Abstract-In the last years, traffic over wireless networks has been increasing exponentially due to the impact of Internet of Things (IoT). IoT is transforming a wide range of services in different domains of urban life, such as environmental monitoring, home automation and public transportation. The so-called Smart City applications will introduce a set of stringent requirements, such as low latency and high mobility, since services must be allocated and instantiated on-demand simultaneously close to multiple devices at different locations. Efficient resource provisioning functionalities are needed to address these demanding constraints introduced by Smart City applications while minimizing resource costs and maximizing Quality of Service (QoS). In this article, the City of Things (CoT) framework is presented, which provides not only data collection and analysis functionalities but also automated resource provisioning mechanisms for future Smart City applications. CoT is deployed as a Smart City testbed in Antwerp (Belgium) that allows researchers and developers to easily setup and validate IoT experiments. A Smart City use case based on Air Quality Monitoring through the deployment of air quality sensors in moving cars has been presented showing the full applicability of the CoT framework for a flexible and scalable resource provisioning in the Smart City ecosystem.
Abstract. BonFIRE offers a Future Internet, multi-site, cloud testbed, targeted at the Internet of Services community, that supports large scale testing of applications, services and systems over multiple, geographically distributed, heterogeneous cloud testbeds. The aim of BonFIRE is to provide an infrastructure that gives experimenters the ability to control and monitor the execution of their experiments to a degree that is not found in traditional cloud facilities. The BonFIRE architecture has been designed to support key functionalities such as: resource management; monitoring of virtual and physical infrastructure metrics; elasticity; single document experiment descriptions; and scheduling. As for January 2012 BonFIRE release 2 is operational, supporting seven pilot experiments. Future releases will enhance the offering, including the interconnecting with networking facilities to provide access to routers, switches and bandwidth-on-demand systems. BonFIRE will be open for general use late 2012.
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Abstract:The advent of big data analytics and cloud computing technologies has resulted in wide-spread research in finding solutions to the data placement problem, which aims at properly placing the data items into distributed datacenters. Although traditional schemes of uniformly partitioning the data into distributed nodes is the defacto standard for many popular distributed data stores like HDFS or Cassandra, these methods may cause network congestion for data-intensive services, thereby affecting the system throughput. This is because as opposed to MapReduce style workloads, data-intensive services require access to multiple datasets within each transaction. In this paper, we propose a scalable method for performing data placement of data-intensive services into geographically distributed clouds. The proposed algorithm partitions a set of data-items into geodistributed clouds using spectral clustering on hypergraphs. Additionally, our spectral clustering algorithm leverages randomized techniques for obtaining low-rank approximations of the hypergraph matrix, thereby facilitating superior scalability for computation of the spectra of the hypergraph laplacian. Experiments on a real-world trace-based online social network dataset show that the proposed algorithm is effective, efficient, and scalable. Empirically, it is comparable or even better (in certain scenarios) in efficacy on the evaluated metrics, while being up to 10 times faster in running time when compared to state-of-the-art techniques.
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