1. Improving Proxy Cache Performance: Analysis of Three Replacement Policies John Dilley and Martin Arlitt IEEE internet computing volume3 Nov-Dec 1999 Chun-Fu Kung System Laboratory Dept. of Computer Engineering and Science Yuan-Ze University 2000/8/30

2. Outline Introduction New replacement polices Simulation and results Conclusion

3. Introduction The replacement policy’s goal is to make the best use of available resources, including disk and memory space and network bandwidth. The most popular measure of cache efficiency is object hit rate – the number of times that objects in the cache are referenced. Another important measure of web cache efficiency is byte hit rate – the number of bytes returned directly from the cache as a fraction of the total bytes accessed. CPU and I/O utilization, object retrieval latency.

4. Introduction(cont.) An optimal cache replacement policy would know a document’s future popularity and choose the most advantageous way to use its finite space. To maximize object hit rate, it is better to keep many small popular objects. To maximize object byte hit rate, it is better to keep larger popular objects.

5. GDS-Frequency is a variant of the GDS policy that consider reference frequency in addition to the object size. This policy is optimized to keep smaller, more popular objects in cache to maximize object hit rate. F i is frequency count; S i is the object size. GDS-F

6. LFU-DA The dynamic aging policy prevents previously popular documents from polluting the cache by adding a cache age factor to the reference count. L is a running age factor that starts at 0 and is updated for each replaced (evicted) object. C i is the cost associated with bringing object i into the cache.

7. LRU_H We also implemented a heap-based LRU replacement policy, LRU_H. The heap-based replacement policies let memory usage grow to a high watermark before examining any cache metadata. When the memory usage reaches the high watermark, they evict objects until they reach low watermark.

8. Hit Rate

10. Byte Hit Rate

12. CPU Utilization

13. Conclusion As bandwidth becomes cheaper and more available, caching will play a greater role in reducing access latency and origin server demand. A consistency check for a small object usually takes as long as retrieving the object in the first place. We intend to further analyze this, and we are exploring techniques for improving object consistency in large-scale wide-area distributed systems.