Multi Criteria Based Container Management in a Geo-Distributed Cluster

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.