Efficient and scalable execution of smart city parallel applications

Abstract

Recent research efforts in the field of urban computing aim to develop innovative services for citizens through the application of ubiquitous and pervasive computing paradigms in urban spaces.Smart city applications need to cope with a large number of involved users and devices. Since data and objects are strictly related to the territory on which they are defined and used, it is preferable, when possible, to perform computation locally through the adoption of dispersed computing nodes such as CPU-equipped sensors. In this context, the computation related to smart city applications can be profitably and efficiently parallelized by partitioning the territory into regions and assigning the computation related to each single region to a local node. Nevertheless, the adoption of parallel computing models poses several communication and synchronization issues, especially when the number of nodes is large and the time constraints of applications are compelling. This paper presents and analyzes a parallel computing model for smart city applications in which each node needs to exchange information only with a subset of neighbor nodes, allowing the synchronization overhead to be significantly reduced. As sample application, we consider the analysis and prediction of internet traffic generated by vehicle and pedestrian devices moving on a smart avenue equipped with distributed computing nodes. This work offers a detailed performance evaluation in a number of scenarios, including uniform and nonuniform user distribution and different types of user mobility behavior. The results show that the presented computation model offers notable advantages in terms of computation efficiency and speedup, with respect to a classical all-to-all synchronization paradigm, in which the nodes need to coordinate with a central entity. KEYWORDSparallel computation, smart city, synchronization, urban computing INTRODUCTIONThe widespread diffusion of sensing technologies and large-scale computing infrastructures has enabled the collection of big and heterogeneous data that are daily produced in urban spaces. Such data are pertaining to the mobility of people or vehicles, air quality, safety issues, water/electricity consumptions, etc, and represent useful resources to improve urban services and environments. This is stimulating several research efforts in the field of urban computing, an interdisciplinary scientific field that pertains to the study and application of computing technology in urban environments. 1-3 In particular, the smart city paradigm aims to plan and design future urban territories in a more efficient way. Smart cities rely on the adoption of IT technologies, sensors, Web cams, databases, IoT systems, ubiquitous devices, wireless networks, and all those frameworks that are used for sensing cities and territories, for collecting data and for acting on the basis of the applications logic. In order to process the big amount of data being produced, smart cities are supposed to use multiple hardware and software technologies includi...

Large Scale Cooperative Virtual Environments

“…The paper Efficient and scalable execution of smart city parallel applications presents an analysis and prediction of internet traffic generated by vehicle and pedestrian devices moving across a “smart avenue”. The authors consider smart city applications requiring that the computation regarding a region of the city is performed using the information received from a subset of neighbor regions.…”