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In community networks, individuals and local organizations 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. Most current community networks use wireless links for the node interconnection, applying o...
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... community mesh network generally includes different node types and each type plays a different role in the network. For example, Guifi.net [4], which is considered the largest meshed community network worldwide, includes two main types of nodes, according to [3]: terminal nodes which rep- resent the end user nodes, and hubs which serve traffic to end users. In this network, each terminal node has a unique connection to a hub that routes traffic, and hubs can have many connected terminal nodes [3]. The architecture of a community cloud has to consider the topology of the community network which the cloud will be deployed on. Considering the typical community nodes explained above and the analysis of the community network topology [3], a hierarchical architecture [5] for community clouds is suggested. In this architecture, each super node is responsible for the management of a set of attached nodes. From the perspective of the attached nodes, these super nodes act as a centralized unit to manage the cloud services. These super nodes connect physically between other super nodes and logically in an overlay network to other cloud managing nodes. This hierarchical architecture can be classified into the two main classes of fully decentralized and centralized systems [5]. If the design of the architecture is done towards a centralized systems, advantages include efficient search and control, while if it is decentralized, load-balancing, robustness and failure tolerance would be the benefits. There are several large- scale distributed applications that using a hierarchical design achieved great success, such as Kazaa and Skype. Figure 2 depicts the overlay network that results from the hierarchical architecture of the community cloud, having ordinary nodes (ON) and super nodes (SN). ONs behave both as provider and requester in the cloud system. That means, at different times they can both request a resource or provide a resource. SNs are dedicated machines which are mainly responsible for coordinating and managing the ONs. 2) Super Nodes (SNs): Each SN is responsible for a set of ONs and stores their metadata in a structure called SN_ONList . This list has to be updated after each resource sharing operation. Besides, each SN has another list called SN_SNList which holds the metadata of other SNs. This list is refreshed periodically to update which SN can supply how much amount of resources. For this purpose, each SN publishes their own status to other SNs, e.g. by gossiping. 3) 1) 2) Community Ordinary Super Nodes Nodes Cloud (SNs): (ONs): Manager: Each SN Each is Deploying responsible ON is assigned community for a set to of a clouds SN ONs called and in stores a parent-SN wireless their metadata mesh and holds network in a structure the will necessary called require SN_ONList information installing . This a about community list it. In has addition, to cloud be updated manager each ON after software maintains each resource [6] locally on super a sharing list nodes. called operation. Figure 3 ON_SNList Besides, shows which the each contains generic SN has the architecture another metadata list of of called other this community SN_SNList SNs. With which cloud the information manager holds the software. in metadata this list, The of an ordinary other extended SNs. nodes registration This form list the is of refreshed physical an ON periodically layer in the of system the cloud to can update in done. a which wireless SN mesh can supply network. how The much core amount layer of contains When resources. the an software ON For needs this for purpose, some managing resources, each and SN monitoring it publishes sends a the request their virtual own to status machines its parent-SN. to other on ordinary SNs, Moreover, e.g. nodes. by ONs gossiping. The periodically front end send layer a heartbeat provides the message interface to their to the parent-SN infrastructure to inform services about (Infrastructure-as- their aliveness a-Service, and inform IaaS) about provided their current by the status. super node. The cloud co-ordinator connects with multiple super nodes in a mesh network to form federated super node clouds [6]. The co-ordinator is responsible for interchanging the capability and utilization of cloud resources and applies the incentive mechanism for the resource allocation. 3) Community Cloud Manager: Deploying community clouds in a wireless mesh network will require installing a community cloud manager software [6] on super nodes. Figure 3 shows the generic architecture of this community cloud manager software. The ordinary nodes form the physical layer of the cloud in a wireless mesh network. The core layer contains the software for managing and monitoring the virtual machines on ordinary nodes. The front end layer provides the interface to the infrastructure services (Infrastructure-as- a-Service, IaaS) provided by the super node. The cloud co-ordinator connects with multiple super nodes in a mesh network to form federated super node clouds [6]. The co-ordinator is responsible for interchanging the capability and utilization of cloud resources and applies the incentive mechanism for the resource allocation. III. I NCENTIVE M ECHANISM IN C OMMUNITY C LOUD Since the community networks are mainly based on voluntariness of participants, incentives to create this voluntariness is crucial to achieve the sustainability of the system [7], [8]. Our community cloud manager therefore needs to apply in resource allocation an incentive mechanism which encourages users to contribute to the system. This contribution will create a feedback loop in which increased utilization of the system would lead to interest and sustainability. We identify the following requirements for the needed incentive mechanism: • Decentralization: Since a dedicated centralized reposi- tory might not be available in community cloud, incentive mechanism should be decentralized and self-managing. • Adaptability: When the incentive mechanism is deployed on community network, this should not affect the principles community networks are built upon. • Generic: Many services and applications may run on community clouds. The incentive mechanism should ef- ficiently support high service diversity. • Rewarding: The altruistic users which contribute resources to the cloud system should be rewarded. Effort- or contribution-based rewards are options. • Lightweight: The cost of executing the incentive mechanism in terms of overhead should be low. • Fairness: The incentive mechanism has to take into account the user’s physical limitation with regard to the contribution made before assessing it. Therefore, users should be compared in the equity conditions. • Maximization of social welfare : The incentive mechanism should focus on increasing the social welfare rather than benefiting a small set of nodes. According the these listed requirements, we propose an effort-based incentive mechanism which applies reciprocity- based resource allocation. This mechanism is inspired by the Parecon economic model [9], and effort-based incentives [10]. In this model, nodes’ rewards are calculated based on how much effort they put in when contributing to the system. It is assumed that SNs correspond to more powerful machines which can handle the needed number of request from ONs and are enabled to monitor them. Resources can be virtual machines which are requested. ONs can be heterogeneous nodes in terms of capacity and shared sources and have asymmetric resource requests. Each user’s node has a credit which reflects its contribution to the system. The credit of a node CR i depends on the amount of resources R i the node shared and the cost of transaction which is calculated by the time T i during which these resources SR i were shared. If the corresponding node shares resources, the transaction cost is added to its ...
