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In federated learning, distributed nodes train a local machine learning model and exchange it through a central aggregator. In real environments, these training nodes are heterogeneous in computing capacity and bandwidth, thus their specific characteristics influence the performance of the federated learning process. We propose for such situations the design of a federated learning server that is able to adapt dynamically to the heterogeneity of the training nodes. In experiments with real devices deployed in a wireless mesh network, we observed that the designed adaptive federated learning server successfully exploited the idle times of the fast nodes by assigning them larger training workloads, which led to a higher global model performance without increasing the training time., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871582 — NGIatlantic.eu and was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA), and by national funds through FCT, Fundação para a Ciência e a Tecnologia, Portugal, under project UIDB/50021/2020. The work of C.-H. Liu was supported in part by the U.S. National Science Foundation (NSF) under Award CNS-2006453 and in part by Mississippi State University under Grant ORED 253551-060702. The work of L. Wei is supported in part by the U.S. National Science Foundation (#2006612 and #2150486)., Peer Reviewed, Postprint (author's final draft)

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 Reviewed, Postprint (author's final draft)

Federated learning is a distributed learning technique in which a machine learning model is trained collaboratively among several nodes. While the privacy preservation of the training data is one of the important promises of federated learning, there is also an opportunity to use low capacity devices for machine learning model training by taking advantage of the fact that the training effort is divided among many nodes. In this paper, we conduct experiments with a federated learning network deployed on several low capacity devices connected to a wireless mesh network. The measurements show the hardware capacity and link bandwidth of the clients on the federated learning process. The results suggest that for heterogeneous networks the federated learning clients should be extended with more autonomous decision capacities according to the network and local conditions., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871582 - NGIatlantic.eu and was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA). The work of C.-H. Liu was supported in part by the U.S. National Science Foundation (NSF) under Award CNS-2006453 and in part by Mississippi State University under Grant ORED 253551-060702. The work of L. Wei is supported in part by the U.S. National Science Foundation (#2006612 and #2150486)., Peer Reviewed, Postprint (author's final draft)

There is a growing research interest in Federated Learning (FL), a promising approach for data privacy preservation and proximity of training to the network edge, where data is generated. Resource consumption for Machine Learning (ML) training and inference is important for edge nodes, but most of the proposed protocols and algorithms for FL are evaluated by simulations. In this demo paper, we present an environment based on distributed mini-PCs to enable experimental study of FL protocols and algorithms. We have installed low-capacity mini-PCs within a wireless city-level mesh network and deployed container-based FL components on these nodes. We show the deployed FL clients and server at different nodes in the city and demonstrate how an FL experiment can be set and run in a real environment., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871582 — NGIatlantic.eu and was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA). The work of C.-H. Liu was supported in part by the U.S. National Science Foundation (NSF) under Award CNS-2006453 and in part by Mississippi State University under Grant ORED 253551-060702. The work of L. Wei is supported in part by the U.S. National Science Foundation (#2150486 and #2006612). I Koutsopoulos acknowledges support from the CHIST-ERA grant CHIST-ERA-18-SDCDN-004 (GSRI grant number T11EPA4-00056)., Peer Reviewed, Postprint (author's final draft)

Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for advanced optimization methods to place services in the network. In particular, an efficient service placement method is key for the performance of these systems. This work presents the Multi-Criteria Optimal Placement method, a novel and fast two-stage multi-objective method to place services in decentralized community network edge micro-clouds. A comprehensive set of computational experiments is carried out using real traces of Guifi.net, which is the largest production community network worldwide. According to the results, the proposed method outperforms both the random placement method used currently in Guifi.net and the Bandwidth-aware Service Placement method, which provides the best known solutions in the literature, by a mean gap in bandwidth gain of about 53% and 10%, respectively, while it also reduces the number of resources used., This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities (PGC2018-097599-B-100 and PID2019-106774RB-C21), and by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 (DiPET CHIST-ERA) and PCI2019-111851-2 (LeadingEdge CHIST-ERA)., Peer Reviewed, Postprint (author's final draft)

