Data storage in the Cloud became a very popular service. However, delegation of data management results in loss of control from user perspective, in particular regarding the real location where data are stored. Thus, data location verification in the Cloud is a challenging issue. Among the huge methods proposed to consider data location verification, this paper focuses on machine learning based methods, which use network Round Trip Times as main metric. In particular, it provides experimental results based on country-wide dataset collected through Grid'5000 platform. Results show the capacities of regression-based methods to support data location verification at specific accuracy depending on user requirements.

In this paper, we introduce a majority quorum system dedicated to p-m-n general threshold schemes where p, n and m are respectively the minimal number of chunks that provide some information (but not necessarily all) on the original data, the total number of nodes in which the chunks of an object are stored and the minimal number of nodes needed to retrieve the original data using this protocol. In other words, less than p chunks reveal absolutely no information about the original data and less than m chunks can't reconstruct the original data. The p-m-n general threshold schemes optimize the usage of storage resources by reducing the total size of data to write and ensure fault-tolerance up to (n -- m) nodes failure. With such a data distribution, a specific value of m can be set to have a good trade off between resources utilization and fault-tolerance. The only drawback of such schemes is the lack of any consistency protocol. If fact, consistency protocols like classical majority quorum are based on full replication. To successfully read or write a data using the majority quorum protocol, an absolute majority of replicas must be read / written correctly. This condition ensures that any read and write operations will contain at least one common replica, which guarantees their consistency. However, when a threshold scheme is used, an adaptation is needed. In fact, classical majority quorum protocol can no longer ensure that m chunks will have the latest version when [n/2] + 1

A great number of scientific applications that access to a large amount of data have emerged last few years. For example in high energy physics, the volume of data will be measured in petabytes in the next years. Grids seek to aggregate geographically distributed resources for the need of these applications. It results that scientists have to deal with many problems to manage available data over the grid. Since grid resources are interconnected by WAN, the main speculation is that data transfers over the grid are bounded by network latency and bandwidth. Here, we point out some storage device issues which in many cases invalidate this assumption. Implicitly, we give a new direction that will lead to a better utilization of available resources on a grid

Complex scientific problems involving large volumes of data need a huge computing power.Memory bandwidth remains a key issue in high performance systems.This paper presents an originrd method of memory organization, based on serial multiport memory components, which allows simultaneous access to all ports without causing either cortfhct between them or suspension.The resulting process for information exchange gives a cost-effective realization of a data memory for vector processors.The memory bandwidth can be increased considerably in a modular manner, without practical implementation constraints.The information exchange on the serial links guarantees a continuous data flow and the reaching of a maximum yield of the pipeline, thus avoiding the step-by-step feeding of the pipeline operator.