Energy and QoE aware Placement of Applications and Data at the Edge

Recent years are witnessing extensions of cyber-infrastructures towards distributed environments. The Edge of the network is gaining a central role in the agenda of both infrastructure and application providers. Following the actual distributed structure of such a computational environment, nowadays, many solutions face resource and application management needs in Cloud/Edge continua. One of the most challenging aspects is ensuring highly available computing and data infrastructures while optimizing the system's energy consumption. In this paper, we describe a decentralized solution that limits the energy consumption by the system without failing to match the users' expectations, defined as the services' Quality of Experience (QoE) when accessing data and leveraging applications at the Edge. Experimental evaluations through simulation conducted with PureEdgeSim demonstrate the effectiveness of the approach

Decentralized Federated Learning and Network Topologies: An Empirical Study on Convergence

Federated Learning is a well-known learning paradigm that allows the distributed training of machine learning models. Federated Learning keeps data in the source devices and communicates only the model's coefficients to a centralized server. This paper studies the decentralized flavor of Federated Learning. A peer-to-peer network replaces the centralized server, and nodes exchange model's coefficients directly. In particular, we look for empirical evidence on the effect of different network topologies and communication parameters on the convergence in the training of distributed models. Our observations suggest that small-world networks converge faster for small amounts of nodes, while xx are more suitable for larger setups

A cognitive-based solution for semantic knowledge and content dissemination in opportunistic networks

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Multicentre investigation of neutron contamination at cardiac implantable electronic device (CIED) location due to high-energy photon beams using passive detectors and Monte Carlo simulations

Radiotherapy treatments involving LINACs operating at accelerating potentials >10 MV generate (photo)neutrons which deliver dose to patients also outside the target volume. This effect is particularly relevant for patients with cardiac implantable electronic devices (CIEDs), which can be damaged by the therapeutic irradiation. In the last few years, there has been a rising interest in this issue, and it seems that damage to CIEDs is primarily associated with the thermal component of the photoneutron field. In particular, a recent study led by Politecnico di Milano considered CIEDs from various manufacturers and showed that some of these devices can be damaged after an irradiation with a thermal neutron fluence of about 10^9 cm^-2. The present work results from a collaboration among Politecnico di Milano, the University of Pisa, the University of Trieste and three Italian hospitals located in Lucca, Trieste and Varese, respectively, and it is primarily aimed at evaluating the thermal neutron fluence in CIED region for some high-energy treatments delivered at 15 and 18 MV and to determine whether it is comparable to the critical value given above, which has been experimentally determined to be potentially harmful for CIEDs. Thermal neutron fluence was measured through CR-39 detectors and TLDs, which were housed inside a BOMAB-like phantom mimicking the patient’s trunk. The experimental sessions involved two models of LINAC, Varian Clinac DHX (Varese hospital) and Elekta Synergy (Lucca and Trieste hospitals). The experimental results show that the treatments considered in this study can lead to a thermal neutron fluence in the cardiac region comparable to the critical value. Furthermore, detailed Monte Carlo geometries for the facilities involved in this project were developed with the MCNP code (v. 6.2), and they were tested by comparing simulation results to measurements considering some benchmark irradiation plans. Bubble detectors were also employed for fast neutron fluence measurements to be compared to simulation outputs. These computational models stand out as promising tools for the investigations required in this work, and they can be used for further studies also extending their use to analogous facilities hosting the same models of LINACs

Crowdsourcing through cognitive opportunistic networks

Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However the emergence of opportunistic networking (ON) has opened up crowdsourcing to the spatial domain. In this paper we bring the ON model for potential crowdsourcing in the smart city envi- ronment. We introduce cognitive features to the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics, i.e. mental models that describe how the human brain handles decision- making amongst complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in the cyber-world of their users and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants, using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking

PBC and MEIS class genes in the planarian Schmidtea mediterranea

Planarians have outstanding regenerative capabilities which depend on the presence of a pool of somatic stem cells. In addition planarians keep perfect proportion of the body while degrowing during starvation. While the establishment and maintenance of the anteroposterior (AlP) axis has been shown to depend on Wnt and Hedgehog signalling, not much is known about the subsequent patterning events. PBC and MEIS class genes are evolutionary conserved TALE homeobox genes with important functions in patteming and cell differentiation. They are well known co-factors of the Hox genes however they also have other Hox-independent functions. The aim of this study was to identify the planarian orthologs of the PBC and MEIS class genes and to examine their putative role in anteroposterior patteming. Here one PBC class gene, Smed-pbx, and the three MEIS class genes Smed-meis I , Smed-meis2 and Smed-prep were identified and orthology established through phylogenetic analyses. Smed-prep was found to define the cephalic area in which the anterior structures are formed, most prominently the brain. In anterior regenerating Smed-prep(RNAi) worms a reduction or even complete absence of the brain and anterior markers was observed. Importantly, the differentiation of stem cells into nerve cells was shown not to be dependent on Smed-prep. In step with its function the Smed-prep gene was found to have an anteroposterior bias in expression, being highly expressed in the head. While both Smed-meis genes were found to be important for regeneration and maintenance of the eyes and for the adaptation of the body to the new size of the animal after amputation, Smed-pbx was found to have pleiotropic functions, phenocopying aspects of all three MEIS class genes upon RNAi interference, and also producing additional phenotypes. This study has found that the four planarian orthologs of PBC and MEIS class genes are important players in regeneration. Two genes, Smed-pbx and especially Smed-prep, have been found to be necessary for anteroposterior patterning. This is the first time that a homeobox transcription factor has been directly implicated in anteroposterior patterning in planarians.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Peer-to-peer Systems for Resource Discovery in a Dynamic Grid

The convergence of the Grid and Peer-to-Peer (P2P) worlds has led to many solutions that try to efficiently solve the problem of resource discovery on Grids. Some of these solutions are extensions of P2P DHT-based networks. We believe that these systems are not flexible enough when the indexed data are very dynamic, i.e., the values of the resource attributes change very frequently over time. This is a common case for Grid metadata, like CPU loads, queue occupation, etc. Moreover, since common requests for Grid resources may be expressed as multi-attribute range queries, we think that the DHT-based P2P solutions are poorly flexible and efficient in handling them. In this paper we present two P2P systems. Both are based on Routing Indexes, which are used to efficiently route queries and update messages in the presence of highly variable data. The first system uses a tree-shaped overlay network. The second one is an evolution of the first, and is based on a two-level hierarchical network topology, where tree topologies must only be maintained at the lower level of the hierarchy, i.e., within the various node groups making up the network. The main goal of the second organization is to achieve a simpler maintenance of the overall P2P graph topology, by preserving the good properties of the tree-shaped topology. We discuss the results of extensive simulation studies aimed at assessing the performance and scalability of the proposed approaches. We also analyze how the network topologies affect the propagation of query and update messages

Experiences with Complex User Profiles for Approximate P2P Community Matching

The problem of defining P2P overlays where peers characterized by similar interests are directly connected is currently an important research issue. We have recently proposed a two layer P2P architecture where the first layer exploits a gossip algorithm for the detection of communities of peers characterized by similar interests, while the second layer defines a DHT storing the profiles of the communities detected in the first layer. The DHT is exploited by peers joining the system to find out a community matching its interests. This paper investigates a DHT based approach supporting a similarity based search of user profiles. Our approach exploits Locality Sensitive Hashing to support the similarity indexing. The paper investigates several types of profiles to model user interests and evaluates the indexing mechanisms of the DHT with respect to the different types. Experimental evaluation has been conducted by considering a real data set