Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/8052E48F-67F0-4602-AED0-74F947712C97 | ||
| Year | 2022 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Vasileios Skevakis, "Fuzzy service placement strategies in Kubernetes", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.92796 | ||
| Appears in Collections |
The continuous rise of microservices-based architectures in application development provided the need for tools that orchestrate containerized applications deployed in cloud infrastructures, such as Kubernetes. A Kubernetes Cluster hosts a finite number of Nodes (VMs), and the application's services are packed in Pods and placed in the Kubernetes Nodes. The default Kubernetes Service Placement is static and does not adapt to workload changes, so the default placement solutions are sub-optimal. Pods must be placed in their respective Nodes in a way that minimizes Node-to-Node (egress) traffic. That way, the infrastructure cost is optimized, and the response time of the requests is minimized since egress traffic is slower than ingress. Modern application workloads require some high-utilized services to run in more than one instance. In this work, the service placement problem is handled as a graph clustering one, aiming to optimize the application by both cost and response time. The graph clustering needs to be fuzzy, to allow a graph's node (Kubernetes Pod) to belong in more than one partition (Nodes). Two applications were deployed in the Kubernetes infrastructure of the Google Cloud Platform to support our fuzzy service placement solution in real use cases (an e-commerce eShop and an IoT architecture). The experimental results demonstrate that our fuzzy placement solution can significantly reduce both the total requested traffic between Nodes and the response time of the applications' requests compared to the placement using the default Kubernetes Scheduler's method. At the same time, our solution can maintain the monetary cost savings of previous related work on cost-optimization in Kubernetes.
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