The increase of the computing capacity of IoT devices and the appearance of lightweight machine learning frameworks have led to the situation that machine learning can nowadays be run in IoT applications at the network edge. There is an opportunity to implement machine learning algorithms with the more and more computationally powerful edge nodes and using the ever increasing amount of local data coming from nearby sensors. For this purpose, federated learning becomes a promising machine learning approach, where a machine learning model is trained by various nodes using their local data. For performing practical federated learning experiments, we have built a testbed deployed within a wireless city mesh network with geographically distributed low capacity devices. We describe the testbed implementation and show its potential to experimentally study federated learning protocols and algorithms in real edge environments., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871582 — NGIatlantic.eu and was partially supported by the Spanish Government under contracts PID2019-106774RBC21, PCI2019-111850-2 (DiPET CHIST-ERA), PCI2019-111851-2 (LeadingEdge CHIST-ERA). The work of C.-H. Liu was supported in part by the U.S. National Science Foundation (NSF) under Award CNS-2006453 and in part by Mississippi State University under Grant ORED 253551-060702. The work of L. Wei is supported in part by the U.S. National Science Foundation (#2006612 and #2150486)., Peer Reviewed, Postprint (published version)

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 Reviewed, Postprint (author's final draft)

Universal connectivity is still unavailable or expensive for half of the global population, despite being critical for social participation. The deployment of crowdsourced networking infrastructures creates an opportunity for local development, where anyone can deploy a new device. In such infrastructures connectivity offer can expand incrementally and be sustainable through investment and fees resulting from the demand and consumption of content and services, including Internet access, that compensate the cost of the underlying network. While routing coordinates network data flows, economic flows can be coordinated by smart contracts built over a local distributed ledger. We define crowdsourced networks, the concept, architecture, and implementation using a local Ethereum PoA blockchain with Solidity smart contracts that compensate the data traffic contribution and consumption recorded by a traffic monitoring system, on a wireless mesh network. The prototype software has been validated in a controlled mesh network environment. Functional tests show its ability to account and route economic flows with small resource consumption, and therefore confirms these networks can develop organically by the addition of consumer and provider participants to reach the typical scale of most wireless mesh access networks and deliver networking services that aim to be socially and economically sustainable., Peer Reviewed, Postprint (author's final draft)

The Guifi.net community network has evolved during the past 15 years into a telecommunications infrastructure that offers Internet access to more than 80.000 people. The monitoring system currently in place for this network is lagging behind the growth of the infrastructure, requiring manual intervention and counting several single points of failure. In this paper we present REDEMON, a resilient decentralized monitoring system, hosted on distributed and interconnected edge devices, for a reliable, eventually-consistent monitoring of the Guifi.net network, leveraging CRDT-based data structures implemented on AntidoteDB. We developed the REDEMON system as a prototype featuring resilience, decentralization and automation, in order to replace the legacy monitoring system. To assess the system, this prototype was deployed on resource-constraint edge nodes in the Guifi.net production network and evaluated under realistic conditions. The decentralized assignment mechanism successfully achieves setting the minimum number of monitoring servers per network device that satisfies the established system requirements. Besides, by concentrating the workload on the minimum required number of servers running at their maximum capacity, the remaining devices can idle away, reducing the consumption footprint of the system. With regard to computing resources, we measure a moderate CPU and RAM usage by the monitoring system on low-capacity devices, while we observe that a considerable network traffic is required for achieving a resilient and consistent data storage layer. This resilient and decentralized architecture could lay the basis for other edge applications in the cloud computing domain that need to coordinate over distributed and consistent shared data., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish State Research Agency (AEI) under contracts PCI2019- 111850-2 and PCI2019-111851-2, and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

