Web Cache Replacement Policies: Properties, Limitations and Implications

1. Web Cache Replacement Policies: Properties, Limitations and Implications Fabrício Benevenuto, Fernando Duarte, Virgílio Almeida, Jussara Almeida Computer Science Department Federal University of Minas Gerais Brazil

2. Summary • Introduction to Web caching • Motivations and goals • Evaluation methodology • Performance metrics • Workload description • Caching system simulator • Experimental results • Conclusions and future work

3. Web Caching • Dramatic growth of the WWW in terms of content, users, servers and complexity • Web caching is a common strategy used to: • reduce the traffic over Internet • increase server scalability • diminish the latency in the network • Use of caching by the deployment of Web Proxies

4. Servers Proxies Clients Web Caching • Web proxies can be seen as intermediaries of the traffic between the HTTP clients and servers • Nowadays the Web has a hierarchical topology:

5. Web Caching • Cache replacement is one of the issues that a proxy should be able to manage: • As the cache has finite size, when it is full, how does a proxy choose a page to remove from its cache? • A lot of research has been done to address this question and several cache replacement policies can be found in the literature • Key questions: • Is the design of new cache replacement policies needed? • What are the properties that new policies should take advantage of to improve a caching system?

6. Goals Investigate how much a new caching policy could improve cache system performance Explore the main causes of periods of poor and high performance in caching systems

7. Evaluation Methodology • Evaluation of different metrics over time: • Hit Ratio • Percentage of first-timers • Maximum improvement • Entropy • Time intervals of 1, 10 and 100 minutes • Use of real workloads

8. Performance Metric: Hit Ratio • Hit ratio is the percentage of requests satisfied by the cache • It is most general metric used to evaluate the effectiveness of a caching policy • Measuring hit ratio over time to detect periods of variations of performance

9. Performance Metric: Percentage of First-Timers • Caching policies cannot satisfy first-timers • the first-timer has never been requested in the past • First-timer is the first request for an object of the trace.

10. Performance Metric: Maximum Improvement • We evaluate the maximum hit ratio a new caching policy can improve over the simple LRU policy • The maximum improvement MI is defined as: • Maximum improvement over LRU:

11. Performance Metric: Entropy • Taking n distinct objects with probability pi of occurrence, the entropy H(X) of a request stream is calculated as: • Entropy measures the concentration of popularity of a request stream • The higher the value of the entropy, the lower the concentration of popularity • Caching policies should keep objects with high probability of being referenced in the near future

12. Performance Metric: Entropy • Entropy depends on the number of distinct objects • Use of the normalized entropy HN: • Investigate the influence of popularity on caching performance

13. Experiment Setup • Real traces from proxy caches located at two points of the Web topology: • Closer to clients: Federal University of Minas Gerais (UFMG) • Closer to servers: National Laboratory for Applied Network Research (NLANR) • Cache Size: 10% of the number of distinct objects • Replacement caching policy: Simple LRU

14. Workload Description • Traces used • Cache warming: University 1, NLANR 1 • Performance evaluation: University 2, NLANR 2 • Higher concentration of popularity on university traces (lower entropy) • Larger fraction of different objects in the NLANR traces, what diminish significantly the caching performance

15. Experimental Results: Hit Ratio proxy closer to clients proxy closer to servers • Higher hit ratio for University trace • Strong variation along the time • What are the factors that causes the variations on hit ratio?

16. Experimental Results: Percentage of First-Timers proxy closer to clients proxy closer to servers • Smaller % of first-timers at the proxy closer to clients • Correlation coefficient between hit ratio and the percentage of first-timers: • -0.857 for the NLANR and -0.962 for the university • Caching policies cannot satisfy first-timers, the most important factor for poor and good performance in the analyzed traces

17. Experimental Results: Entropy proxy closer to clients proxy closer to servers • Proxy closer to clients: lower entropy → higher concentration of popularity • LRU policy does not take advantage of all locality of reference • Correlation coefficient between hit ratio and entropy: • -0.787 for the NLANR and -0.453 for the university • If we had a caching policy able to filter all the locality (entropy = 1), how much could hit ratio be improved?

18. Experimental Results: Maximum Improvement proxy closer to clients proxy closer to servers • The hit ratio cannot be significantly improved for the trace closer to clients • High number of first-timers diminishing the hit ratio • Improving caching performance • Reorganization of the hierarchy of caches (cache placement) • Caching system able to deal with the first-timers

19. Conclusions and Future Work • Summary of main findings • Strong variation of hit ratio along the time • High number of first-timers (higher close to servers) • Main cause of low hit ratio • LRU policy is not able to filter the entire locality of a stream • Small correlation with hit ratio • The maximum improvement we could obtain over LRU: • less than 5 percent closer to clients • In average 25 percent closer to servers • Results suggest reorganization of cache topology and a caching system able to deal with the higher number of first-timers • Future work • Cache placement: find the optimal cache organization in order to improve the overall system performance • Auto-adaptive cache system able to minimize periods of poor performance

20. Questions? Fabricio Benevenuto, Fernando Duarte, Virgilio Almeida, Jussara Almeida {fabricio, fernando, virgilio, jussara}@dcc.ufmg.br