Cloud computing is an evolution in IT consumption and delivery which makes available self-management on the Internet with a flexible, pay-as-you-go business model. Within the context of Internet of Things, the MQTT (Message Queuing Telemetry Transport) protocol that is implemented broadly by the applications of "Publish-Subscribe" paradigm has a vital role. However, MQTT brokers are saturated easily if they have to cope with huge and speedy data generated by IoT "chatty" devices. With capability of provisioning/deprovisioning granular virtual resources, Cloud computing empowered MQTT brokers by enabling its elasticity feature. Elasticity helps the brokers deal with a very large variety of data integrated into the IoT every single day. However, there was lack of sturdy benchmarking tools that judge all the aspects of MQTT brokers in order to advocate correct elastic decision-making. This article focuses on the work of benchmarking MQTT by introducing a new developed tool called MQTTBrokerBench. With this tool, users not only can benchmark MQTT brokers but also can specify saturation points where the IoT load makes the brokers be saturated. Those saturation points can be used to set thresholds for elastic decision-making. Furthermore, the article also demonstrates the results acquired by this tool through the experiments on Windows Azure Cloud Platform.
The increasing number of IoT devices leads to the demand for updating them securely, and stably. Without updating there is a high risk of being attack by hackers or malfunction due to outdated packages. To the date of this article, a lot of IoT updating methods were introduced. Among them are updating using VM containers, using packages, or using blockchain. However, some methods take time to recover a failed update while some cases require the minimum downtime of the IoT devices, or even some devices cannot communicate to the control server or cannot perform the rollback themselves after the update failure. In this article, our contribution is implementing different methods to reduce the rollback down time issue including using primary - secondary selection, using git revert, and using blockchain-based revert.
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