Designing Adaptive Replication Schemes for Efficient Content Delivery in Edge Networks

Content availability has become increasingly important for the Internet delivery chain. In order to enhance availability, content distribution network (CDN) providers have invested in hybrid designs that combines resources from datacenter and edge networks. Although, to deliver videos with an outstanding availability and meet the increasing user expectations, hybrid CDNs must enforce strict QoS metrics, like bitrate and latency, through SLA contracts. Adaptive content replication has been seen as a promising way to achieve this goal. However, it remains unclear how to avoid waste of resources when strict SLA contracts must be enforced. In this dissertation, we focus on studying and evaluating adaptive replication schemes for a new generation of hybrid networks, whose resources come from consumers' devices, such as set- top boxes. To this end, we propose (i) Caju, a general-purpose content distribution system, which handles resource allocation in edge networks, and (ii) three novel adaptive replication schemes, AREN, Hermes, and WiseReplica. Extensive simulations with Caju show that our adaptive repli- cation schemes are very efficient and can easily be extended to other CDN architectures. Finally, we provide some guidelines about our ongoing development of Caju in a world-wide testbed deployment.

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Source https://theses.hal.science/tel-00931562
Author Silvestre, Guthemberg
Maintainer CCSD
Last Updated May 7, 2026, 09:50 (UTC)
Created May 7, 2026, 09:50 (UTC)
Identifier tel-00931562
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Large-Scale Distributed Systems and Applications (Regal) ; Laboratoire d'Informatique de Paris 6 (LIP6) ; Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
creator Silvestre, Guthemberg
date 2013-10-18T00:00:00
harvest_object_id ede18817-0c7a-4b0e-b67d-680709cf8427
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-08-20T00:00:00
set_spec type:THESE