Faculty Publications
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Publications by NITK Faculty
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Item Improving the efficiency of genetic algorithm approach to virtual machine allocation(Institute of Electrical and Electronics Engineers Inc., 2014) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.Virtual machine (VM) allocation is the process of allocating virtual machines to suitable hosts. This problem is an NP-Hard problem. It can be considered as a variation of the bin-packing problem. Among various solutions that attempt to solve this problem, several approaches that apply Genetic Algorithm have been proposed. This paper proposes a method to improve the efficiency of such approaches. Implementation of the proposed approach shows significant improvements in the runtime, memory used, energy efficiency and SLA violations. © 2014 IEEE.Item A bio-inspired model to provide data security in cloud storage(Institute of Electrical and Electronics Engineers Inc., 2017) Hitaswi, N.; Chandrasekaran, K.The demand for cloud computing is increasing rapidly because of the advantages it provides to the customers like, pay as you use, self-serving, elastic, sharing of resources, ease of use, and accessibility. Due to the increase in the usage of the technology, there exists a high chance of compromising the security of the data being stored on the cloud. The major hindrance in the usage of the technology is the security concerns which accompany it. This increases the demand for a robust security mechanism to protect the data on the cloud. So as to overcome this drawback of cloud computing, encrypting the data to be stored on the cloud is one of the solutions. As part of this paper, a security mechanism to improve the security of data in cloud storage is suggested. The security mechanism used is inspired by the bio-inspired genetic algorithm. The inspiration behind the proposed security model is an amalgamation of genetic algorithm and attribute based encryption. As per the methodology proposed the data need to be encrypted before being stored on the cloud. This way the cloud service provider is unaware of the data being stored and even if the data is compromised to some third party, there is no information leakage. © 2016 IEEE.Item NEAT Algorithm for Testsuite generation in Automated Software Testing(Institute of Electrical and Electronics Engineers Inc., 2018) Praveen Raj, H.L.P.; Chandrasekaran, K.Software testing is one of the most essential and an indispensable part of Software production life cycle. Software testing helps in validating if the product meets with the requirements or not, and also testing helps to validate the performance of the product. Unfortunately, this process takes up about 50% of the production time and budget, due to its laboriosity. Hence, in order to reduce the time it takes, Automated Software Testing becomes essential. Here we propose a novel idea of using Machine Learning for automatically generating the test suites. In this paper we present an approach that uses NEAT (Neuroevolution of Augmenting Topologies) Algorithm to automatically generate new test suites or for improving the coverage of already produced test suite. Our approach automatically generates test suites for white box testing. White box testing refers to testing of the internal structure and the working of the Software Under Test. © 2018 IEEE.