Evaluating Memento Service Optimizations

05/31/2019 ∙ by Martin Klein, et al. ∙ Los Alamos National Laboratory 0

Services and applications based on the Memento Aggregator can suffer from slow response times due to the federated search across web archives performed by the Memento infrastructure. In an effort to decrease the response times, we established a cache system and experimented with machine learning models to predict archival holdings. We reported on the experimental results in previous work and can now, after these optimizations have been in production for two years, evaluate their efficiency, based on long-term log data. During our investigation we find that the cache is very effective with a 70-80 rate for human-driven services. The machine learning prediction operates at an acceptable average recall level of 0.727 but our results also show that a more frequent retraining of the models is needed to further improve prediction accuracy.



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1 Introduction

Since Memento was standardized in [4], a variety of services have emerged based on the protocol. Examples are:

(a) Previous implementation
(b) Current, optimized implementation
Figure 1: Memento Aggregator simplified structural view

While these services allow users/machines to perform federated searches across more than publicly available web archives, this functionality comes with challenges. In particular, with the number of available web archives growing, sending a request to each and every one of them for every single incoming Memento service request is not feasible. Particularly, overall response times and the load on the Memento as well as the individual web archive infrastructure are our major concerns. Figure 0(a) shows a simplified view of the federated search behind the Memento services. The Memento Aggregator infrastructure (URI-G) receives a request with an original URI (URI-R) and a datetime in the past. The Aggregator now contacts all web archives and asks for a Memento of the URI-R created as close as possible to the specified datetime. The Aggregator receives URIs of Mementos (URI-Ms) from all archives that in fact have an archived copy available. This service therefore is only as fast as the slowest responding archive and, maybe more importantly, each of the archives responds to all requests, even if they do not have Mementos of the requested URI-R.

In order to optimize this federated search, we first established a cache system. Based on its resulting log data we conducted a number of experiments to investigate the feasibility of simple machine learning classifiers to predict whether a web archive has Mementos of a requested URI-R

[1]. The results were encouraging and hence we put the machine learning prediction process into production as well. Figure 0(b) shows a simplified view of the federated search process as it has been in production since . The Memento Aggregator infrastructure receives a request as shown in Figure 0(a) but now it first consults the cache. If a request for the same URI-R has been served recently, its data is available in the cache and will therefore be served from cache (cache hit). This significantly decreases the response time of the services. In case of a cache miss - no entry for the URI-R is found in the cache - the machine learning based process is started. As described in [1], it takes various simple characteristics of the URI-R into account (length, number of tokens, top level domain, etc.) and predicts the archives in which Mementos are available. The Memento Aggregator infrastructure then contacts only these predicted archives and receives URI-Ms from those that indeed have Mementos, which it eventually presents to the original requester. As a last step, the Aggregator contacts all other web archives not predicted by the machine learning process and obtains URI-Ms from those that have Mementos and were missed by our prediction. While this step still involves all archives and hence suffers from the same latency mentioned above, the requests are sent in batch mode and not optimized for speed, hence this process is much more friendly to the queried web archives. As a result of this step, the Aggregator eventually has the complete picture of available Mementos and populates the cache with this data. In practice, this means that responses orchestrated by the machine learning prediction are accurate but may not be complete, where all responses from cache reflect accurate and complete data.

In previous work [1] we outlined the theoretical foundation of this process and presented initial experimental results that encouraged us to implement the concept presented here. In this work, we seek to evaluate both the cache and the machine learning based prediction. After almost two years in production, we can utilize the log files to offer insights into the following research questions:

  1. How effective is the cache in terms of hit/miss rate? Does the ratio vary for different Memento services? Is the chosen cache freshness period appropriate?

  2. How effective is the machine learning process in practice, compared to the baseline established in our previous work? How does the machine learning prediction perform in terms of keeping the number of false positives low? Do the models need to be retrained and if so how often?

2 Data collection

To conduct our evaluation on the performance of the cache and the machine learning prediction, we studied the log files of the above outlined four Memento services. For the machine learning evaluation we extracted data recorded between July and October . This time frame is dictated by the last time we retrained the machine learning models (in July ) and by the time we conducted the experiment (starting in October ). In total, the log files for the machine learning evaluation contain entries.