By leveraging resources from the Fed4Fire+ City-Lab testbed, we design the PiGeon edge computing platform that experiments solution that enable ICN based edge services in wireless mesh networks (WMNs). PiGeon combines into a platform several trends in edge computing namely the ICN (Information-Centric Networking), the containerization of services exemplified by Docker, novel service placement algorithms and the increasing availability of energy efficient but still powerful hardware at user premises (Raspberry Pi, mini-PCs, and enhanced home gateways). We underpin the PiGeon platform with Docker container-based service that can be seamlessly delivered, cached and deployed at the network edge. The core of the PiGeon platform is the Decision Engine making a decision on where and when to deploy a service instance to satisfy the service requirements while considering the network status and available hardware resources. We collect network data from a real citywide mesh network such as CityLab FIRE testbed located at the city of Antwerp, Belgium. The collected data is used to feed our service placement heuristic within the PiGeon platform. Through a real deployment in CityLab testbed, we show that our service placement heuristic improves the response time up to 37% for stateful services (Web2.0 service). Apart from improving the QoS for end-users, our results show that ICN plays a key role in improving the service delivery time as well as reducing the traffic consumption in WMNs. The overall effect of ICN in our platform is that most content and service delivery requests can be satisfied very close to the client device, many times just one hop away, decoupling QoS from intra-network traffic and origin server load., This work was supported by the European H2020 framework programme project Fed4Fire+ (732638), by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 and PCI2019-111851-2, and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

The growing demand for network connectivity has boosted the number of community networks (CNs). CNs are decentralized and self-organized communication networks owned and managed at the edge by volunteers. Due to the heterogeneity of edge node characteristics, high software and hardware diversity, irregular topology and unreliable behavior of the network, the performance of its services varie depending on where they are hosted. These characteristics of CNs and edge platforms running on them require of advanced simulation-optimization methods to place services. In this context, we propose a simheuristic algorithm to address this stochastic problem. The core of this approach relies on a multi-start metaheuristic with a multi-objective optimization method. Our approach combines Monte Carlo simulation and the multi-criteria optimal placement heuristic, The method is tested using real traces of Guifi.net CN, which is considered to be largest CN worldwide., This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities under contract PGC2018-097599-B-100 and by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 and PCI2019-111851-2., Peer Reviewed, Postprint (author's final draft)

This is the peer reviewed version of the following article: Kabbinale, AR, Dimogerontakis, E, Selimi, M, et al. Blockchain for economically sustainable wireless mesh networks. Concurrency Computat Pract Exper. 2020; 32:e5349, which has been published in final form at https://doi.org/10.1002/cpe.5349. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving., Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low-cost Internet access with infrastructures built from routers contributed by diverse owners, whereas blockchain enables transparency and accountability for investments, revenue, or other forms of economic compensations from sharing of network traffic, content, and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means that every participant can invest in resources and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self-organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city-wide production mesh network and in laboratory network. We quantify the performance and bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains., This paper has been supported by the AmmbrTech Group, the Spanish Government TIN2016‐77836‐C2‐2‐R and the European Community H2020 Programme netCommons (H2020‐688768). The authors would like to thank the people from the Guifi.net (Guifi‐Sants) community network for hosting the servers and supporting the experiments., Peer Reviewed, Postprint (author's final draft)

We present the case of monitoring a decentralized and crowdsourced network infrastructure, that needs to be monitored over geographically distributed devices at the network edge. It is a characteristic of the target environment that both, the infrastructure to be monitored and the hosts where the monitoring system runs, change over time, and network partitions may happen. The proposed monitoring system is decentralized, and monitoring servers coordinate their actions through an eventually consistent data storage layer deployed at the network edge. We developed a proof-of-concept implementation, which leverages CRDT-based data types provided by AntidoteDB. Our evaluation focuses on the understanding of the continuously updated mapping of monitoring server to network devices, specifically on the effects of different policies for each individual monitoring server to decide on which and how many network devices to monitor. One of the policies is experimented by means of a deployment on 8 real nodes, leveraging the data replication of AntidoteDB in a realistic setting. The observed effects of the different policies are interpreted from the point of view of the trade-off between resource consumption and redundancy., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish government contract TIN2016-77836-C2-2-R and PID2019-106774RB-C21 by the Catalan government contract AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

