Faculty Publications
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Publications by NITK Faculty
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Item Novel energy efficient virtual machine allocation at data center using Genetic algorithm(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from clients in a smart computing environment poses a greater challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can save the energy of data center as well as avoiding the service level agreement (SLA) violation. This paper deals with the design of an energy efficient algorithm for optimized resources allocation at data center using combined approach of Dynamic Voltage Frequency Scaling (DVFS) and Genetic algorithm (GA). The performance of the proposed energy efficient algorithm is compared with DVFS. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy over a specified workload with 0% SLA violation. © 2015 IEEE.Item A novel energy efficient resource allocation using hybrid approach of genetic DVFS with bin packing(Institute of Electrical and Electronics Engineers Inc., 2015) Sharma, N.K.; Guddeti, G.Increased resources utilization from several clients in a smart computing environment poses a key challenge in allocating optimal energy efficient resources at the data center. Allocation of these optimal resources should be carried out in such a manner that we can reduce the energy consumption of the data center and also avoid the service level agreement (SLA) violation. This paper deals with the development of an energy efficient algorithm for optimal resources allocation at the data center using hybrid approach of the Dynamic Voltage Frequency Scaling (DVFS), Genetic algorithm (GA) and Bin Packing techniques. The performance of the proposed hybrid approach is compared with Genetic Algorithm, DVFS with Bin Packing, DVFS without Bin Packing techniques. Experimental results demonstrate that the proposed energy efficient algorithm consumes 22.4% less energy as compared to the DVFS with Bin Packing technique over a specified workload with 0% SLA violation. © 2015 IEEE.Item VirtTorrent: BitTorrent for Inter-VM File distribution(Association for Computing Machinery acmhelp@acm.org, 2016) Kamath, A.A.; Jamadagni, C.; Chandrasekaran, K.A major problem in virtualized cloud datacenters today is the inefficiency of communication between virtual machines, i.e. inter-VM communication. Efforts have gone into optimizing communication between VMs present on the same physical server in the cloud, i.e. co-resident or co-located VMs; however, optimizing communication between VMs present on different physical servers is a separate issue. As these communicate via the TCP/IP network stack, improvements in performance or efficiency are normally proposed by customizations to the communication protocols requisite to the datacenter. We propose a mechanism to increase the average speed of inter-VM file distribution in the cloud for VMs on separate physical servers by implementing a BitTorrent-like peer-to-peer (P2P) system. The proposal, named VirtTorrent, uses direct communication between VMs in the cloud to implement a protocol similar to BitTorrent, allowing files to be distributed swiftly and with minimal network congestion. As inter-VM communication and network congestion are both important issues in the datacenter, we believe that VirtTorrent can make a marked improvement in the scenarios laid out in this paper. © 2016 ACM.Item CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications over Multi-Cloud Environments(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines. © 2013 IEEE.Item Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$NP-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%-30% and electricity cost by approximately 25% compared to the baseline. © 2008-2012 IEEE.Item CSMD: Container state management for deployment in cloud data centers(Elsevier B.V., 2025) Nath, S.B.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance. To increase the consolidation ratio of a cloud data center, in this paper, we propose a system called Container State Management for Deployment (CSMD) to manage the container states. CSMD uses an algorithm to checkpoint the idle containers so that their resources can be released. The new containers are deployed using the released resources in a server. In addition, CSMD uses an algorithm to check the container status periodically, and the containers are resumed from the checkpoint state when the user requests them. We evaluate CSMD in Amazon Elastic Compute Cloud (Amazon EC2) by performing efficient state management of the containers. The experiments in the Amazon cloud show that the proposed CSMD system is superior to the existing algorithms as the proposed system increases the consolidation ratio of data centers. © 2024 Elsevier B.V.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.