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A fundamental Software-as-a-Service (SaaS) characteristic in Cloud Computing is to be application-specific; depending on the application, Cloud Providers (CPs) restrict data formats and attributes allowed into their servers via a data validation process. An ill-defined data validation process may directly impact both security (e.g. application failure, legal issues) and accounting and charging (e.g. trusting metadata in file headers). Therefore, this paper investigates, evaluates (by means of tests), and discusses data validation processes of popular CPs. A proof of concept system was thus built, implementing encoders carefully crafted to circumvent data validation processes, ultimately demonstrating how large amounts of unaccounted, arbitrary data can be stored into CPs., 8 pages, ISBN 978-1-4673-5229-1

Part 1: Video Streaming; International audience; Peer-to-peer (P2P) has become a popular mechanism for video distribution over the Internet, by allowing users to collaborate on locating and exchanging video blocks. The approach LiveShift supports further collaboration by enabling storage and a later redistribution of received blocks, thus, enabling time shifting and video-on-demand in an integrated manner. Video blocks, however, are not always downloaded quickly enough to be played back without interruptions. In such situations, the playback policy defines whether peers (a) stall the playback, waiting for blocks to be found and downloaded, or (b) skip them, losing information. Thus, for the fist time this paper investigates in a reproducible manner playback policies for P2P video streaming systems. A survey on currently-used playback policies shows that existing playback policies, required by any streaming system, have been defined almost arbitrarily, with a minimal scientific methodology applied. Based on this survey and on major characteristics of video streaming, a set of five distinct playback policies is formalized and implemented in LiveShift. Comparative evaluations outline the behavior of those policies under both under- and over-provisioned networks with respect to the playback lag experienced by users, the share of skipped blocks, and the share of sessions that fail. Finally, playback policies with most suitable characteristics for either live or on-demand scenarios are derived.

The paradigm shift from centralized services into hundreds of thousands of decentralized services changes traffic directions and patterns in the Internet. In that light, overlay providers form logical communication topologies, which effect the underlying telcos' as well as Internet Service Providers' (ISP) traffic and network management. Thus, Economic Traffic Management (ETM) principles deal with incentives, decentralized traffic generation, and modern applications, such as integrated Video-on-Demand and live streaming approaches. Therefore, this work overviews the very recent work on ETM mechanisms, especially driven by the SmoothIT project, and on LiveShift, combining efficiently architectural and system demands for an incentive-driven application management. Furthermore, this section describes in examples, provides evidences, and concludes that ETM and LiveShift can pave an efficient path to modern application support of distributed overlay traffic management, which benefits telcos, ISPs, overlay providers, and end users, if all or parts of them see the right incentives to cooperate, behave well, or interact.

The popularity of video sharing over the Internet has increased significantly. High traffic generated by such applications at the source can be better distributed using a peer-to-peer (P2P) overlay. Unlike most P2P systems, LiveShift combines both live and on-demand video streaming — while video is transmitted through the peer-to-peer network in a live fashion, all peers participate in distributed storage. This adds the ability to replay time-shifted streams from other peers in a distributed and scalable manner. This paper describes an adaptive fully-distributed mesh-pull protocol that supports the envisioned use case and a set of policies that enable efficient usage of resources, discussing interesting trade-offs encountered. User-focused evaluation results, including both channel switching and time shifting behavior, show that the proposed system provides good quality of experience for most users, in terms of infrequent stalling, low playback lag, and a small proportion of skipped blocks in all the scenarios studied, even in presence of churn.

Trackers are used in peer-to-peer (P2P) networks for provider discovery, that is, mapping resources to potential providers. Centralized trackers, e.g., as in the original BitTorrent protocol, do not benefit from P2P properties, such as no single point of failure, scalability, and load balancing. Decentralized mechanisms have thus been proposed, based on distributed hash tables (DHTs) and gossiping, such as BitTorrent's Peer Exchange (PEX). While DHT-based trackers suffer from load balancing problems, gossip-based ones cannot deliver new mappings quickly. This paper presents B-Tracker, a fully-distributed, pull-based tracker. B-Tracker extends DHT functionality by distributing the tracker load among all providers in a swarm. Bloom filters are used to avoid redundant mappings to be transmitted. This results in the important properties of load balancing and scalability, while adding the ability for peers to fetch new mappings instantly. B-Tracker shows, through simulations, improved load balancing and better efficiency when compared to pure DHTs and PEX.