Community-built telecommunication networks such as Guifi.net demonstrate how end users can actively collaborate in the self-provision of network services, for instance by operating a self-organized distributed monitoring system. Network monitoring is performed by many small servers at the users' premises but data are only accessible via a centralized interface. Besides, due to network partitions and churn of the monitoring servers, failures in the monitoring system are frequent, leaving parts of the network unmonitored. Distributed databases are a promising solution for data replication under network partition condition, but they suffer from a trade-off between data consistency and availability. Furthermore, these databases are used in data centers with abundant computing resources, not in light edge networks. In this work we present DIMON, a reliable edge-based, eventually-consistent monitoring system that leverages CRDT-based data structures implemented in AntidoteDB. Conflict-free replicated data types (CRDTs) are able to converge to a consistent state in environments with network partitions as those found in edge networks. Our results give insights on the load of AntidoteDB on edge devices under different scenarios of read and write operations. The experiments carried out in a production network with a real system implemented contribute to the research community's knowledge about the available technologies for a consistent replicated data storage layer to support edge computing clouds., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish government under contract TIN2016-77836-C2-2-R and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Edge computing is radically shaping the way Internet services are run by enabling computations to be available close to the users - thus mitigating the latency and performance challenges faced in today’s Internet infrastructure. Emerging markets, rural and remote communities are further away from the cloud and edge computing has indeed become an essential panacea. Many solutions have been recently proposed to facilitate efficient service delivery in edge data centers. However, we argue that those solutions cannot fully support the operations in Community Mesh Networks (CMNs) since the network connection may be less reliable and exhibit variable performance. In this paper, we propose to leverage lightweight virtualisation, Information-Centric Networking (ICN), and service deployment algorithms to overcome these limitations. The proposal is implemented in the PiCasso system, which utilises in-network caching and name based routing of ICN, combined with our HANET (HArdware and NETwork Resources) service deployment heuristic, to optimise the forwarding path of service delivery in a network zone. We analyse the data collected from the Guifi.net Sants network zone, to develop a smart heuristic for the service deployment in that zone. Through a real deployment in Guifi.net, we show that HANET improves the response time up to 53% and 28.7% for stateless and stateful services respectively. PiCasso achieves 43% traffic reduction on service delivery in our real deployment, compared to the traditional host-centric communication. The overall effect of our ICN platform is that most content and service delivery requests can be satisfied very close to the client device, many times just one hop away, decoupling QoS from intra-network traffic and origin server load., Peer Reviewed, Postprint (author's final draft)

The digital world demands a network infrastructure to supply connectivity to any participant anywhere. Sustainable networks require balanced value flows. Value is connectivity delivered at a material and service cost to compensate, involving diverse participants, ranging from consumers to providers, such as last mile access, regional transport, Internet carriers, or content providers. We focus on the case of wireless mesh networks that deliver connectivity through access points and a mesh network that routes traffic to Internet gateways, provisioned by several device owners and service operators [1, 2, 3]. The presented work is motivated by the need for balance and automation among services delivered, costs and incentives for participation in these decentralised networks. This balance is key for achieving extensible network infrastructures that can deliver widespread availability of Internet connectivity with minimal barriers of entry., This paper has been partially supported by the AmmbrTech Group, the Spanish government TIN2016-77836-C2-2-R and the Catalan government AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

