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The trend today is to deploy applications and more generally service function chains (SFCs) in public clouds. However, before being deployed in the cloud, chains were deployed on dedicated infrastructures, where software, hardware, and network components were managed by the same entity, making it straightforward to provide robustness guarantees. By moving their services to the cloud, the users lose their control on the infrastructure and hence on the robustness. In this paper, we provide an online algorithm for robust placement of service chains in data centers. Our placement algorithm determines the required number of replicas for each function of the chain and its placement in the data center. Our simulations on large data-center topologies with up to 30 528 nodes show that our algorithm is fast enough such that one can consider robust chain placements in real time even in a very large data center and without the need of prior knowledge on the demand distribution.

Information-centric networking (ICN) is a promising solution for most of Internet applications where the content represents the core of the application. However, the proposed solutions for the ICN architecture are associated with many complexities including pervasive caching in the Internet and incompatibility with legacy IP networks, so the deployment of ICN in real networks is still an open problem. In this paper, we propose a backward-compatible ICN architecture to address the caching issue in particular. The key idea is implementing edge caching in ICN, using a coalition of end clients and edge servers. Our solution can be deployed in IP networks with HTTP requests. We performed a trace-driven simulation for analyzing peer-assisted information-centric networking (PICN) benefits using IRCache and Berkeley trace files. The results show that on average, PICN decreases the latency for 78% and increases the content retrieval speed for 69% compared with a direct download from the original Web servers. When comparing PICN with a solution based on central proxy servers, we show that the hit ratio obtained using a small cache size in each PICN client is almost 14% higher than the hit ratio obtained with a central proxy server using an unlimited cache storage.

This paper introduces source-channel adaptive rate control (SARC), a new congestion control algorithm for layered video transmission in large multicast groups. In order to solve the well-known feedback implosion problem in large multicast groups, we first present a mechanism for filtering RTCP receiver reports sent from receivers to the whole session. The proposed filtering mechanism provides a classification of receivers according to a predefined similarity measure. An end-to-end source and FEC rate control based on this distributed feedback aggregation mechanism coupled with a video layered coding system is then described. The number of layers, their rate, and their levels of protection are adapted dynamically to aggregated feedbacks. The algorithms have been validated with the NS2 network simulator.