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

Summary

• Introduction to Web caching• Motivations and goals• Evaluation methodology

– Performance metrics– Workload description– Caching system simulator

• Experimental results• Conclusions and future work

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

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:

Clients

Proxies

Servers

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?

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

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

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

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.

requestsofnumber

sfirstTimerofnumbersfirstTimer %

• We evaluate the maximum hit ratio a new caching policy can improve over the simple LRU policy

Performance Metric: Maximum Improvement

oLRUHitRati

oLRUHitRatisFirstTimerMILRU %

%%1

• The maximum improvement MI is defined as:

HitRatiosFirstTimerMI %%1

•Maximum improvement over LRU:

Performance Metric: Entropy

• 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

• Taking n distinct objects with probability pi of occurrence, the entropy H(X) of a request stream is calculated as:

n

iii ppXH

12log)(

Performance Metric: Entropy

• Use of the normalized entropy HN:

N

XHH N

2log

)(

n

iii ppXH

12log)(

• Entropy depends on the number of distinct objects

• Investigate the influence of popularity on caching performance

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

Workload DescriptionName University 1 University 2 NLANR 1 NLANR 2

start date 01-10-2004 01-12-2004 01-18-2005 01-20-2005

# days 2 10 2 11

# requests 1,004,747 3,459,549 1,207,075 3,427,391

distinct objects 299,367 623,164 891,906 2,350,215

normalized entropy 0.8532 0.8268 0.9482 0.9329

• 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

Experimental Results: Hit Ratio

• Higher hit ratio for University trace

• Strong variation along the time

• What are the factors that causes the variations on hit ratio?

proxy closer to clients proxy closer to servers

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Experimental Results: Percentage of First-Timers

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• 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

proxy closer to clients proxy closer to servers

Experimental Results: Entropy

• 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?

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Experimental Results: Maximum Improvement

• 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

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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

Questions?

Fabricio Benevenuto, Fernando Duarte,

Virgilio Almeida, Jussara Almeida

{fabricio, fernando, virgilio, jussara}@dcc.ufmg.br