1 / 15

Evaluating Content Management Techniques for Web Proxy Caches

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 1999.10.06. Outline. Key workload characteristic

errol
Download Presentation

Evaluating Content Management Techniques for Web Proxy Caches

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating Content Management Techniques for Web Proxy Caches Martin Arlitt, Ludmila Cherkasova, John Dilley, Rich Friedrich and Tai JinHewlett-Packard Laboratories 4th International WWW Caching Workshop 元智大學資訊工程所 系統實驗室 陳桂慧 1999.10.06

  2. Outline • Key workload characteristic • Experimental design • Simulation results • Conclusion

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

  4. 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

  5. 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

  6. 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

  7. Hybrid Algorithm (HYB) • Motivated by Bolot and Joschka’s algorithm W1rtti + W2 si + (W3 + W4 si)/ti • ti : the time since the document was last referenced • rtti: the time it took to retrieve the document • (clatser(i) + WB/cbwser(i))(nrefi** WN)/ si • nrefi: 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 cbwser(i) and nrefj

  8. Latency Estimation Algorithm (LAT) • clatj = (1-ALPHA) clatj + ALPHA sclat • cbwj = (1-ALPHA) cbwj + ALPHA scbw. • Clatj : estimated latency (time) to open a connection to the server • cbwj : estimated bandwidth of the connection • sclat and scbw : the connection establishment latency and bandwidth for that document are measured • di = clatser(i) + si/cbwser(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

  9. 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

  10. 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)

  11. 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.

  12. Analysis of Virtual Cache Performance • VC0 using GDSF-Hits, VC1 using LFU-DA. 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

  13. Analysis of Virtual Cache Management • VC0 using LFU-DA, VC1 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

  14. 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

  15. 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.

More Related