AÇAI: Ascent Similarity Caching with Approximate Indexes
| Autor: | Tareq SI SALEM, Neglia, G., Carra, D. |
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| Přispěvatelé: | Université Côte d'Azur (UCA), Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Verona (UNIVR), Si Salem, Tareq, Università degli studi di Verona = University of Verona (UNIVR) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | The 33rd International Teletraffic Congress (ITC 33) The 33rd International Teletraffic Congress (ITC 33), Aug 2021, Avignon (virtual), France Proceedings of the 33rd International Teletraffic Congress (ITC 33) Proceedings of the 33rd International Teletraffic Congress (ITC 33), Aug 2021, Avignon (virtual), France Scopus-Elsevier ITC 2021-33rd International Teletraffic Congress ITC 2021-33rd International Teletraffic Congress, Aug 2021, Avignon (virtual), France |
| Popis: | International audience; Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present AÇAI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity. |
| Databáze: | OpenAIRE |
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