Towards distributed architecture for collaborative cloud services in community networks

Internet and communication technologies have lowered the costs for communities to collaborate, leading to new services like user-generated content and social computing, and through collaboration, collectively built infrastructures like community networks have also emerged. Community networks get formed when individuals and local organisations from a geographic area team up to create and run a community-owned IP network to satisfy the community’s demand for ICT, such as facilitating Internet access and providing services of local interest. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. To address the limitation and enhance utility of community networks, we propose a collaborative distributed architecture for building a community cloud system that employs resources contributed by the members of the community network for provisioning infrastructure and software services. Such architecture needs to be tailored to the specific social, economic and technical characteristics of the community networks for community clouds to be successful and sustainable. By real deployments of clouds in community networks and evaluation of application performance, we show that community clouds are feasible. Our result may encourage collaborative innovative cloud-based services made possible with the resources of a community.Peer ReviewedPostprint (author’s final draft

Well log interpretation and 3D reservoir property modeling of Maui-B field, Taranaki Basin, New Zealand

Maui-B is one of the largest hydrocarbon-producing fields in the Taranaki Basin. Many previous works have estimated reservoir volume. This study uses 3D property modeling, which is one of the most powerful tools to characterize lithology and reservoir fluids distribution through the field. This modeling will help in understanding the reservoir properties and enhancing the production by selecting the best location for future drilling candidates. In this study, 3D seismic, core, and well log data were used to build and define a structural model, facies analysis, and petrophysical parameters. After well log interpretation and petrophysical parameter calculations, each parameter was upscaled. Then, geostatistical methods, including Gaussian simulation, variogram, and Monte Carlo simulation, were used to build a 3D property model. A thousand 3D models were constructed and performed for each parameter; the outputs were implemented into Monte Carlo simulation, which is a highly reliable method regarding accuracy to calculate the mean of each parameter. Then, the volume of the reservoir was estimated. In this study, integration of seismic interpretation and well logs defined the depth and thickness of the hydrocarbon reservoir through the field. Gamma ray, spontaneous potential, and caliper logs were used for depth correlation and identifying permeable zones. As a result, five different lithofacies, where sandstone and claystone distribution have the significant impact on reservoir quality were identified. The matrix identification (MID) method was used for porosity correction, which showed effective porosity ranges of 15-25%. Moreover, permeability was estimated as 79-3700 mD, where all results were calibrated using available core data. Furthermore, 9% to 40% water saturation was estimated using the resistivity logs and core data. Finally, oil and gas in place were estimated --Abstract, page iii

Mind the Gap: Reconciling Formalism and Intuitionism in Computational Design Research

The paper discusses the epistemological and methodological implications of an increasing process of formalisation and naturalisation of knowledge within the context of the complexity paradigm. This process is argued to induce a shift in the nature of notations and representations, to which corresponds an epistemic shift from a graphic to a computational rationality, with substantial effects on current design methodologies and strategies used in computational design. The shortcomings of a heavy formalism are discussed with respect to a possible reconciliation between the operational efficiency of formalist representations and the recovery of the phenomenological grounds of design experimentations through a simultaneous articulation of formalist and intuitionist approaches in computational design research

Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft

The Antifeedant, Insecticidal and Insect Growth Inhibitory Activities of Euphorbia Lathyrism L. Plant Extracts on Cetonia Aurata L. (Coleoptera: Scarabaeoidea: Cetoniidae)

Insecticides have been linked to serious toxicological and environmental issues. The purpose of this study is to assess the insecticidal efficacy of dough produced from the roots of caper spurge or paper spurge (Euphorbia lathyris) and to classify chemicals based on their toxicity levels. Keywords: Cetonia aurata, Euphorbia lathyrism, antifeedant, insecticidal DOI: 10.7176/JNSR/14-6-02 Publication date: April 30th 202

BePOCH: Improving federated learning performance in resource-constrained computing devices

Inference with trained machine learning models is now possible with small computing devices while only a few years ago it was run mostly in the cloud only. The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines. But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device. Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption. In this paper, we present an approach which uses a dynamic epoch parameter in the model training. We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning. We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy. Thus, BePOCH makes machine learning model training on low-capacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important asnect towards greener machine learning techniques.This work was partially funded by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111850- 2 (DiPET CHIST-ERA), PCI2019-111851-2 (LeadingEdge CHIST-ERA), and the Generalitat de Catalunya as Consolidated Research Group 2017-SGR-990. Suport was given also by the Agency for Electronic Communications (AEK) of North Macedonia.Peer ReviewedPostprint (author's final draft

Designing a double LoRa connectivity for the Arduino Portenta H7

Machine learning is moving to the smallest computing devices. Today machine learning is applied even in tiny IoT microcontroller boards. In the IoT, LoRa is a popular communication technology to connect remote devices with gateways. Still, the confluence of machine learning in microcontrollers and networked LoRa connectivity is not yet fully exploited. In this paper we design a new LoRa connectivity for the Arduino Portenta H7, a recent microcontroller board equipped with embedded sensors suitable for diverse machine learning tasks. With the solution that we found the Arduino Portenta H7 is able to become part of a LoRa mesh network. This capacity increases the Portenta's range of applications. For the vision of distributed machine learning at the tiny edge, we can add with the Portenta an important board to become a smart compute node within a LoRa mesh network.This work was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA).Peer ReviewedPostprint (author's final draft