While the Memento services aggregate data from more than publicly available web archives, we limit our study to the archives listed in Table 1. These archives are all natively Memento-compliant and we have collected a sufficient amount of usage data to train a machine learning prediction model for them. The reason the Internet Archive (IA), the world’s oldest and largest web archive, is not listed is because we do not maintain a prediction model for the IA. Its index is so vast and so rapidly changing that we foresaw no model being able to accurately predict their holdings. Hence the IA is excluded from our evaluations of recall and false positives.

Acronym Web Archive Name
archive.is Archive.is
archive-it Archive-It
ba Bibliotheca Alexandrina Web Archive
blarchive UK Web Archive
bsb Bayerische Staatsbibliothek
gcwa Canadian Archive
loc Library of Congress
nli National Library of Ireland
perma Perma.cc
proni The Public Record Office of Northern Ireland
pt Arquivo.pt
swa Stanford Web Archive
uknatarch UK National Archives Web Archive
Table 1: Evaluated web archives and their acronyms

3 Cache Evaluation

There are many ways to implement [3] and optimize [2] cache systems but since our cache was for requests against web archive holdings rather than traditional web caches, we questioned whether these established methods were applicable. We therefore decided in favor of simplicity and implemented a binary cache for which each request either results in a cache hit or a cache miss. We further decided to keep the cache records fresh for a period of days. After a record has been in the cache for days, it is labeled “stale” and a request against the record results in a cache miss. A record is stale for another days before it ultimately is deleted.

Figure 2: Cache performance over four Memento services

We were interested in investigating the performance of the cache over time in terms of cache hit vs. miss rate and the validity of the -day freshness period. Figure 2 shows the cache performance of four Memento services: TimeTravel, browser extension, APIs, and Redirect, each of which is represented by a bar from left to right. The blue portion of each bar indicates the cache hit ratio for the respective service, cache miss ratios are shown in red, and stale ratios in green. The overall height of a bar represents the total number of requests we have seen against the respective Memento service during the observed period of time. We can make a few observations from Figure 2. First, we see a high cache hit ratio for the TimeTravel service and the browser extension with and respectively. The API service with and especially the Redirect service with show much lower numbers. This makes intuitive sense when considering the nature of the services. The former two are services aimed at human users and hence the requests are likely heavily influenced by popularity of the requested URIs. It is therefore reasonable to assume that URIs are being requested multiple times and hence these services show a higher cache hit rate. The latter two services, on the other hand, are predominantly used by machines for batch requests, so likely include more random and not necessarily popular URIs, hence the lower hit rate. This seems especially true for the Redirect service with a miss rate of . It is interesting to observe a similar miss rate between the TimeTravel, browser extension, and API services but a significant stale rate for the APIs (). We speculate that this is due to machines sending repeated requests of the same URIs, as it is frequently done in a variety of web archiving studies, and the experiments running longer than the cache entries are fresh.

We draw two main conclusions from Figure 2. First, the cache works. On average, across all four services, of requests are being served from cache, saving millions of requests to public web archives. Second, while the -day cache freshness period seems suitable for most services, we see reason to increase the period for the API services in order to lower the ratio of stale requests.

4 Machine Learning Evaluation

The experiment outlined in our previous work [1] resulted in an observed recall value of . We take this value as the baseline and are motivated to investigate whether this baseline held true over time. Figure 3 visualizes all web archives and their computed average recall values666We compute recall values the same way as was done in [1]: , where represents true positives and false negatives. over time, based on our log files. The figure further shows a dashed red horizontal line that intercepts with the y-axis at (the baseline) and a solid blue line at , which is the average recall over all archives, based on our here computed data. We can observe a recall value for out of the web archives above or just below the baseline. The UK National Archives Web Archive has a perfect recall value of and the UK Web Archive as well as Perma.cc are almost perfect with a recall of . These results are very encouraging and mean that our machine learning process is very accurate in predicting which archives hold a copy of a requested URI. However, Figure 3 also shows five archives with a recall value below the baseline and below our average. In particular, the recall values for Archive-It and the Library of Congress Web Archive are rather low with and respectively. These numbers confirm that our machine learning process is not operating on a satisfying level for all web archives. One possible explanation for the low recall is that the web archive is very dynamic, meaning it frequently adds and removes entries from its index. Consequently, the data the machine learning process was trained on quickly does not adequately represent the state of the archive anymore and hence the prediction mechanism results in misguided requests. Our conclusion to this observation is the need for more frequent retraining processes for the archives with low recall numbers.

