BHARANIDHARAN G
@thenewcollege.edu.in
Assistant Professor and BCA
The New College, Chennai-14
carrying his PhD Research in the specific area of Green Cloud Computing under the guidance of most respected , Professor, Department of Computer Applications, VISTAS, Chennai after his completion of M.Phil from Vinayaka Mission University, Salem, Tamilnadu. He is actively involved in research to develop a Green framework and to maintain environmental sustainability in cloud infrastructure by provisioning the right resources. He is also currently working as an Assistant Professor in the Department of Computer Applications, The New College (Autonomous), Chennai-14 from 2008-Till date. He was also served as Joint Secretary in the New College Staff Association (NCSA) from 2013-15. Apart from his teaching and research he is a veteran Tamil News Reader in Raj Television, Chennai from 2008 - Till date. His native is Cuddalore which is a coastal town situated near Pondicherry. Cuddalore is famous for silver beach, Paadaleeswarar temple and for renowned schoo
EDUCATION
Ph.D. COMPUTER SCIENCE 2023 VISTAS, PALLAVARAM, CHENNAI VELS UNIVERSITY, CHENNAI
M.Phil. COMPUTER SCIENCE 2011 VINAYAKA MISSION UNIVERSITY, SALEM VINAYAKA MISSION UNIVERSITY, SALEM
M.C.A COMPUTER APPLICATIONS 2007 SRM UNIVERSITY, KATTANKULATHUR, CHENNAI. SRM UNIVERSITY
B.Sc. COMPUTER SCIENCE 2003 COLLEGE OF ARTS&SCIENCE,CUDDALORE MADRAS UNIVERSITY
RESEARCH INTERESTS
Cloud Computing, Internet of Things (IoT) and Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Jayalakshmi Sambandan, Bharanidharan Gurumurthy, and R. Syed Jamalullah
Wiley
Syed Jamalullah R, L. Mary Gladence, G Bharanidharan, and J Abdul Rasheedh
IEEE
Advancements in electroencephalogram (EEG) data processing have enabled the consistent monitoring of the human brain, playing a crucial role in the early detection and prevention of potential health issues. This study analyzes EEG data to identify abnormal signals, particularly those related to Bradykinesia, a neurological disorder associated with an increased risk of Parkinson's disease. Previous research has utilized neural networks, clinical features, and computational methodologies for signal classification. This research proposes using the Parks McClellan best approach and filter order estimation across various frequency bands to extract meaningful characteristics from EEG data. Additionally, the Power Spectral Density (PSD) approach, Fast Fourier Transform (FFT) method, and periodogram are employed to differentiate amplitude bandwidths. The non-linear vector technique enhances classification accuracy through the Peak Signal-to-Noise Ratio (PSNR). The study also explores the evaluation of consistency quotient using mean-square and mean-absolute error methodologies. By calculating the cross-correlation average of bandwidths, the classification accuracy of the Bradykinesia assessment is improved. The research findings, supported by simulated computations and MATLAB algorithmic implementations, validate the effectiveness of the proposed methods. This research contributes to the ongoing efforts in EEG data analysis, enhancing the early detection and intervention of brain health issues.
G Bharanidharan, S Jayalakshmi, and P. Mayilvahanan
IEEE
In Cloud Computing (CC) environment, container virtualization through Docker engine is bringing the new digital transformation in the enterprise multi-tier application architecture in this modern era. Elastic pod containers attracts and helps the application developers in developing and executing the cloud-native modern applications with key benefits such as light weight, agility to launch, easy deployment through images, consuming less power, minimum cost, less carbon footprints with increased resource utilization and provisioning on public cloud data centers. In existing, reactive auto-scaling mechanism of pod resources is used to add or remove resources manually or rule based for handling static workloads from users and most of the times it may lead to over provision or under provisioning of instances that violates QOS and SLA. In this paper, predictive horizontal scaling of pods utilizing custom metrics with orchestration through Kuberenetes is proposed with LSTM (Long Short-Term Memory) based on Deep Learning (DL) technique. DL is data hungry for right prediction of replicas of a cluster that is needed in advance to run cyclic workloads and to handle the sudden spike of demand by using the cloud large dataset real time traces. Moreover, LSTM model is compared with GRU (Gated Recurrent Unit) and experimental results shows that the results of LSTM prediction has less absolute error rate on comparing with GRU to keep the resource provisioning accuracy better for running the modern workloads seamlessly on public cloud.
