Abstract
Ensuring the effectiveness factor of usability consists in ensuring that the application allows users to reach their goals and perform their tasks. One of the few means for reaching this goal relies on task analysis and proving the compatibility between the interactive application and its task models. Synergistic execution enables the validation of a system against its task model by co-executing the system and the task model and comparing the behavior of the system against what is prescribed in the model. This allows a tester to explore scenarios in order to detect deviations between the two behaviors. Manual exploration of scenarios does not guarantee a good coverage of the analysis. To address this, we resort to model based testing (MBT) techniques to automatically generate scenarios for automated synergistic execution. To achieve this, we generate, from the task model, scenarios to be co-executed over the task model and the system. During this generation step we explore the possibility of including considerations about user error in the analysis. The automation of the execution of the scenarios closes the process. We illustrate the approach with an example

Abstract

Abstract
Faced with the need to quantify software (un)reliability in the presence of faults, the semantics of state-based systems is urged to evolve towards quantified (e.g. probabilistic) nondeterminism. When one is approaching such semantics from a categorical perspective, this inevitably calls for some technical elaboration, in a monadic setting. This paper proposes that such an evolution be undertaken without sacrificing the simplicity of the original (qualitative) definitions, by keeping quantification implicit rather than explicit. The approach is a monad lifting strategy whereby, under some conditions, definitions can be preserved provided the semantics moves to another category. The technique is illustrated by showing how to introduce probabilism in an existing software component calculus, by moving to a suitable category of matrices and using linear algebra in the reasoning. The paper also addresses the problem of preserving monadic strength in the move from original to target (Kleisli) categories, a topic which bears relationship to recent studies in categorial physics.

Abstract
A key component in a distributed parallel analytical processing engine is shuffling, the distribution of data to multiple nodes such that the computation can be done in parallel. In this paper we describe the initial design of a communication middleware to support asynchronous shuffling of data among multiple processes on a distributed memory environment. The proposed middleware relies on RDMA (Remote Direct Memory Access) operations to transfer data, and provides basic operations to send and queue data on remote machines, and to retrieve this queued data. Preliminary results show that the RDMA-based middleware can provide a 75% reduction on communication costs, when compared with a traditional sockets implementation.

Abstract
A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large-scale multitenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that may be scarce in a cloud environment. We present DEDIS, a dependable and fully decentralized system that performs cluster-wide off-line deduplication of virtual machines' primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of an open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers, thus, sparing the need of additional hardware.

Abstract
A key component in large scale distributed analytical processing is shuffling, the distribution of data to multiple nodes such that the computation can be done in parallel. In this paper we describe the design and implementation of a communication middleware to support data shuffling for executing multi-stage analytical processing operations in parallel. The middleware relies on RDMA (Remote Direct Memory Access) to provide basic operations to asynchronously exchange data among multiple machines. Experimental results show that the RDMA-based middleware developed can provide a 75% reduction of the costs of communication operations on parallel analytical processing tasks, when compared with a sockets middleware.