Cloud computing is moving from data centers to the network edge. Edge computing introduces lightweight computing devices closer to where data are produced. Local processing enables faster response times for cloud-based services. In this approach, however, cloud elasticity still lies in the data center and not at the edge, and an edge device cannot run services locally beyond its capacity. In this paper we present microclouds as an end user-managed infrastructure built from low-cost home servers leveraging PaaS and SaaS at the network edge. We discuss the following aspects: 1) the microcloud platform architecture and the facilities it offers to the users to manage their services and applications, 2) support services to enable the microcloud provision, and 3) of a number of decentralized applications and their potential to support service provision in microclouds., This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish government contract TIN2016-77836-C2-2-R, and by the Catalan government contract AGAUR SGR 990., Peer Reviewed, Postprint (author's final draft)

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. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, 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 these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment., Peer Reviewed, Postprint (published version)

Community networks (CNs) have seen an increase in the last fifteen years. Their members contact nodes which operate Internet proxies, web servers, user file storage and video streaming services, to name a few. Detecting communities of nodes with properties (such as co-location) and assessing node eligibility for service placement is thus a key-factor in optimizing the experience of users. We present a novel solution for the problem of service placement as a two-phase approach, based on: 1) community finding using a scalable graph label propagation technique and 2) a decentralized election procedure to address the multi-objective challenge of optimizing service placement in CNs. Herein we: i) highlight the applicability of leader election heuristics which are important for service placement in community networks and scheduler-dependent scenarios; ii) present a parallel and distributed solution designed as a scalable alternative for the problem of service placement, which has mostly seen computational approaches based on centralization and sequential execution., Peer Reviewed, Postprint (author's final draft)

Peer Reviewed, Postprint (published version)

Recently, mesh networking and blockchain are two of the hottest technologies in the telecommunications industry. Combining both can reformulate Internet access. While mesh networking makes connecting to the Internet easy and affordable, blockchain on top of mesh networks makes Internet access profitable by enabling bandwidth-sharing for crypto-tokens. Hyperledger Fabric (HLF) is a blockchain framework implementation and one of the Hyperledger projects hosted by The Linux Foundation. We evaluate HLF in a real production mesh network and in the laboratory. We quantify the performance, bottlenecks and limitations of the current implementation v1.0. We identify the opportunities for improvement to serve the needs of wireless mesh access networks. To the best of our knowledge, this is the first HLF deployment made in a production wireless mesh network., Peer Reviewed, Postprint (published version)

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 a, Peer Reviewed, Postprint (author's final draft)

