Abstract
In this paper, we report an algorithm that is designed to leverage the cloud as infrastructure to support Internet of Things (IoT) by elastically scaling in/out so that IoT-based service users never stop receiving sensors’ data. This algorithm is able to provide an uninterrupted service to end users even during the scaling operation since its internal state repartitioning is transparent for publishers or subscribers; its scaling operation is time-bounded and depends only on the dimension of the state partitions to be transmitted to the different nodes. We describe its implementation in E-SilboPS, an elastic content-based publish/subscribe (CBPS) system specifically designed to support context-aware sensing and communication in IoT-based services. E-SilboPS is a key internal asset of the FIWARE IoT services enablement platform, which offers an architecture of components specifically designed to capture data from, or act upon, IoT devices as easily as reading/changing the value of attributes linked to context entities. In addition, we discuss the quantitative measurements used to evaluate the scale-out process, as well as the results of this evaluation. This new feature rounds out the context-aware content-based features of E-SilboPS by providing, for example, the necessary middleware for constructing dashboards and monitoring panels that are capable of dynamically changing queries and continuously handling data in IoT-based services.
Highlights
The Internet of Things (IoT) relies on a continuously growing number of interconnected uniquely addressable heterogeneous electronics (UAHE), including sensors, actuators, smart devices, embedded computers, etc., producing tremendous amount of data about the surrounding living environments to nourish an ever-growing number of services [1]
To evaluate how our architecture will operate and deliver messages coming from IoT sensors to all the interested subscribers, we implemented E-SilboPS
Since our aim is not to show that our system is faster that E-StreamHub in some environment but that state repartitioning can be achieved with reasonable performance, we do not think the lack of a direct comparison is a blocking point
Summary
The Internet of Things (IoT) relies on a continuously growing number of interconnected uniquely addressable heterogeneous electronics (UAHE), including sensors, actuators, smart devices, embedded computers, etc., producing tremendous amount of data about the surrounding living environments to nourish an ever-growing number of services [1]. The large-scale nature of IoT-based services can be effectively and efficiently facilitated and supported via utilizing Cloud Computing infrastructures and platforms for providing flexible and extensive computational power, resource virtualization and high-capacity storage for data streams. Data brokerage [2] is a key concept for handling such a massive number of IoT-based services in a cloud environment. Since cloud environments are intrinsically dynamic, the same brokerage system has to handle variability, scale out to cope with new load, and scale in when the peak has passed in order to save resources. OpenStack [3] or OpenNebula [4], offer no more than a queuing system for delivering notifications from components or at best a topic-based publish/subscribe [5,6,7]. The same thing happens with publish/subscribe services available online like Amazon Web Services (AWS) Simple Notification
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