Faculty Publications
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Publications by NITK Faculty
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Item Context Aware VM Placement Optimization Technique for Heterogeneous IaaS Cloud(Institute of Electrical and Electronics Engineers Inc., 2019) Kulkarni, A.K.; Annappa, A.Ever increasing demand for cloud adoption is prompting researchers and engineers around the world to make cloud computing more efficient and beneficial for cloud service providers and users. Cloud computing brings profits for all when the cloud infrastructure is used efficiently, and its services are made affordable to businesses of all scales. Managing cloud data center incurs a significant cost, which includes investing in IT infrastructure at the beginning and data center management costs for power, repair, space, and so on at later stages. The power costs are contributing to a significant share in overall data center management costs, and saving in power consumption can help reduce management costs for data center owners. This paper proposes an efficient context-aware adaptive heuristic-based solution for the virtual machine (VM) placement optimization in the heterogeneous cloud data centers. The proposed VM placement technique takes into the account of physical machine characteristics and load (peak and non-peak) conditions in the heterogeneous data centers to save power and also improve performance efficiency for data center owners. The experiments conducted with real cloud workloads and also synthetic workloads against a well-known adaptive heuristic-based technique indicate significant performance improvements and energy saving with our proposed solution. © 2013 IEEE.Item A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment(Institute of Electrical and Electronics Engineers, 2020) Domanal, S.G.; Guddeti, R.M.R.; Buyya, R.In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time. © 2008-2012 IEEE.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 A novel nine-level boost inverter with a low component count for electric vehicle applications(John Wiley and Sons Ltd, 2021) Shiva Naik, B.S.; Yellasiri, Y.; Aditya, K.; Nageswar Rao, B.N.In electric vehicles (EVs), considerable battery cells are cascaded in series for motor driving to improve the output voltage. The series combination of battery cells causes challenges like isolation of faulty cells, voltage unbalance, and slow charge equalization. Therefore, state-of-charge (SOC) and voltage equalization circuits are often used in industries to protect the battery cells. A nine-level inverter circuit with a double voltage boost is proposed to reduce the above issues based on the switch-capacitor (SC) principle. Unique features like self-balancing, voltage boosting are attained, which cannot be achieved through traditional inverters. The proposed topology can operate at a wide range of modulation indices ((Formula presented.)) to produce different voltage levels. The absence of a back-end H-bridge in the proposed circuit offers low voltage stress across the semiconductors. The operating principle, capacitor sizing, and modulation approach are presented. Further, experimental tests are conducted at different loading conditions to verify the performance of the proposed circuit. © 2021 John Wiley & Sons Ltd.Item Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems(Elsevier Ltd, 2023) Nisha, K.S.; Gaonkar, D.N.; Sabhahit, N.S.Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system. © 2022 Elsevier LtdItem A single-source nine-level boost inverter with new optimal switching scheme for EV applications(John Wiley and Sons Ltd, 2024) Aditya, K.; Yellasiri, Y.; Shiva Naik, B.; Nageswar Rao, B.; Panda, A.K.The importance of two-level inverters is well known in EV applications; it contains significant unwanted harmonics in generated voltage. One of the most efficient way to increase power quality is to replace a two-level inverter with a multilevel inverter (MLI). The MLI's essence considerably reduces total harmonic distortion. Eventually, the size of the filter requirement also will minimize. Because of the increased device count and capacitor voltage balance issues, these converters have a slew of reliability concerns. To mitigate these drawbacks, a novel switched-capacitor based nine-level inverter (SC-NLI) structure with a new optimal control switching technique for electric vehicle (EV) applications is proposed in this paper. The proposed SC-NLI structure comprises 10 switches, one diode, and two capacitors. The proposed structure's circuit description, modes of operation, proper component selection, and a new optimal switching scheme are presented. A discussion about the comparative analysis of suggested topology with currently developed MLI structures is presented. In addition, to the simulation results, experimental tests are conducted under various load conditions to evaluate the proposed SC-NLI structure. © 2023 John Wiley & Sons Ltd.