Cotutela Universitat Politècnica de Catalunya i Instituto Superior Técnico [Lisboa], Community networks (CNs) have gained momentum in the last few years in response to the growing demand for network connectivity in rural and urban areas. These networks, owned and managed by volunteers, offer various services to their members. While Internet access is the most popular service offered to their members, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, CN micro-clouds pursue not only an improved service performance, but also a low entry barrier for the deployment of alternatives to 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 and different service requirements in CNs, a "careful" placement of micro-cloud services over the network is required. First, in order to understand the micro-cloud service requirements for a successful operation in CNs, we perform deployment, feasibility analysis and in-depth performance assessment of popular CN micro-cloud services such as distributed storage, live video-streaming and service discovery. We characterize and define workload upper bounds for successful operations of such services and perform cross-layer analysis and optimizations to improve the service performance. This deployment experience supports the feasibility of CN micro-clouds and our measurements contribute to understand the performance of services and applications in this challenging environment. Then, in order to improve the performance of the services on the network level over which a service host provides a service to client nodes, it is necessary to adapt the logical network topology to both external (e.g., wireless connectivity, node availability) and internal (e.g., service copies, service demand) factors. To achieve this, we propose to leverage state information about the network to inform service plac, Las redes comunitarias (Community Networks - CNs) han cobrado impulso en los últimos años en respuesta a la creciente demanda de conectividad de red en zonas rurales y urbanas. Estas redes, desplegadas y gestionadas por voluntarios, ofrecen diversos servicios a sus miembros. Si bien el acceso a Internet es el servicio más popular ofrecido a sus participantes, la provisión de servicios de interés local dentro de la red está siendo possible gracias a la tecnología emergente de micro-clouds en CNs. Al acercar los servicios a los usuarios, los micro-clouds persiguen no sólo una mejor experiencia de uso del servicio, sino también bajar la barrera de entrada para el despliegue de alternativas a los servicios de Internet convencionales dentro de la CN. Lamentablemente, la provisión de servicios no es tan simple. Debido a una topología grande e irregular, a la alta diversidad de software y hardware y a los diferentes requisitos de servicio en las CNs, es necesaria una colocación ”cuidadosa” de los servicios de micro-cloud a través de la red. Primero, para comprender los requisitos del micro-cloud para su operación exitosa en CNs, se realiza el despliegue, el análisis de factibilidad y la evaluación en profundidad del rendimiento de servicios populares de micro-cloud tales como almacenamiento distribuido, retransmisión de vídeo y descubrimiento de servicios. Se caracterizan y definen los límites superiores de la carga de trabajo para una operación exitosa de tales servicios y se realizan análisis y optimizaciones entre capas para mejorar el rendimiento del servicio. Esta experiencia de implementación apoya la factibilidad de los micro-clouds comunitarios, y las mediciones mediciones obtenidas contribuyen a comprender el rendimiento de los servicios y aplicaciones en este desafiante entorno. A continuación, con el fin de mejorar el rendimiento de los servicios a nivel de red sobre la que un servidor de servicios proporciona un recurso a los nodos cliente es necesario adaptar, As redes comunitárias (Community Networks - CNs) têm ganho popularidade nos últimos anos em resposta à crescente procura por conectividade de rede em áreas rurais e urbanas. Estas redes, propriedade de e geridas por voluntários, oferecem diversos serviços aos seus membros. Apesar do acesso à Internet ser o serviço mais popular, a provisão de serviços de interesse local dentro da própria rede é habilitada pela tecnologia emergente de micro-clouds em CNs. Ao colocar os serviços mais perto dos seus utilizadores, as micro-clouds não procuram apenas melhorar o desempenho dos serviços, mas também uma redução da barreira no que toca à implantação de outros serviços convencionais de Internet dentro da CN. Infelizmente, o provisionamento de serviços não é simples. Devido à grande e irregular topologia, à grande diversidade tanto de software como de hardware, bem como à diversidade de requisitos em CNs, é necessária uma colocação ”cuidada” de serviços em micro-clouds na rede. Primeiro, de forma a compreender os requisitos dos serviços em micro-clouds para que funcionem corretamente em CNs, analisamos a viabilidade, implementamos e avaliamos o desempenho de alguns serviços populares em micro-clouds tais como armazenamento distribuído, streaming de vídeo em direto, e serviços de descoberta de recursos e de outros serviços. Caracterizamos e definimos limites de carga para que as operações de tais serviços seja bem sucedida, e analisamos e optimizamos em várias camadas de forma a melhorar o desempenho do serviço. Esta experiência de implementação suporta a viabilidade das micro-clouds comunitárias, e as nossas experiências contribuem para o discernimento do desempenho de serviços e aplicações neste ambiente desafiante. Depois, de forma a melhorar o desempenho dos serviços ao nível da rede sobre o qual um fornecedor disponibiliza um serviço a clientes, é necessário adaptar a topologia lógica da rede a factores tanto externos (e.g. conectividade sem fios, disponibilidade de nós) co, Postprint (published version)

Cloud services in community networks have been enabled by micro-cloud providers. They form community network micro-clouds (CNMCs), which grow organically, i.e. without being planned and optimized beforehand. Services running in community networks face specific challenges intrinsic to these infrastructures, such as the limited capacity of nodes and links, their dynamics and geographic distribution. CNMCs are used to deploy distributed applications, such as streaming and storage services, which transfer significant amounts of data between the nodes on which they run. Currently there is no support given to users for enabling them to chose better or the best option for specific service deployments. This paper looks at the next step in community network cloud service deployments, by taking network characteristics into account when deciding placement of service instances. We propose a service placement algorithm (PASP) that minimizes the service overlay diameter, while fulfilling service specific criteria. First, we characterize with simulations the potential performance gains of our approach. Secondly, we apply our algorithm to deploy a distributed storage service currently used in Guifi.net, and evaluate it in the real production network, assessing the performance and effects of our algorithm. We find that our PASP algorithm reduces the client reading times by an average of 16% (with a max. improvement of 31 %) compared to the currently used organic placement scheme. Our results show how the choice of an appropriate set of nodes, taken from a larger resource pool, can influence service performance significantly., Peer Reviewed, Postprint (published version)

