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CAPS: A cloud-assisted approach to handle spikes in peer-to-peer web search

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Abstract

In search engines, popular news events cause huge spikes in the queries related to the events. In this work, we consider the stability issues caused by these spikes in a peer-to-peer web search engine formed using voluntary resources shared by peers. The requirement of providing top-ranking results from a dynamic index distinguishes web search from other classes of peer-to-peer search like object lookup and file search. This makes the traditional methods of load reduction based on caching and replication, proposed for peer-to-peer object lookup and file search, insufficient for containing interest spikes in peer-to-peer web search. We propose transient use of public cloud to maintain the stability of peer-to-peer web search engines during interest spikes. To the best of our knowledge, this is the first work proposing transient use of public cloud to handle spikes in peer-to-peer search. In the proposed architecture, CAPS, the responsibility of handling spiking queries is dynamically offloaded to public clouds during the spike period. The peer bandwidth to be used for transfer of relevant index from peers to cloud is decided considering the impact on other applications in the peer as well as the requirements of the search application. We show that transient use of public cloud can be performed without major adverse impact on the desirable properties of peer-to-peer search like privacy and decentralized control. Experimental evaluation under realistic settings show that cloud-assistance can be used effectively to handle spikes.

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Notes

  1. Yacy peer-to-peer web search engine, http://yacy.net/ Accessed: June 2014 Faroo peer-to-peer web search engine, http://www.faroo.com/ Accessed: June 2014

  2. http://www.faroo.com/hp/p2p/p2p.html Accessed: June 2014

  3. Google status message on query spike, http://twitter.com/#!/google/status/65502190315376640 Accessed: June 2014

  4. http://blogs.bing.com/search/2013/01/17/bing-social-updates-arrive-today-for-every-search-there-is-someone-who-can-help/ http://blogs.bing.com/search/2013/01/17/bing-social-updates-arrive-today-for-every-search-there-is-someone-who-can-help/Accessed: June 2014

  5. http://yacy.net/en/Technology.html Accessed: June 2014

  6. http://www.faroo.com/hp/p2p/faq.html#difference Accessed: June 2014

  7. https://blog.twitter.com/2013/new-tweets-per-second-record-and-how Accessed: June 2014

  8. https://blog.twitter.com/2013/new-tweets-per-second-record-and-how Accessed: June 2014

  9. https://blog.twitter.com/2011/twitter-search-now-3x-faster Accessed: June 2014

  10. Google enterprise blog: http://googleenterprise.blogspot.in/2012/07/introducing-google-cloud-platform.html Accessed: June 2014

  11. Amazon S3 pricing http://aws.amazon.com/s3/pricing/ Accessed: June 2014

  12. http://www.tekgazet.com/google-search-only-1000-maximum-search-results/net/1278.html http://www.tekgazet.com/google-search-only-1000-maximum-search-results/net/1278.html Accessed: June 2014

  13. Youtube official explanation, https://support.google.com/youtube/answer/91449?hl=en-GB Accessed: June 2014

  14. https://support.skype.com/en/faq/FA1417/how-much-bandwidth-does-skype-need Accessed: June 2014

  15. For this, the maximum bandwidth parameter (introduced as b h in Eq. 3) could be set as {total available bandwidth - (minimum/fixed bandwidth requirements of other applications)}.

  16. http://news.cnet.com/AOL-apologizes-for-release-of-user-search-data/2100-1030_3-6102793.html http://news.cnet.com/AOL-apologizes-for-release-of-user-search-data/2100-1030_3-6102793.html Accessed: June 2014

  17. “Tor bridges in the Amazon cloud”: https://cloud.torproject.org/ Accessed: June 2014

  18. http://www.faroo.com/hp/p2p/faq.html#difference Accessed: June 2014

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Acknowledgments

The authors would like to thank Department of Science and Technology (DST), Government of India for supporting the work. We also thank Sriram Kailsam and Balaji S J for discussions on the solution.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (IIT), Madras, Chennai, 600 036, India

    Harisankar Haridas & Janakiram Dharanipragada

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  1. Harisankar Haridas
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  2. Janakiram Dharanipragada
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Correspondence to Harisankar Haridas.

Additional information

An earlier version of this work was presented in a conference [8]. Extensions over the earlier version include an enhanced algorithm for deadline-based switching during spikes and evaluation of the same, study of implications of alternate design choices and solution approaches and study of privacy implications.

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Haridas, H., Dharanipragada, J. CAPS: A cloud-assisted approach to handle spikes in peer-to-peer web search. Peer-to-Peer Netw. Appl. 9, 193–208 (2016). https://doi.org/10.1007/s12083-014-0322-y

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