A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals
By Róbert Busa-Fekete, Julian Zimmert, András György, Linhai Qiu, Tzu-wei Sung, Hao Shen, Hyomin Choi, Sharmila Subramaniam, Li Xiao
DOI https://doi.org/10.48550/arXiv.2502.02430
Abstract
Web refresh crawling is the problem of keeping a cache of web pages fresh, that is, having the most recent copy available when a page is requested, given a limited bandwidth available to the crawler. Under the assumption that the change and request events, resp., to each web page follow independent Poisson processes, the optimal scheduling policy was derived by Azar et al In this paper, we study an extension of this problem where side information indicating content changes, such as various types of web pings, for example, signals from sitemaps, content delivery networks, etc., is available. Incorporating such side information into the crawling policy is challenging, because (i) the signals can be noisy with false positive events and with missing change events; and (ii) the crawler should achieve a fair performance over web pages regardless of the quality of the side information, which might differ from web page to web page. We propose a scalable crawling algorithm which (i) uses the noisy side information in an optimal way under mild assumptions; (ii) can be deployed without heavy centralized computation; (iii) is able to crawl web pages at a constant total rate without spikes in the total bandwidth usage over any time interval, and automatically adapt to the new optimal solution when the total bandwidth changes without centralized computation. Experiments clearly demonstrate the versatility of our approach.
Summary
The paper tackles the challenge of maintaining up-to-date web pages within a limited bandwidth using a new crawling algorithm. The authors explore an extension of traditional web crawling techniques where additional noisy change-indicating signals (like web pings) are available to inform the crawler about content changes. The proposed scalable algorithm optimally utilizes these noisy signals, does not require centralized computation, and maintains a constant total crawl rate without bandwidth spikes. Experimental results demonstrate the algorithm’s effectiveness.
Introduction
Web refresh crawling involves regularly updating the cached versions of web pages to ensure they reflect the latest content. This is crucial as there are trillions of web pages online, making efficient data management essential. The challenge lies in scheduling refresh events optimally to maximize the chances of serving a fresh page when a request is made, all while being mindful of bandwidth constraints.
The traditional setup assumes that changes and requests are independent and follow Poisson processes. The authors build upon this model by including change-indicating signals—information from web servers about content updates. However, the signals can be noisy, with false positives and missed changes. The goal is to create a crawling policy that fairly handles these imperfections across various web pages.
The authors outline three key contributions:
- Incorporation of Noisy Signals: Extending the crawling problem to include noisy signals and deriving an optimal continuous crawling strategy.
- Decentralized Discrete Strategy: Developing a discrete crawling approach that maintains overall bandwidth usage without spikes and can adapt to changes in available bandwidth.
- Empirical Validation: Providing experimental results that validate the proposed methods and show improvements in performance.
Main Findings
- Noisy Change-Indicating Signals: The analysis reveals that change-indicating signals, despite their noise, are beneficial. The signals allow the crawling algorithm to operate efficiently even when only a fraction of changes are observable.
- Continuous vs. Discrete Policies: The authors derive optimal continuous crawling strategies that minimize the average time until a request is served with a fresh page. They then transition to a discrete policy for practical application, which is computationally simpler and suitable for real-world deployment.
- Scalability: The proposed method can be implemented in a decentralized manner, making it adaptable to the massive scale of the web. The algorithm maintains performance with a constant crawl rate and can adjust to bandwidth changes without centralized computation.
- Empirical Studies: Extensive experiments demonstrate the algorithm’s effectiveness in utilizing noisy CISs and show significant performance improvements over baseline approaches.
Conclusion
In conclusion, the paper presents a scalable crawling algorithm that effectively incorporates noisy change-indicating signals, enhancing the efficiency of web data management within bandwidth constraints. The discrete policies derived from continuous strategies allow for decentralized execution, making the approach suitable for real-world web crawling scenarios, where millions of pages require constant updating. The empirical results validate the effectiveness of the proposed methods, indicating that the approach not only maintains but often improves freshness in crawled pages compared to more traditional methods.
Future Work
The authors suggest future investigations into refining methods for handling delayed change-indicating signals and further optimizing their policies for real-time bandwidth adjustments. They acknowledge the necessity for robust models to incorporate and learn from these signals in a dynamic web environment.