Cloud computing has demonstrated itself to be a scalable and cost-efficient solution for many real-world applications. However, its modus operandi is not ideally suited to resource-constrained environments that are characterized by limited network bandwidth and high latencies. With the increasing proliferation and sophistication of edge devices, the idea of fog computing proposes to offload some of the computation to the edge. To this end, micro-clouds-which are modular and portable assemblies of small single-board computers-have started to gain attention as infrastructures to support fog computing by offering isolated resource provisioning at the edge in a cost-effective way. We investigate the feasibility and readiness of micro-clouds for delivering the vision of fog computing. Through a number of experiments, we showcase the potential of micro-clouds formed by collections of Raspberry Pi computers to host a range of fog-related applications, particularly for locations where there is limited network bandwidths and long latencies.

Cloud infrastructures are generally overprovisioned for handling load peaks and node failures. However, the drawback of this approach is that a large portion of data center resources remains unused. In this paper, we propose a framework that leverages unused resources of data centers, which are ephemeral by nature, to run MapReduce jobs. Our approach allows: i) to run efficiently Hadoop jobs on top of heterogeneous Cloud resources, thanks to our data placement strategy, ii) to predict accurately the volatility of ephemeral resources, thanks to the quantile regression method, and iii) for avoiding the interference between MapReduce jobs and co-resident workloads, thanks to our reactive QoS controller. We have extended Hadoop implementation with our framework and evaluated it with three different data center workloads. The experimental results show that our approach divides Hadoop job execution time by up to 7 when compared to the standard Hadoop implementation.

This paper presents the design of a P2P data persistent platform. Durable access and integrity of the data are ensured despite massive attacks. This platform, named DataCube, exploits the properties of cluster-based peer-to-peer substrates to implement a compound of full replication and rateless erasure codes. DataCube guarantees durable access and integrity of data despite adversarial attacks. In particular, the recovery of damaged data is achieved through the retrieval of coded blocks whose integrity is checked on the fly.