Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Automating the Selection of Container Orchestrators for Service Deployment
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chaurasia, P.; Nath, S.B.; Addya, S.K.; Ghosh, S.K.
    With the ubiquitous usage of cloud computing, the services are deployed as a virtual machine (VM) in cloud servers. However, VM based deployment often takes more amount of resources. In order to minimize the resource consumption of service deployment, container based lightweight virtualization is used. The management of the containers for deployment is a challenging problem as the container managers need to consume less amount of resources while also catering to the needs of the clients. In order to choose the right container manager, we have proposed an architecture based on the application and user needs. In the proposed architecture, we have a machine learning based decision engine to solve the problem. We have considered docker containers for experimentation. The experimental results show that the proposed system can select the proper container manager among docker compose based manager and Kubernetes. © 2022 IEEE.
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    Multi Criteria Based Container Management in a Geo-Distributed Cluster
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, M.R.; Annappa, B.; Vishnu Teja, M.
    According to Gartner, 95% of workloads will shift to containers by 2025 due to its lightweight feature. Docker is a commonly used container software for binding applications; the container orchestration system Kubernetes (K8s) manages resources seamlessly across Cloud, Fog, and Edge environments through containers. However, Nodes in the cluster introduces the risk of exceeding node capacity thresholds, leading to failures and potential application loss which degrades the Quality of Service (QoS). In this regard, Multi-Criteria Decision Making (MCDM) strategy for ranking the nodes in the cluster is proposed to achieve the migration decision in the Geo-Distributed cluster for both stateful and stateless application servers using K8s. The proposed strategy has achieved a 15.94sec Average service restore time for the Nginx server and 48.99sec for the Zookeeper server. A proactive Deep Learning model BI-LSTM is proposed for resource utilization prediction of the cluster and achieved MAE of 0.01928 and 0.0206 for CPU and Memory utilization. © 2024 IEEE.