Prototyping Incentive-Based Resource Assignment for Clouds in Community Networks

Journal of Computer and System Sciences

2015

Self Cite

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. In simulations of large scale community cloud scenarios we study the behaviour of the incentive mechanism in different configurations. Our evaluation gives insight into how the developed mechanisms 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 collaborative cloud services in community networks.

Section: Architecture and Designmentioning

confidence: 99%

Incentive-based resource assignment and regulation for collaborative cloud services in community networks

Khan

Büyükşahin

Journal of Computer and System Sciences

2015

Self Cite

“…These applications need to differentiate from the generic cloud services available over the Internet. For example, FreedomBox 2 and MeshNet 3 projects focus on ensuring privacy, and FI-WARE CoudEdge 4 and ownCloud 5 let cloud applications consume local resources.…”

Section: Utilitymentioning

confidence: 99%

Section: V C O L L a B O R At I V E D I S T R I B U T E D A R C H mentioning

confidence: 99%

“…With the cloud coordinator, the infrastructure service can provide a unified view of the resources contributed by multiple local clouds. When federating multiple local clouds, the cloud coordinator applies a peering regulation mechanism [4], [5] fed by the economic engine and social engine to perform resource allocation. The cloud coordinator can consist of multiple components, some of which are indicated in Figure 4.…”

Section: V C O L L a B O R At I V E D I S T R I B U T E D A R C H mentioning

confidence: 99%

“…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 community network [9], [10], and investigating the performance of cloud services in these realworld settings [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Distributed Architecture for Collaborative Cloud Services in Community Networks

Khan

Selimi

2014 International Conference on Intelligent Networking and Collaborative Systems

2014

Self Cite

Abstract-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.Index Terms-cloud computing; community cloud; community networks; collaborative resource sharing I . I N T R O D U C T I O NThe recent developments in information and communication technologies have significantly reduced the barriers for communication, coordination and collaboration for individuals and communities. This not only gave rise to widely adopted applications like social networking and user-generated content among many others, but infrastructures based on a cooperative model have also been built, for example community wireless mesh networks [1], which gained momentum in early 2000s in response to limited options for network connectivity in rural and urban communities. Using off-the-shelf network equipment and open unlicensed wireless spectrum, volunteers teamed up to invest, create and run wireless networks in their local communities as an open telecommunication infrastructure based on self-service and self-management by the users. These community networks have proved quite successful, for example Guifi.net 1 provides wireless and optical fibre based broadband 1 http://guifi.net access to more than 20,000 users. Current community networks use mainly wireless technology to interconnect nodes. With the commoditization of optical fibre, some community networks however have also started providing broadband services combining both technologies. Community networks are a successful case of resource sharing among a collective, where resources shared are not only the networking hardware but also the time, effort and knowledge contributed...

Performance evaluation of a distributed storage service in community network clouds

Selimi

Concurrency and Computation

Cerdí-Alabern

et al. 2015

Self Cite

Community networks are decentralized communication networks built and operated by citizens, for citizens. The consolidation of todays cloud technologies offers now for community networks the possibility to collectively built community clouds, building upon user-provided networks and extending towards 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-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. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, in order 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, but 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.