Browsing by Author "Kumar, M.R."

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    Efficient Kalman filter based deep learning approaches for workload prediction in cloud and edge environments
    (Springer, 2025) Kumar, M.R.; Annappa, B.; Yadav, V.
    Offering cloud resources to consumers presents several difficulties for cloud service providers. When utilizing resources efficiently in cloud and edge contexts, precisely forecasting workload is a crucial problem. Accurate workload prediction allows intelligent resource allocation, preventing needless waste of computational and storage resources while meeting user’s Quality of Service(QoS). In order to mitigate this issue, Kalman filter-based novel hybrid models, including Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BI-LSTM), and Gated Recurrent Unit (GRU), are proposed. These models utilize CNN and attention mechanisms to predict workloads at Edge Servers accurately. The proposed models were extensively evaluated on real world traces like Alibaba_v2018, Materna, Bitbrains, Microsoft Azure_2019 and Planet lab datasets at various time intervals with and without using Kalman filter. The experimental comparison shows that 97%, 82% and 90% reduction in MSE for Alibaba, 73%, 73% and 63% reduction in MSE for Materna, 72%, 63% and 40% reduction in MSE for Planet lab, 95%, 77% and 96% reduction in MSE for Microsoft Azure and 91%, 87% and 91% reduction in MSE for Bitbrains with respect to CPU utilization %. The effectiveness of the proposed forecasting model is validated through statistical analysis using the Friedman and Nemenyi post-hoc tests. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
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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.