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Citations
... Reciprocity-based mechanisms for sharing storage and computing resources are reported in [14], [13] and [64]. In [14] and [13], the reciprocity-based mechanism is implemented over a Community Cloud made out of shared computational resources of the network members and is based on records of participants' efforts. ...
... Reciprocity-based mechanisms for sharing storage and computing resources are reported in [14], [13] and [64]. In [14] and [13], the reciprocity-based mechanism is implemented over a Community Cloud made out of shared computational resources of the network members and is based on records of participants' efforts. Results indicate that the most suitable structure for community clouds should distinguish between ordinary nodes that possess cloud resources and super nodes that are responsible for the management of resource sharing. ...
... Cloud computing infrastructures can be developed in various ways but face severe challenges due to the nature of CNs i.e., hardware and software diversity with various options for inexpensive material, decentralized management where users contribute and manage their own resources and rapid changes in the number of contributing nodes. The idea of developing a distributed Community Cloud that follows the topology of CNs is proposed in [14]. The goal is to regulate consumption and contribution of participant resources in the community cloud in accordance to one's level of contribution. ...
Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications' ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse ways that CN operators pursue the sustainability goal. Depending on the actual context of the term, these range all the way from mechanisms to fund their activities and synergistic approaches with commercial service providers, to organizational structures and social activities that serve as incentives to maximize the engagement of their members. Finally, we iterate and discuss theoretical concepts of incentive mechanisms that have been proposed in the literature for these networks as well as implemented tools and processes designed to set the ground for CN participation. While, theoretical mechanisms leverage game theory, reputation frameworks, and social mechanisms, implemented mechanisms focus on organizational matters, education and services, all aiming to motivate the active and sustained participation of users and other actors in the CN.
... Secondly, we propose a set of technical, social and economic policies that, if placed in community networks, should accelerate the uptake and help the sustainability of community clouds. In our earlier work, we have explored how incentive-based resource regulation Buyuksahin et al. 2013) and economic policies can affect collaboration among the members of community networks, and how the scalability issues can affect the design of a community cloud system (Khan, Sharifi, et al. 2013). We have looked into potential distributed architecture for community cloud (Khan, Selimi, et al. 2014), and we are also building a prototype system to be deployed in Guifi.net ...
Internet and communication technologies have lowered the costs of enabling individuals and communities to collaborate together. This collaboration has provided new services like user-generated content and social computing, as evident from success stories like Wikipedia. Through collaboration, collectively built infrastructures like community wireless mesh networks where users provide the communication network, have also emerged. Community networks have demonstrated successful bandwidth sharing, but have not been able to extend their collective effort to other computing resources like storage and processing. The success of cloud computing has been enabled by economies of scale and the need for elastic, flexible and on-demand provisioning of computing services. 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. We explore in this paper how social and economic mechanisms can play a role in overcoming the barriers of voluntary resource provisioning in such community clouds, by analysing the costs involved in building these services and how they give value to the participants. We indicate socio-economic policies and how they can be implemented in community networks, to ease the uptake and ensure the sustainability of community clouds.
... Such an archi-tecture tailored to the specific situation and social and economic context of the community networks allows the collaborative cloud services to better fit the demands of local communities, facilitating adoption and uptake of community cloud model. In our earlier work, we have explored how incentive-based resource regulation [4]- [6] and economic policies [7] can affect collaboration among the members of community networks, and how the scalability issues can affect the design of a community cloud system [8]. We are also building a prototype system to be deployed in Guifi.net ...
... Such cloud management systems can be tailored for community networks by extending the existing functionality to address the particular conditions of community networks. For example, incentive mechanisms inspired by the social nature of community networks can be built into resource regulation component to encourage users to contribute resources [4]- [6]. ...