Figure 3: Recall of web archives

A different way to evaluate the performance of our machine learning prediction mechanism is to look at the number of false positives per archive. A false positive in this context occurs when the machine learning process predicts that a web archive has an archived copy of a requested URI where, in reality, it does not. False positives therefore result in requests that we seek to avoid as the contacted web archives return an empty result set. Figure 4 displays the number of false positives for the archives. Immediately apparent is that the number for the Bibliotheca Alexandrina Web Archive is rather high (). After consulting with staff from the archive, we were able to confirm that the archive was undergoing a lengthy infrastructure reorganization that frequently resulted in the web archive platform being unresponsive to requests from the Memento services, explaining the high number. Other archives such as Archive.is (), the UK Web Archive (), and Perma.cc () also show high false positive numbers. Possible reasons are the dynamic nature of a web archive such as Archive.is as mentioned earlier, technical issues that for a period of time resulted in HTTP “ - File not found” errors (UK Web Archive), and simply an imperfect trained machine learning model, despite the high recall numbers (Perma.cc). The numbers shown in Figure 4 further confirm our above mentioned conclusion for the need of a regular retraining of the machine learning models to accommodate for changes in the archives that cause false positives.

5 Dynamic Web Archives

As mentioned, one possible explanation for low recall and false positives is the dynamic nature of web archives. Specifically, web archives quickly growing by continuously adding Mementos to their index or frequently deleting or preventing access to previously available Mementos. The former may be due to an institution’s or even a country’s archiving policies and the latter may be caused by take-down requests or other legal disputes. We investigated these dynamics, based on our log files, and how it affects our machine learning based prediction method. To this extent, we considered URI-Rs that have been requested multiple times from the Memento services and their requests (cache misses) caused the Aggregator every time to obtain the full picture of their Mementos distributed across various web archives.

Figure 4: False positives of web archives

Our log files revealed a total of such instances and we were able to determine that in of all cases the result set of archives with available Mementos had not changed for multiple requests of the same URI-R. Changes in the result set that do occur are split between cases () where archives were added and cases () where archives were removed. The IA leads the statistics; it was added to a result set more than thousand times and removed from one more than thousand times. Figure 5 displays the amount of times archives that are subject to our machine learning prediction were added/removed from a result set. The figure confirms the dynamic character of some of the archives. In particular, Archive.is, Archive-It, and the Library of Congress were added to and removed from numerous result sets. The large number of instances where the Bibliotheca Alexandrina Web Archive was added and removed can be attributed to a large number of transient errors the Memento services observed, likely due to the infrastructure restructuring mentioned above. The results presented in Figure 5 further strengthen our argument that the machine learning retraining is essential to keep up with the evolution of the archiving landscape and continuously serve results with high recall and as few as possible false positives.

Figure 5: Instances of archives being added/removed

6 Conclusion

In this study we report on an evaluation of the effectiveness of our cache system and machine learning prediction - two optimization mechanisms for Memento Aggregator-based services. We find the cache to be very effective, especially for human-driven services, where we observe a cache hit rate of more than . However, there clearly is room for improvement for machine-driven services. We further conclude that the overall, machine learning prediction models operate at an acceptable recall level of

but we also show clear indicators that support the need to retrain our models on a regular basis in order to keep up with the changing web archive landscape and to further lower the false positive rates per archive. Going forward, we are interested in exploring the performance of machine learning models that are based on archival holdings and not on our recorded usage data. In addition, we are experimenting with neural network classifiers for the prediction task and first results are very promising yet too premature to be reported here.


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