Shreenath Acharya

Work place: Department of Computer Science, St Joseph Engineering College, Mangaluru, 575028, India



Research Interests: Computer systems and computational processes, Autonomic Computing, Computer Architecture and Organization, Computing Platform


Shreenath Acharya received B.E from Mysore University and M.Tech from VTU. Currently serving in Computer Science & Engineering Department at St Joseph Engineering College, Mangaluru. He has over 19 years of experience in education sector and more than 25 publications in international conferences/journals. His areas of interest are cloud computing, big data, computer communication networks and security.

Author Articles
Energy Saving VM Placement in Cloud

By Shreenath Acharya Demian Antony D Mello

DOI:, Pub. Date: 8 Dec. 2018

The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.

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Dynamic Malware Analysis and Detection in Virtual Environment

By Akshatha Sujyothi Shreenath Acharya

DOI:, Pub. Date: 8 Mar. 2017

The amount and the complexity of malicious activity increasing and evolving day by day. Typical static code analysis is futile when challenged by diverse variants. The prolog of new malware samples every day is not uncommon and the malware designed by the attackers have the ability to change as they propagate. Thus, automated dynamic malware analysis becomes a widely preferred technique for the identification of unknown malware.
In this paper, an automated malware detection system is presented based on dynamic malware analysis approach. The behavior of malware is observed in the controlled environment of the popular malware analysis system. It uses the clustering and classification of embedded malware behavior reports to identify the presence of malicious behavior. Based on the experimentation and evaluation it is evident that the proposed system is able to achieve better F-measures, FPR, FNR, TPR and TNR values resulting in accurate classification leading to more efficient detection of unknown malware compared to the traditional hierarchical classification approach.

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