Exploiting Rateless Codes in Cloud Storage Systems

devices (virtual disks) that can be directly accessed and used as if they were raw physical disks. In this paper we devise ENIGMA, an architecture for the back-end of BLCS systems able to provide adequate levels of access and transfer performance, availability, integrity, and confidentiality, for the data it stores. ENIGMA exploits LT rateless codes to store fragments of sectors on storage nodes organized in clusters. We quantitatively evaluate how the various ENIGMA system parameters affect the performance, availability, integrity, and confidentiality of virtual disks. These evaluations are carried out by using both analytical modeling (for availability, integrity, and confidentiality) and discrete event simulation (for performance), and by considering a set of realistic operational scenarios. Our results indicate that it is possible to simultaneously achieve all the objectives set forth for BLCS systems by using ENIGMA, and that a careful choice of the various system parameters is crucial to achieve a good compromise among them. Moreover, they also show that LT coding-based BLCS systems outperform traditional BLCS systems in all the aspects mentioned before

Fast Data Retrieval and Enhanced Data Security of Cloud Storage in Luby Transform

AbstractCloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. It advantages to mention but a few include scalability, resilience, flexibility, efficiency and outsourcing non-core activities.Despite the potential gains achieved from the cloud computing, the organizations are slow in accepting it due to security issues and challenges associated with it. The idea of handing over important data to another company is worrisome; such that the consumers need to be vigilant in understanding the risks of data breaches in this new environment. This paper introduces analysis of the cloud computing security issues and challenges focusing on providing data confidentiality along with high requirement of data availability in cloud technology

AONT-LT: a Data Protection Scheme for Cloud and Cooperative Storage Systems

We propose a variant of the well-known AONT-RS scheme for dispersed storage systems. The novelty consists in replacing the Reed-Solomon code with rateless Luby transform codes. The resulting system, named AONT-LT, is able to improve the performance by dispersing the data over an arbitrarily large number of storage nodes while ensuring limited complexity. The proposed solution is particularly suitable in the case of cooperative storage systems. It is shown that while the AONT-RS scheme requires the adoption of fragmentation for achieving widespread distribution, thus penalizing the performance, the new AONT-LT scheme can exploit variable length codes which allow to achieve very good performance and scalability.Comment: 6 pages, 8 figures, to be presented at the 2014 High Performance Computing & Simulation Conference (HPCS 2014) - Workshop on Security, Privacy and Performance in Cloud Computin

Multiframe coded computation for distributed uplink channel decoding

The latest 5G technology in wireless communication has led to an increasing demand for higher data rates and low latencies. The overall latency of the system in a cloud radio access network is greatly affected by the decoding latency in the uplink channel. Various proposed solutions suggest using network function virtualization (NFV). NFV is the process of decoupling the network functions from hardware appliances. This provides the exibility to implement distributed computing and network coding to effectively reduce the decoding latency and improve the reliability of the system. To ensure the system is cost effective, commercial off the shelf (COTS) devices are used, which are susceptible to random runtimes and server failures. NFV coded computation has shown to provide a significant improvement in straggler mitigation in previous work. This work focuses on reducing the overall decoding time while improving the fault tolerance of the system. The overall latency of the system can be reduced by improving the computation efficiency and processing speed in a distributed communication network. To achieve this, multiframe NFV coded computation is implemented, which exploits the advantage of servers with different runtimes. In multiframe coded computation, each server continues to decode coded frames of the original message until the message is decoded. Individual servers can make up for straggling servers or server failures, increasing the fault tolerance and network recovery time of the system. As a consequence, the overall decoding latency of a message is significantly reduced. This is supported by simulation results, which show the improvement in system performance in comparison to a standard NFV coded system

Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations

The Internet of Things (IoT) promises ubiquitous connectivity of everything everywhere, which represents the biggest technology trend in the years to come. It is expected that by 2020 over 25 billion devices will be connected to cellular networks; far beyond the number of devices in current wireless networks. Machine-to-Machine (M2M) communications aims at providing the communication infrastructure for enabling IoT by facilitating the billions of multi-role devices to communicate with each other and with the underlying data transport infrastructure without, or with little, human intervention. Providing this infrastructure will require a dramatic shift from the current protocols mostly designed for human-to-human (H2H) applications. This article reviews recent 3GPP solutions for enabling massive cellular IoT and investigates the random access strategies for M2M communications, which shows that cellular networks must evolve to handle the new ways in which devices will connect and communicate with the system. A massive non-orthogonal multiple access (NOMA) technique is then presented as a promising solution to support a massive number of IoT devices in cellular networks, where we also identify its practical challenges and future research directions.Comment: To appear in IEEE Communications Magazin

Dynamic Allocation for Resource Protection in Decentralized Cloud Storage

Decentralized Cloud Storage (DCS) networks represent an interesting solution for data storage and management. DCS networks rely on the voluntary effort of a considerable number of (possibly untrusted) nodes, which may dynamically join and leave the network at any time. To profitably rely on DCS for data storage, data owners therefore need solutions that guarantee confidentiality and availability of their data. In this paper, we present an approach enabling data owners to keep data confidentiality and availability under control, limiting the owners intervention with corrective actions when availability or confidentiality is at risk. Our approach is based on the combined adoption of AONT (All-Or-Nothing-Transform) and fountain codes. It provides confidentiality of outsourced data also against malicious coalitions of nodes, and guarantees data availability even in case of node failures. Our experimental evaluation clearly shows the benefits of using fountain codes with respect to other approaches adopted by current DCS networks

Polyraptor: embracing path and data redundancy in data centres for efficient data transport

In this paper, we introduce Polyraptor, a novel data transport protocol that uses RaptorQ (RQ) codes and is tailored for one-to-many and many-to-one data transfer patterns, which are extremely common in modern data centres. Polyraptor builds on previous work on fountain coding-based transport and provides excellent performance, by exploiting native support for multicasting in data centres and data resilience provided by data replication

Network streaming and compression for mixed reality tele-immersion

Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor