2020
DOI: 10.1109/jiot.2020.2964405
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Adaptive Microservice Scaling for Elastic Applications

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Cited by 37 publications
(16 citation statements)
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References 23 publications
(69 reference statements)
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“…Coulson et al [6] developed a prototype based on micro-service for web application by autoscaling process and evaluated it for prediction using supervised machine learning. Wang et al [7] mentioned an elastic scheduling model that can solve task scheduling of different micro-services in cloud-based computing resources.…”
Section: A Microservices In Various Sectors and Cia Security Strategymentioning
confidence: 99%
“…Coulson et al [6] developed a prototype based on micro-service for web application by autoscaling process and evaluated it for prediction using supervised machine learning. Wang et al [7] mentioned an elastic scheduling model that can solve task scheduling of different micro-services in cloud-based computing resources.…”
Section: A Microservices In Various Sectors and Cia Security Strategymentioning
confidence: 99%
“…For microservices, request processing typically follows a deterministic dataflow-graph, instead of one with conditional execution paths as in inference serving systems. Machine learning methods are also employed for workload prediction in microservice systems for better autoscaling [51], [52]. Recently, DAGOR [14] provided overloading detection and collaborative load shedding in microservice systems.…”
Section: Related Workmentioning
confidence: 99%
“…By leveraging Docker containers, instant services can be implemented with lower overhead than through operating-system virtualization [19]. These containers run on a cluster-management infrastructure such as Apache Mesos to manage load balancing between containers in the cluster [20].…”
Section: A Microservices Architecturementioning
confidence: 99%