Vishan Kumar Gupta

Work place: Dept. of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

E-mail: vishangupta@gmail.com

Website:

Research Interests: Data Structures and Algorithms, Data Mining, Computational Learning Theory, Computational Biology, Computer systems and computational processes

Biography

Dr. Vishan Kumar Gupta has received his Ph.D. degree in Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India. He has received his M.Tech degree in Information Technology from ABV-Indian Institute of Information Technology and Management, Gwalior, MP, India and Bachelor of Engineering in CSE from Rajiv Gandhi Technical University, Bhopal, MP, India, Currently, he is working as an Associate Professor in Graphic Era Deemed to be University, Dehradun, Uttarakhand, India. His areas of research are Computational Biology, Data Mining, and Machine learning.

Author Articles
A Theoretical Graph based Framework for Parameter Tuning of Multi-core Systems

By Surendra Kumar Shukla Devesh Pratap Singh Shaili Gupta Kireet Joshi Vishan Kumar Gupta

DOI: https://doi.org/10.5815/ijwmt.2022.04.02, Pub. Date: 8 Aug. 2022

Multi-core systems are outperforming nowadays. Therefore, various computing paradigms are intrinsically incorporated in the multicore domain to exploit its potential and solve well known computing problems. Parameter tuning is a well-known computing problem in the field of Multicore domain. Addressing the said hurdle would leverage in the performance enhancement of Multicore systems. Various efforts in this direction have been made through the conventional parameter tuning algorithms in a limited scope; however, the problem is yet not addressed completely. In this research article, we have addressed parameter tuning problem by employing applications of graph theory, especially Dijkstra shortest path algorithm to address the said issue. Dijkstra’s principle has been applied to establish correlation among the parameters further tuning by finding the pair of suitable parameters. Two other algorithms which are based on application feedback (to provide performance goals to the system) has been introduced. The proposed algorithms collectively (as a framework), addressed the parameter tuning problem. The effectiveness of the algorithms is verified and further measured in distinct parameter tuning scenarios and promising outcome has been achieved.

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A Performance Perspective of Live Migration of Virtual Machine in Cloud Data Center with Future Directions

By Divya Kapil Saurabh Kumar Mishra Vishan Kumar Gupta

DOI: https://doi.org/10.5815/ijwmt.2022.04.04, Pub. Date: 8 Aug. 2022

Live virtual machine migration is a valuable feature for the virtualized data center or cloud environment. This is the process to migrate running virtual machines from one physical host to another host. Live virtual machine migration can be used to provide various benefits such as server consolidation, energy-saving, and maintenance. It is a valuable feature for the virtualized data center or cloud environment. Cloud computing provides IT capabilities as a service and its key technology is virtualization. The key benefit of virtualization is to offer better resource employment by executing various VMs on the same physical system. In this research, we analyze the performance of the various hypervisors based on their migration features and compares the migration feature. Hypervisors are Xen, VMWare, KVM, and their migration feature are XenMotion, vMotion, and KVM migration, respectively. According to our study, we find that there are many factors that affect the performance of the live virtual machine migration such as a long downtime, the large amount of data that is sent in an iteration manner so with a higher dirtying rate the total migration time extends. In our comparison, we show VMWare has the least downtime.  We also identify and discuss the various research challenges in detail to stimulate the researchers to work in this direction.

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Analysis and Detection of various DDoS attacks on Internet of Things Network

By Atika Bansal Divya Kapil Anupriya Sagar Agarwal Vishan Kumar Gupta

DOI: https://doi.org/10.5815/ijwmt.2022.03.02, Pub. Date: 8 Jun. 2022

Internet of Things is used for those devices, which are connected over a network, once the devices are connected to the internet they are known as smart devices. These devices share information and communicate with each other to influence our day to day lives. Due to the rise in these devices, security is compromised. Malware is malicious software that can damage the computer, server, or network intentionally. Malware can also exploit the confidentiality, integrity, availability (CIA) triad. Rather than the traditional malware, IoT malware can damage different internet connected devices such as routers, DVRs, CCTV, or many internets connected devices. The IoT devices are more vulnerable due to weak passwords, missing authentication schemes, backdoor entries, lack of high-security algorithms, and plug and play services. There is no widespread survey available about IoT malware in an efficiently organized manner, publicly. In this article, we have classified the IoT malware according to their release and provide on the basis of their functionalities, growth, revolution, and their detection mechanism. We perform DDoS attack on Raspberry PI to hamper the home automation system. We employ Wireshark to monitor network traffic and demonstrate the service unavailability.

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