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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.

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.

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.

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.

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, while 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 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 also in laboratory network. We quantify the performance, 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., Comment: arXiv admin note: substantial text overlap with arXiv:1804.00561

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.

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.

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.

Recently, mesh networking and blockchain are two of the hottest technologies in the telecommunications industry. Combining both can reformulate internet access and make connecting to the Internet not only easy, but affordable too. 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, quantify its performance, bottlenecks and limitations of the current implementation. 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., Comment: 6 pages, 6 figures, Submitted to cryblock workshop Mobisys 2018

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. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Recent years have seen a trend towards decentralisation - from initiatives on decentralized web to decentralized network infrastructures. In this position paper, we present an architectural vision for decentralising cloud service infrastructures. Our vision is on community cloud infrastructures on top of decentralised access infrastructures i.e. community networks, using resources pooled from the community. Our architectural vision considers some fundamental challenges of integrating the current state of the art virtualisation technologies such as Software Defined Networking (SDN) into community infrastructures which are highly unreliable. Our proposed design goal is to include lightweight network and processing virtualization with fault tolerance mechanisms to ensure sufficient level of reliability to support local services.

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.

© 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.

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

Community networks are decentralized communication networks built and operated by citizens, for citizens. We consider service discovery for clouds in such community networks. The currently employed solutions for service discovery are static and are not able to follow the dynamics in cloud service provision. We propose a novel service discovery mechanism based on the common Linux tool Avahi combined with TincVPN, in which the VPN allows Avahi to reach nodes beyond the local link. The concept of the micro-cloud is introduced which contains the nodes reached with the extended broadcast domain of Avahi. We evaluate the performance of the proposed service discovery mechanism in a real community cloud deployment consisting of 25 geographically distributed nodes. In experiments with different settings and number of services, we measure the number of services discovered as a function of the discovery time. Our results show that while a client eventually discovers all the services, a significant time is needed to achieve a complete service discovery. The proposed mechanism therefore seems appropriate for the case of community clouds with many replicas of a sought service, where the fast discovery of just a few providers satisfies the requirements of a client. By applying standard Linux tools, an advantage of our solution is that it is by default available on most Linux distribution, which allows our solution to be easily used for real deployments.

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 innovation in application areas in which end-user involvement is required.

Community networks are successful large scale, decentralized IP networks, built and operated by citizens for citizens. Cloud computing infrastructures present in today's Internet hardly exist in community networks. But the demand for cloud storage, and in particular secure cloud storage, increases also from within the communities. We deploy in community networks Tahoe-LAFS, a decentralized storage system with provider-independent security that guarantees privacy to the users. The correct functionality of Tahoe-LAFS in the community network is our first performance evaluation goal. Secondly, acceptable user experience is needed in order to make users willing to join the cloud, to create an eco-system around this cloud storage service. Our approach for the performance evaluation is to reproduce in our experimental scenario the real use case. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over a WAN setting on cloud nodes in a community network. In experiments we characterize with different file sizes the upload and download times of Tahoe-LAFS, in relation to the bandwidth and latency between the client and the storage nodes. While we observe higher upload and faster download times, Tahoe-LAFS performed correctly in the challenging environment of the community network. Our results suggest Tahoe-LAFS as a promising application for privacy-preserving, secure and fault-tolerant cloud-based storage services in community networks.

Community networks are decentralized communication networks built and operated by citizens, for citizens. Most users see in community networks only the possibility to gain Internet access, while we propose clouds in community networks as the real opportunity: different to the general purpose cloud-based applications offered in the Internet, community clouds would allow providing cloud-based services that are relevant for the community and that are shaped and owned by the community. It is in favour of our vision that today's cloud management systems and applications have consolidated and can run on commodity hardware, making them now ready for potential deployment and usage in community networks. The experience that we report in this paper is on a real distributed cloud that we have permanently running within a community network, where for our experiments two distributed file systems were deployed over very heterogeneous distributed cloud resources that are part of the system. Tahoe-LAFS and Xtreem FS were evaluated where the distributed storage nodes are provided by KVM-based VMs from Proxmox and Open Stack cloud management platforms, by Linux containers (LXC) from a community resource management platform and even from storage space on IoT embedded boards. Furthermore, we implement a service discovery and publishing mechanism that automatically publishes and discovers available services (e.g. Distributed storage service) of a cloud node to all the other nodes. We compared the performance of Tahoe-LAFS and Xtreem FS in this highly diverse settings and under the dynamic conditions of the community network. While both file system performed functionally correct, since Tahoe-LAFS offers end-to-end encryption by default and fault-tolerance to churn of nodes, it seems to be able to be a solution for important use cases for storage in community networks where privacy of data is important.

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.

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.

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.

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.

Seamless computing and service sharing in community networks (CNs) have gained momentum due to the emerging technology of community network micro-clouds (CNMCs). However, deploying and running services in CNMCs confront enormous challenges to cope with, such as the dynamic nature of micro-clouds, limited capacity of nodes and links, asymmetric quality of wireless links, geographic singularity based deployment model rather than network QoS based, etc. CNMCs have been increasingly used by network-intensive services which exchange significant amounts of data between nodes, therefore their performance heavily relies on the available bandwidth resource in a network. This paper proposes a novel bandwidth-aware service placement algorithm which aims to replace the current random placement adopted by Guifi.net. Our experimental results show that the proposed BASP algorithm consistently outperforms the random placement in Guifi.net by 35% regarding its bandwidth gain. More promisingly, as the number of services increases, the gain tends to increase accordingly.

© 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.

Edge computing is shaping the way to run services in the Internet by allowing the computations to become available close to the user's proximity. Many implementations have been recently proposed to facilitate the service delivery in data centers and distributed networks. However, we argue that those implementations cannot fully support the operations in Community Mesh Networks (CMNs) since the network connection is highly intermittent and unreliable. In this paper, we propose PiCasso, a novel multi-access edge computing platform that combines the advances in lightweight virtualisation and Information-Centric Networking (ICN). PiCasso utilises in-network caching and name based routing of ICN to optimise the forwarding path of service delivery. We analyse the data collected from Guifi.net, the biggest CMN worldwide, to develop smart heuristic for the service deployment. Through a real deployment in Guifi.net, we show that our service deployment heuristic HANET (HArdware and NETwork Resources), improves the response time up to 53% and 28.7% for stateless and stateful services respectively. Finally, using PiCasso for the service delivery in Guifi.net, we achieve 43% traffic reduction compared to the traditional host-centric communication.