Wireless community networks (CNs) are large-scale, self-organized and decentralized communication infrastructures built and operated by citizens for citizens. CN cloud infrastructures have been recently introduced to run services inside the network, without the need to consume them from the Internet. We have developed a Linux-based distribution code-named Cloudy, which fosters the service deployment and automation in CN clouds. In this paper, we present two ways provisioned by Cloudy to integrate the services and improve the user QoS in these clouds. First, we present a distributed service discovery mechanism that helps users with service quality metrics to choose the best service from a pool of instances. Second, we experiment with a live video streaming service deployed in CN environments, using more than 50 real CN nodes distributed across Europe for the evaluation. Our analysis shows that, tuning the vital parameters of this service as neighborhood peer selection strategy and source node dispersion strategy, improves the video streaming QoS in the CNs. Our results indicate that both ways help the user to experience improved service performance. Automated service selection, needed once the number of micro service providers becomes larger, is the next step that can be built upon our results., QC 20180531

This demo paper presents Cloudy, a Debian-based distribution to build and deploy clouds incommunity networks. The demonstration covers the following aspects: Installation of Cloudy, theCloudy GUI for usage and administration by end users, demonstration of Cloudy nodes and services deployed in the Guifi community network., QC 20160119, CLOMMUNITY

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Wireless community networks (CNs) are large-scale, self-organized and decentralized communication infrastructures built and operated by citizens for citizens. CN cloud infrastructures have been recently introduced to run services inside the network, without the need to consume them from the Internet. We have developed a Linux-based distribution code-named Cloudy, which fosters the service deployment and automation in CN clouds. In this paper, we present two ways provisioned by Cloudy to integrate the services and improve the user QoS in these clouds. First, we present a distributed service discovery mechanism that helps users with service quality metrics to choose the best service from a pool of instances. Second, we experiment with a live video streaming service deployed in CN environments, using more than 50 real CN nodes distributed across Europe for the evaluation. Our analysis shows that, tuning the vital parameters of this service as neighborhood peer selection strategy and source node dispersion strategy, improves the video streaming QoS in the CNs. Our results indicate that both ways help the user to experience improved service performance. Automated service selection, needed once the number of micro service providers becomes larger, is the next step that can be built upon our results., Peer Reviewed, Postprint (author's final draft)

Internet and communication technologies have lowered the costs to collaborate for communities, leading to new services like user-generated content and social computing and, through collaboration, collectively built infrastructures, such as community networks. Community networks are 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. Internet access is often considered the main service of community networks, but the provision of services of local interest within the network is a unique opportunity for community networks, which is currently predominantly unexplored. The consolidation of today's cloud technologies offers community networks the possibility to collectively build community clouds, building upon user-provided networks, and extending towards an ecosystem of cloud services. We propose a framework for building a collaborative distributed community cloud system that employs resources contributed by the members of the community network for provisioning infrastructure and software services. This framework is tailored to the specific social, economic, and technical characteristics of community networks and requirements for community clouds in order to be successful and sustainable. We materialise this framework in the implementation of the Cloudy distribution. We conduct real deployments of these clouds in the Guifi.net community network and evaluate cloud-based applications such as service discovery and distributed storage. This deployment experience supports the feasibility of community clouds and our measurements demonstrate the performance of services and applications running in these community clouds. Our results encourage the development and operation of collaborative cloud-based services using the resources of a community network. We anticipate that such services can effectively complement commercial offers and have the potential to boost in, Peer Reviewed, Postprint (author's final draft)

