Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that work in a more general framework. Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods. By using appropriate strategies, our hybrid method can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also either of the individual methods used separately.

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 into 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.

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 off-the-shelf wireless equipment. While IP connectivity over the shared network infrastructure is successfully achieved, the deployment of applications in community networks is surprisingly low. To address the solution of this problem, we propose in this paper a service to incentivize the contribution of computing and storage as cloud resources to community networks, in order to stimulate the deployment of services and applications. Our final goal is the vision that in the long term, the users of community networks will not need to consume applications from the Internet, but find them within the wireless community network.Peer ReviewedPostprint (published version

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.

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.