Evaluation of Autoscaling Metrics for (stateful) IoT Gateways Wide adoption of Internet of Things solutions in the industry requires IoT platforms to support longrunning, stateful protocols with numerous connections to ingest the measurements, and at the same time to provide the highest throughput possible to achieve service level objectives (SLOs). The challenge of providing sufficient throughput can be solved via automated horizontal scalability for IoT platformsu0027 gateways. Firstly, to address this challenge, a study of the stateoftheart solutions and approaches adopted in the industry to ensure predictable throughput at IoT gateways was conducted. Secondly, the following three gateways autoscaling metrics were studied on the inhouse developed cloudnative IoT Platform: CPU utilization, the number of concurrent active connections, and the absolute throughput per gateway. The results of the experimental evaluation suggest the combination of rate limiting and active connection monitoring as a prerequisite for high throughput at IoT gateways.

Design and Evaluation of Decentralized Scaling Mechanisms for Stream Processing Over the last decade, stream processing (SP) gained momentum as an efficient paradigm dealing with the realtime processing of large streams of data. As mature as they are, mainstream stream processing engines are not ready to be used over more distributed platforms that are today emerging such as Fog platforms. Streams being produced at the edge of the platform, and edges being composed of less powerful compute nodes, a decentralized approach of Stream Processing is needed. This paper moves a step towards decentralized SP by proposing a decentralized autoscaling mechanism for SP applications. Autoscaling refers to the ability for a system to automatically scale in and out as the load varies. As a stream processing application is generally composed of a set of operators in a pipeline, our protocol lets each operatoru0027s replica take scaling decisions and enforce them independently. Assuming a fully decentralized setting, we consider each replica has only a partial view of the graph of operators. Then, without care, concurrent distributed scaling could lead to inconsistencies in replicasu0027 views and to data loss. To address this issue, we devise a maintenance protocol ensuring the consistency of replicasu0027 views, no data loss and no downtime during reconfigurations. Finally, the paper describes the early experimentations conducted with a software prototype of a decentralized SP engine over a computing cluster, showing the usability of the mechanism.