Community networks are self-organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user-provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end-user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least-authority file system (Tahoe-LAFS), a decentralized storage system with provider-independent security that guarantees privacy to the users. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe-LAFS. We observed that the write operation of Tahoe-LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe-LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe-LAFS can run on community network clouds with suitable performance for the needed end-user experience., Peer Reviewed, Preprint

Community networks [1] are decentralized and self-organized communication networks built and operated by citizens and for citizens. They are an emergent model of infrastructure that aims to satisfy a community's demand for Internet and ICT services. There are several large community networks in Europe having from 500 to 20000 nodes, such as Guifi.net1, AWMN2, FunkFeuer3 and many more worldwide. Most of them are based on Wi-Fi technology, but also a growing number of optical fiber links start to become deployed., Peer Reviewed, Postprint (published version)

Community networks are decentralized communication networks, built and operated by citizens for citizens. Most of these networks originated for enabling Internet access for under-provisioned areas. With the consolidation of cloud technology, there is now also the potential to extend the community networks towards the application level, and deploy within these networks cloud-based applications. In this paper we conduct an experimental deployment of a set of applications on cloud-based infrastructures within community networks. The aim of this study is to understand better the feasibility of running cloud-based applications in the challenging environment of these networks. For our study, we use several nodes inside of two community networks to host our applications and carry out experiments under realistic conditions. The results we obtain with deployments of ownCloud, Tahoe-LAFS and BitTorrent, are a correct functioning of the applications and a promising user experience. Our results could encourage users to contribute and participate in larger scale trials of clouds in community networks, and application developers to envision innovative services based on collective infrastructures., Peer Reviewed, Postprint (published version)

Community-Lab is an open, distributed infrastructure for researchers to carry out experiments within wireless community networks. Community networks are an emergent model of infrastructures built with off-the-shelf communication equipment that aims to satisfy a community's demand for Internet access and ICT services. Community-Lab consists of more than 100 nodes that are integrated in existing community networks, thus giving researchers access to community networks and allowing them to conduct experimental evaluation of routing protocols, services and applications deployed there. Community networks have now the opportunity to extend the collaborative network building to the next level, that is, building collaborative services implemented as community clouds, built, operated and maintained by the community, that run on community-owned heterogeneous resources, and offer cloud-based services that are of the community's interest. This demo paper focuses on demonstrating the cloud extension of Community-Lab, enabling now community cloud experiments. By means of selected applications, we show how Community-Lab has been extended with distributed clouds, where different devices such as server, desktop PCs, low-resource embedded PCs and IoT boards are brought together forming a heterogeneous distributed cloud environment for researchers to experiment in community networks., Peer Reviewed, Postprint (published version)

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 Reviewed, Postprint (author’s final draft)

Community-Lab is an open, distributed infrastructure for researchers to carry out experiments within wireless community networks. Community networks are an emergent model of infrastructures built with off-the-shelf communication equipment that aims to satisfy a community's demand for Internet access and ICT services. Community-Lab consists of more than 100 nodes that are integrated in existing community networks, thus giving researchers access to community networks and allowing them to conduct experimental evaluation of routing protocols, services and applications deployed there. Community networks have now the opportunity to extend the collaborative network building to the next level, that is, building collaborative services implemented as community clouds, built, operated and maintained by the community, that run on community-owned heterogeneous resources, and offer cloud-based services that are of the community's interest. This demo paper focuses on demonstrating the cloud extension of Community-Lab, enabling now community cloud experiments. By means of selected applications, we show how Community-Lab has been extended with distributed clouds, where different devices such as server, desktop PCs, low-resource embedded PCs and IoT boards are brought together forming a heterogeneous distributed cloud environment for researchers to experiment in community networks., Peer Reviewed, Postprint (published version)