面向服务的物联网系统功能可扩展性的分散数据流

Damian Arellanes, K. Lau, R. Sakellariou
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引用次数: 2

摘要

在计算资源的背景下,水平和垂直可扩展性已经得到了广泛的研究。然而,随着连接对象数量的指数级增长,功能可扩展性(就软件系统的规模而言)正迅速成为构建高效的面向服务的物联网系统的核心挑战,这些系统可以持续生成大量数据。随着系统的扩展,在服务之间移动数据的集中式方法变得不可行,因为它会导致单个性能瓶颈。分布式方法避免了这种瓶颈,但由于数据流要经过多个中介,它会产生额外的网络流量。分散的数据交换是实现完全高效的物联网系统的唯一解决方案,因为它避免了单一的性能瓶颈,并极大地减少了网络流量。在本文中,我们提出了一种功能可扩展的方法,将数据和控制分离,以实现面向服务的物联网系统中的分散数据流。我们的方法经过经验评估,结果表明,与分布式数据流相比,通过大幅减少数据流和网络流量的数量,它可以很好地扩展物联网系统的规模。
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Decentralized Data Flows for the Functional Scalability of Service-Oriented IoT Systems
Horizontal and vertical scalability have been widely studied in the context of computational resources. However, with the exponential growth in the number of connected objects, functional scalability (in terms of the size of software systems) is rapidly becoming a central challenge for building efficient service-oriented IoT systems that generate huge volumes of data continuously. As systems scale up, a centralised approach for moving data between services becomes infeasible because it leads to a single performance bottleneck. A distributed approach avoids such a bottleneck but it incurs additional network traffic as data streams pass through multiple mediators. Decentralised data exchange is the only solution for realising totally efficient IoT systems, since it avoids a single performance bottleneck and dramatically minimises network traffic. In this paper, we present a functionally scalable approach that separates data and control for the realisation of decentralised data flows in service-oriented IoT systems. Our approach is evaluated empirically, and the results show that it scales well with the size of IoT systems by substantially reducing both the number of data flows and network traffic in comparison with distributed data flows.
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