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.
... As detailed in [8], [9], we propose an incentive mechanism which applies reciprocity-based resource allocation. This is inspired by the Parecon economic model [10], [11] which focuses on social welfare by considering the inequality between nodes. ...
... For each ON which provides VMs, the SN calculates the transaction cost and adds it to that ON's credits, while the cost is deducted from the consumer ON's credits. Once the operation is completed, the effort for each ON involved in the transaction is recalculated as in [8] by: ...
... We have implemented a prototype of the incentive-based regulation mechanism that was proposed in [8], [9]. We implemented the components in the Python programming language and used CouchDB 7 as database. ...
Wireless community networks are a successful example of a collective where communities operate ICT infrastructure and provide IP connectivity based on the principle of reciprocal resource sharing of network bandwidth. This sharing, however, has not extended to computing and storage resources, resulting in very few applications and services which are currently deployed within community networks. Cloud computing, as in today's Internet, has made it common to consume resources provided by public clouds providers, but such cloud infrastructures have not materialized within community networks. We analyse in this paper socio-technical characteristics of community networks in order to derive scenarios for community clouds. Based on an architecture for such a community cloud, we implement a prototype for the incentive-driven resource assignment component, deploy it in a testbed of community network nodes, and evaluate its behaviour experimentally. Our evaluation gives insight on how the deployed prototype components regulate the consumption of cloud resources taking into account the users' contributions, and how this regulation affects the system usage. Our results suggest a further integration of this regulation component into current cloud management platforms in order to open them up for the operation of an ecosystem of community cloud.
... Most peer-to-peer (P2P) systems implement incentive mechanisms based on contribution where nodes are rewarded according to resources they donate to the system [9]. We suggest an effort-based incentive mechanism for community cloud where effort is defined as contribution relative to the capacity of a node [10]. This mechanism is inspired by the Parecon economic model111213 which focuses on social welfare by considering inequality among nodes. ...
... The cost is deducted from the credits of the node that consumed the resources. After the transaction is completed, the effort for each node involved in the transaction is recalculated as in [10] by: ...
... In the past work [10], we studied incentive mechanisms for resource regulation within a single SN zone which corresponds to local community cloud scenario. Here we extend our simulator to study resource regulation across multiple SN zones covering both local and federated community cloud scenarios. ...
Community networks are built with off-the-shelf communication equipment aiming to satisfy a community's demand for Internet access and services. These networks are a real world example of a collective that shares ICT resources. But while these community networks successfully achieve the IP connectivity over the shared network infrastructure, the deployment of applications inside of community networks is surprisingly low. Given that community networks are driven by volunteers, we believe that bringing in incentive-based mechanisms for service and application deployments in community networks will help in unlocking its true potential. We investigate in this paper such mechanisms to steer user contributions, in order to provide cloud services from within community networks. From the analysis of the community network's topology, we derive two scenarios of community clouds, the local cloud and the federated cloud. We develop an architecture tailored to community networks which integrates the incentive mechanism we propose. In simulations of large scale community cloud scenarios we study the behaviour of the incentive mechanism in different configurations, where slices of homogeneous virtual machine instances are shared. Our simulation results allow us to understand better how to configure such an incentive mechanism in a future prototype of a real community cloud system, which ultimately should lead to realisation of clouds in community networks.
Development and continuous operation of network management systems is a major challenge to future networks, where a large number of semi-independent devices jointly try to realize a working network. This work considers network testbed management, and more specifically on testbed management software for community networks. Because of the inherently unstructured and chaotic nature of community networks, managing components inside a community network with a frequently varying and unpredictable performance is particularly challenging. This paper focuses on the verification of community network testbed control software which has to cope with these challenges. We show how the application of a container-based unit testing approach has a positive impact on development efforts and testbed stability.
Community networks offer a shared communication infrastructure where communities of citizens build and own open networks. While the IP connectivity of the networking devices is successfully achieved, the number of services and applications available from within the community network is typically small and the usage of the community network is often limited to providing Internet access to remote areas through wireless links. In this paper we propose to apply the principle of resource sharing of community networks, currently limited to the network bandwidth, to other computing resources, which leads to cloud computing in community networks. Towards this vision, we review some characteristics of community networks and identify potential scenarios for community clouds. We simulate a cloud computing infrastructure service and discuss different aspects of its performance in comparison to a commercial centralized cloud system. We note that in community clouds the computing resources are heterogeneous and less powerful, which affects the time needed to assign resources. Response time of the infrastructure service is high in community clouds even for a small number of resources since resources are distributed, but tends to get closer to that of a centralized cloud when the number of resources requested increases. Our initial results suggest that the performance of the community clouds highly depends on the community network conditions, but has some potential for improvement with network-aware cloud services. The main strength compared to commercial cloud services, however, is that community cloud services hosted on community-owned resources will follow the principles of community network and will be neutral and open.
