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Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review., , , , and . J. Syst. Softw., (2018)A Network Edge Monitoring Approach for Real-Time Data Streaming Applications., , , , and . GECON, volume 10382 of Lecture Notes in Computer Science, page 293-303. Springer, (2016)Quality of Service Assurance for Internet of Things Time-Critical Cloud Applications: Experience with the Switch and Entice Projects., and . IIAI-AAI, page 289-294. IEEE Computer Society, (2017)A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers., , and . Sensors, 18 (9): 2938 (2018)Auto-scaling Applications in Edge Computing: Taxonomy and Challenges., and . BDIOT, page 158-163. ACM, (2017)Dynamic Multi-level Auto-scaling Rules for Containerized Applications., and . Comput. J., 62 (2): 174-197 (2019)Key influencing factors of the Kubernetes auto-scaler for computing-intensive microservice-native cloud-based applications., and . Adv. Eng. Softw., (2020)Real-Time Data-Intensive Telematics Functionalities at the Extreme Edge of the Network: Experience with the PrEstoCloud Project., , , and . COMPSAC (2), page 522-527. IEEE Computer Society, (2018)978-1-5386-2667-2.Implementing time-critical functionalities with a distributed adaptive container architecture., , , and . iiWAS, page 453-457. ACM, (2016)Incremental Learning from Multi-level Monitoring Data and Its Application to Component Based Software Engineering., and . COMPSAC (2), page 378-383. IEEE Computer Society, (2017)978-1-5386-0367-3.