

Key Workload Characteristics Cacheable objects Object set sizes Object sizes Recency of reference Frequency of reference Turnover

Experimental Design Cache size –256 MB, 1 GB, 4 GB, 16 GB, 64 GB, 256 GB and 1TB…... Cache replacement policy –LRU, SIZE, GD-Size, LFU, GDSF, LFU-DA –LAT, HYB Performance metrics –Hit rate –Byte hit rate

Replacement Algorithm (1) Least-Recently-Used (LRU) –replaces the object requested least recently. SIZE –replaces the largest object. LFU –replaces the least frequently used object. GreedyDual-Size (GD-Size) –replaces the object with the lowest utility. –Ki = Ci / Si + L

Replacement Algorithm (2) GreedyDual-Size with Frequency (GDSF) –Ki = Fi * Ci / Si + L Least Frequently Used with Dynamic Aging(LFU- DA) –Ki = Ci * Fi + L

Hybrid Algorithm (HYB) Motivated by Bolot and Joschka’s algorithm W 1 rtt i + W 2 s i + (W 3 + W 4 s i )/t i –t i : the time since the document was last referenced –rtt i : the time it took to retrieve the document (clat ser(i) + W B /cbw ser(i) )(nref i ** W N )/ s i –nref i : the number of references to document i since it last entered the cache –si : the size in bytes of document i –WB and WN : constants that set the relative importance of the variables cbw ser(i) and nref j

Latency Estimation Algorithm (LAT) clat j = (1-ALPHA) clat j + ALPHA s clat cbw j = (1-ALPHA) cbw j + ALPHA s cbw. –Clat j : estimated latency (time) to open a connection to the server –cbw j : estimated bandwidth of the connection –s clat and s cbw : the connection establishment latency and bandwidth for that document are measured di = clat ser(i) + si/cbw ser(i) –ser(i) : the server on which document i resides –si : the document's size –di : LAT selects for replacement the document i with the smallest download time estimate

Comparison of existing replacement policies GD-Size(1) LFU-Aging SIZE LFU GD-Size(P) LRU LFU-Aging GD-Size(P) LRU LFU GD-Size(1) SIZE

Comparison of proposed policies to existing replacement policies GDSF-Hits GD-Size(1) LFU-Aging LFU-DA GD-Size(P) LRU LFU-Aging LFU-DA GD-Size(P) LRU GDSF-Hits GD-Size(1)

Virtual Caches An approach that can focus on both of high hit rates and high byte rate. –each virtual cache (VC) is then managed with its own replacement policy. initially all objects are added to VC 0, replacements from VC i are moved to VC i+1, replacements from VC n-1 are evicted from the cache. all objects that are reaccessed while in the cache (i.e., cache hits) are reinserted in VC 0. –this allows in-demand objects to stay in the cache for a longer period of time.

GDSF-Hits VC-HB-75/25 VC-HB-50/50 VC-HB-25/75 LFU-DA LRU LFU-DA VC-HB-25/75 VC-HB-50/50 VC-HB-75/25 LRU GDSF-Hits Analysis of Virtual Cache Performance –VC 0 using GDSF-Hits, VC 1 using LFU-DA.

Analysis of Virtual Cache Management –VC 0 using LFU-DA, VC 1 using GDSF-Hits. GDSF-Hits VC-HB-25/75 VC-HB-50/50 VC-HB-75/25 LFU-DA LRU LFU-DA VC-HB-25/75 VC-HB-50/50 VC-HB-75/25 GDSF-Hits LRU

Analysis of Virtual Cache Management –effects of VC order on performance VC-BH-25/75 VC-HB-75/25 VC-BH-50/50 VC-HB-50/50 VC-BH-75/25 VC-HB-25/75 VC-HB-25/75 VC-BH-75/25 VC-HB-50/50 VC-BH-50/50 VC-HB-75/25 VC-BH-25/75

Conclusion Size-based policies achieve higher hit rates than other policies. Frequency-based policies are more effective at improving the byte hit rate of a proxy cache. Virtual caches as an approach provide optimal cache performance for multiple metrics simultaneously.