G Bharanidharan and S Jayalakshmi
IEEE
Cloud Computing (CC) system faces new challenges every day, due to the complex structure of system clusters and high volume of data processed by the systems. The ability of acquiring resources in an elastic manner is considered as the primary rationale for adopting CC system. Elasticity mainly supports the facility to grow and shrink the virtual resources dynamically according to the requirement of cloud users. Elastic resource allocation plays a vital part in managing cloud resources efficiently according to user demands as well as cloud service provider’s capacity. For gaining an efficient allocation of resource allocation in cloud, this paper has discussed the study of improving the load distribution and resource utilization with elasticity. However, literature shows that several works in CC and its benefits but there is a dearth of survey in analyzing detail about elasticity present in the cloud. This study has an attempt for fulfilling an existing available gap and introducing this survey based on adapting elasticity in CC. This paper has involved with various elasticity mechanism inclusive of definition, tools to measure, elasticity evaluation and existing elastic solutions with some issues faced. Finally, this paper has presented few open issues and new solutions. To the best knowledge, this study is novel to identify the issues faced in the model of elasticity solution using systematic review method.
G. Bharanidharan and S. Jayalakshmi
In modern Data Centers (DCs), the major significant and challengeable task is resource management of cloud and efficient allocation of Virtual Machines (VMs) or containers in Physical Machines (PMs). There are several schemes proposed regarding this factor that includes VM placement considering utilization of resources. The process of consolidation may be done efficiently using “opportunities” discovery for migrating VMs and estimating utilization of resource to VM placement. However, the deduction of energy utilized over cloud DCs by physical resources with heterogeneous mode gets accomplished using consolidation of VM. This assists in minimize of PM numbers to be utilized and rely on constraints of Quality of Services (QoS). Therefore, this paper has proposed a predictive VM placement using an efficient Learning Automata (LA) with probability distribution activity set and it can be represented as Probability Distribution Action-set Learning Automata (PDALA) which results to the VM placement over heterogeneous cloud DCs. Thus, the proposed algorithm gets beneficial by implementing LA theory and correlation coefficient parameter to generate best decision making over VM allocation. Moreover, CloudSim plus simulator is used to simulate results and the simulation output gets compared with Power Aware Best Fit Decreasing (PABFD) as reactive VM placement. The proposed PDALA method performance is evaluated using parameters like VM migration, SLA Violation and energy consumption having comparatively better performance than existing reactive VM placement.
Bharanidharan. G and S. Jayalakshmi
The Science and Information Organization
The cloud resource usage has been increased exponentially because of adaptation of digitalization in government and corporate organization. This might increase the usage of cloud compute instances, resulting in massive consumption of energy from High performance Public Cloud Data Center servers. In cloud, there are some web applications which may experience diverse workloads at different timestamps that are essential for workload efficiency as well as feasibility of all extent. In cloud application, one of the major features is scalability in which most Cloud Service Providers (CSP) offer Infrastructure as a Service (IaaS) and have implemented auto-scaling on the Virtual Machine (VM) levels. Auto-scaling is a cloud computing feature which has the ability in scaling the resources based on demand and it assists in providing better results for other features like high availability, fault tolerance, energy efficiency, cost management, etc. In the existing approach, the reactive scaling with fixed or smart static threshold do not fulfill the requirement of application to run without hurdles during peak workloads, however this paper focuses on increasing the green tracing over cloud computing through proposed approach using predictive auto-scaling technique for reducing over-provisioning or under-provisioning of instances with history of traces. On the other hand, it offers right sized instances that fit the application to execute in satisfying the users through on-demand with elasticity. This can be done using Deep Learning based Time-Series LSTM Networks, wherein the virtual CPU core instances can be accurately scaled using cool visualization insights after the model has been trained. Moreover, the LSTM accuracy result of prediction is also compared with Gated Recurrent Unit (GRU) to bring business intelligence through analytics with reduced energy, cost and environmental sustainability.
BharaniDharan. G and Jayalakshmi. S
IEEE
In the new software-driven world this is the need of the hour to achieve success by accelerating development, delivery of applications and services rapidly that make customers happy and business competitive. To achieve faster delivery of resources and applications to strengthen the business with more transformations containers have been introduced. At the existing scenario, the hyper-converged data centre uses the concept of virtualization which creates software abstraction of the underlying hardware using Hypervisor software that enables to execute several Virtual Machines (VMs) with dissimilar Operating System (OS) flavours. But virtualization is heavyweight and it may take more time to boot. The proposed containerization and docker in hybrid cloud composable data centre is lightweight supports Operating System (OS) level virtualization that isolates resources, libraries and other binaries bundled into a single package for agile modern scalable workloads. This paper focuses on the container-based virtualization to shape the future cloud-based modern data centre for supporting the micro-services based applications for faster deployment to Developer Operations (DevOps) teams in IT with resiliency, High Availability and better resource management with energy savings.
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INDUSTRY EXPERIENCE
Worked as an Associate (2007-2008) at Sify Technologies,, Chennai in Domain for developing Enterprise Applications