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A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.

Cryptocurrency applications of distributed ledger methods such as blockchains are now well established, but their implications for more general topics are just beginning to be appreciated. Beyond applications in finance and banking, new applications are emerging in supply chain management, manufacturing, agricultural product tracking, advertising verification, IoT, healthcare and the pharmaceutical industry, among others.This column will explore current and open topics for trust, verification, compliance and security in distributed environments with a specific focus on the current status of standards efforts related to blockchain technologies. Distributed TrustThe idea of a completely stand-alone, autonomous, self-contained, self-valida ting application that does not depend on either immediate or eventual network communica tio n is becoming nearly unthinkable. In the past, keeping something secure usually depended on providing it with isolated defenses, such as placing it in a physical safe or otherwise isolating it from external access. This approach is still a component of some forms of electronic security, such as offline hardware cryptographic modules for certificate authorities, but blockchain methods depend, in contrast, on the idea of independent open verification rather than isolated operation.Distributed methods carry the advantage of being useful in multiple, physica lly separated settings, but require the existence of methods to determine that a given transaction is complete. Blockchains have become popular precisely for the reason that they provide non-centralized, independently verifiable capabilities to ensure the integr ity and consistency of distributed ledgers and the associated transactions.

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In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.Key words meta scheduling . network awareness . peer-to-peer architectures . data intensive . scheduling algorithm BackgroundResource management [1, 2] is a central task in any Grid system. Resources may include "traditional" resources such as compute cycles, network band- J Grid Computing (2007) 5:43-64

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g. OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g. latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

Simulating the Inter-Cloud' (SimIC) is a discrete event simulation toolkit based on the process oriented simulation package of SimJava. The SimIC aims of replicating an inter-cloud facility wherein multiple clouds collaborate with each other for distributing service requests with regards to the desired simulation setup. The package encompasses the fundamental entities of the inter-cloud metascheduling algorithm such as users, meta-brokers, localbrokers, datacenters, hosts, hypervisors and virtual machines (VMs). Additionally, resource discovery and scheduling policies together with VMs allocation, re-scheduling and VM migration strategies are included as well. Using the SimIC a modeler can design a fully dynamic inter-cloud setting wherein collaboration is founded on meta-scheduling inspired characteristics of distributed resource managers that exchange user requirements as driven events in real-time simulations. The SimIC aims of achieving interoperability, flexibility and service elasticity while at the same time introducing the notion of heterogeneity of multiple clouds' configurations. In addition it accepts an optimization of a variety of selected performance criteria for a diversity of entities. The crucial factor of dynamics consideration has implemented by allowing reactive orchestration based on current workload of already executed heterogeneous user specifications. These are in the form of text files that the modeler can load in the toolkit and occurs in real-time at different simulation intervals. Finally, a unique request is scheduled for execution to an internal cloud datacenter host VM that is capable of performing the service contract. This is formally designed in Service Level Agreements (SLAs) based upon user profiling.

(2015) Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework. Concurrency and Computation: Practice and Experience, 27 (8). pp. 1966-1984., DisclaimerThe University of Gloucestershire has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material.The University of Gloucestershire makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.The University of Gloucestershire makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. SUMMARYThe evolution of communication protocols, sensory hardware, mobile and pervasive devices, alongside social and cyber-physical networks, has made the Internet of things (IoT) an interesting concept with inherent complexities as it is realised. Such complexities range from addressing mechanisms to information management and from communication protocols to presentation and interaction within the IoT. Although existing Internet and communication models can be extended to provide the basis for realising IoT, they may not be sufficiently capable to handle the new paradigms that IoT introduces, such as social communities, smart spaces, privacy and personalisation of devices and information, modelling and reasoning. With interaction models in IoT moving from the orthodox service consumption model, towards an interactive conversational model, nature-inspired computational models appear to be candidate representations. Specifically, this research contests that the reactive and interactive nature of IoT makes chemical reaction-inspired approaches particularly well suited to such requirements. This paper presents a chemical reaction-inspired computational model using the concepts of graphs and reflection, which attempts to address the complexities associated with the visualisation, modelling, interaction, analysis and abstraction of information in the IoT.