Serveur de stockage en réseau

Résumé

thumb|Un NAS 4-baies, c'est-à-dire pouvant héberger jusqu'à quatre disques durs. Un serveur de stockage en réseau, également appelé stockage en réseau NAS, boîtier de stockage en réseau ou plus simplement NAS (de l'anglais Network Attached Storage), est un serveur de fichiers autonome, relié à un réseau, dont la principale fonction est le stockage de données en un volume centralisé pour des clients réseau hétérogènes. Historique Dès 1983, le serveur NetWare de Novell inclut le NCP. En 1984, Sun Microsystems développe qui permet le partage de fichiers à travers un réseau. Il est bientôt suivi par le LAN Manager de Microsoft et Novell. 3com (pour Windows) et (pour Unix) sont les premiers à commercialiser les NAS dédiés au début des années 1990. Quelques ingénieurs d'Auspex créent le intégré qui fonctionne avec des clients Windows et Unix simultanément. NetApp et d'EMC occupent alors le marché. Fonctionnement Comme un serveur de fichiers, le stockage en résea

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Adaptive video delivery using semantics

Olivier Steiger

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications.

EPFL2005

Chargement

Secure Storage with Replication and Transparent Deduplication

Iraklis Leontiadis

We seek to answer the following question: To what extent can we deduplicate replicated storage ? To answer this question, we design ReDup, a secure storage system that provides users with strong integrity, reliability, and transparency guarantees about data that is outsourced at cloud storage providers. Users store multiple replicas of their data at different storage servers, and the data at each storage server is deduplicated across users. Remote data integrity mechanisms are used to check the integrity of replicas. We consider a strong adversarial model, in which collusions are allowed between storage servers and also between storage servers and dishonest users of the system. A cloud storage provider (CSP) could store less replicas than agreed upon by contract, unbeknownst to honest users. ReDup defends against such adversaries by making replica generation to be time consuming so that a dishonest CSP cannot generate replicas on the fly when challenged by the users. In addition, ReDup employs transparent deduplication, which means that users get a proof attesting the deduplication level used for their files at each replica server, and thus are able to benefit from the storage savings provided by deduplication. The proof is obtained by aggregating individual proofs from replica servers, and has a constant size regardless of the number of replica servers. Our solution scales better than state of the art and is provably secure under standard assumptions.

2018

Chargement

Lightweight cryptography was developed in response to the increasing need to secure devices for the Internet of Things. After significant research effort, many new block ciphers have been designed targeting lightweight settings, optimizing efficiency metrics which conventional block ciphers did not. However, block ciphers must be used in modes of operation to achieve more advanced security goals such as data confidentiality and authenticity, a research area given relatively little attention in the lightweight setting. We introduce a new authenticated encryption (AE) mode of operation, SUNDAE, specially targeted for constrained environments. SUNDAE is smaller than other known lightweight modes in implementation area, such as CLOC, JAMBU, and COFB, however unlike these modes, SUNDAE is designed as a deterministic authenticated encryption (DAE) scheme, meaning it provides maximal security in settings where proper randomness is hard to generate, or secure storage must be minimized due to expense. Unlike other DAE schemes, such as GCM-SIV, SUNDAE can be implemented efficiently on both constrained devices, as well as the servers communicating with those devices. We prove SUNDAE secure relative to its underlying block cipher, and provide an extensive implementation study, with results in both software and hardware, demonstrating that SUNDAE offers improved compactness and power consumption in hardware compared to other lightweight AE modes, while simultaneously offering comparable performance to GCM-SIV on parallel high-end platforms.

2018

Chargement

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