Resource discovery in a grid environment is a critical problem, as a typical grid system includes a very large number of resources, which must be readily identified and accessed to run applications. Traditional grid discovery algorithms perform poorly, as they do not scale, nor allow grid-enabled applications to transparently query the whole set of grid resources. Peer-to-peer (P2P) has been argued as a suitable distributed paradigm that not only overcomes the issues of scalability of such discovery systems, but also better supports the discovery of resources in a context of dynamicity of resources and associated information. In this paper we propose a P2P system for indexing and discovering grid resources. We assume that grid resources are characterized by a set of attributes, and our system can be queried for resources satisfying arbitrary range conditions on these attributes. Note that traditional P2P searching techniques can not be directly applied in this case, since they work well mostly for static content and exact queries. Simulation results show that the system provides an adequate degree of scalability

Clouds have been relevant business drivers and computational backends for a wide range of applications, including IoT, e-health, data analytics. To match the complex needs of such comprehensive set of different kinds of applications, in recent times there is an emerging need for new paradigms and forms of Clouds, organised according to a federated, heterogeneous and distributed structure. To exploit heterogeneity and localisation, in order to enhance the overall performances, ensure energy efficiency, reduce costs for resource providers and in the meantime enhance the user experience, proper service placement solutions are required. However, conducting efficient deployments in such a scenario is complex due to the dynamic nature of applications, resources, users. As a consequence, there the a need for scalable, distributed, adaptive, context-aware solutions characterised by high-efficiency and reduced overhead. We propose a highly distributed, self-adaptive solution aimed at optimising the overall deployment of cloud services by means of point-to-point interactions occurring among clouds and cloudlets belonging to the same federation. The contribution of this paper is the definition of a service exchange mechanism, its Markov-chain based modelling and thorough experimental evaluation.

In the convergence of theCyber-Physical World, user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, therecognition heuristic, for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.