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Organizations need process data in real time. Cloud Computing is a common choice but it presents high latency on data transfer. As an alternative to reduces the latency, Edge computing based on System-on-a-Chip systems (SoC-based systems) can retain part of the data processing, allowing resource sharing among multiple requests through the use of OS-level virtualization. In this work, we analyze the performance of two popular OS-level virtualization solutions - Linux Containers (LXC) and Docker - for I/O-bound activities running in SoC systems, in order to understand how resource sharing affects the processing performance.

Prototyping and testing distributed systems is considered to be a hard task because it is not always possible to reproduce a given sequence of events. While simulations may help on this task, they cannot replace test and validation with real systems. In this paper we present Docker-Hadoop, a container-based virtualization platform designed to prototype, test and deploy MapReduce applications and systems. This tool allowed us to test and reproduce fault-tolerance scenarios that are especially interesting in the context of the PER-MARE project, which aims at adapting the Hadoop framework to the case pervasive systems. Indeed, we developed a fault-tolerant component that can circumvent the limitations from original Hadoop and prevent the job scheduling stall in the case of failures or network disconnections. Thanks to Docker-Hadoop, we could easily prototype and test our improved Hadoop, with the first scalability and speedup results being presented in this paper.

International audience; Recent advances in communication technology en- able the emergence of a new generation of applications that integrates mobile devices with classical high performance systems as part of a common computing environment. In such environ- ments, keeping the coherence of shared data (distributed objects, for example) represents a real challenge as communications are strongly influenced by the performance and the reliability of mobile devices (laptops, PDAs and cellular telephones) and wireless networks (WiFi, Bluetooth). Indeed, data incoherence may arise due to message losses or node volatility, which blocks the algorithms used to synchronize these data. In this paper, we analyze the main challenges concerning the manipulation of shared distributed objects in a pervasive environment. We demonstrate how a membership service can be enhanced to tolerate temporary disconnections and message losses without blocking, while reducing the number of exchanged message.