Faculty Publications
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Publications by NITK Faculty
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Item GPU-aware resource management in heterogeneous cloud data centers(Springer, 2021) Kulkarni, A.K.; Annappa, B.The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into cloud data centers. With the evolution of Graphics Processing Units, composing of an extensive array of parallel computing single-instruction-multiple-data processors are being considered as a platform for high-performance computing because of their high throughput. Many cloud providers have begun offering GPU-enabled services for the users where GPUs are essential (for high computational power) to meet the desired Quality-of-service. Virtual machine placement and load balancing the GPUs in the virtualized environments like the cloud is still an evolving area of research and it is of prime importance to achieve higher resource efficiency and also to save energy. The current VM placement techniques do not consider the impact of VM workload type and GPU memory status on the VM placement decisions. This paper discusses the current issues with the First Fit policy of virtual machine placement used in VMWare Horizon and proposes a GPU-aware VM placement technique for GPU-enabled virtualized environments like cloud data centers. The experiments conducted using the synthetic workloads indicate reduction in the energy consumption, reduction in search space of physical hosts, and the makespan of the system. It also presents a summary of the current challenges for GPU resource management in virtualized environments and specific issues in developing cloud applications targeting GPUs under the virtualization layer. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item 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.