Browsing by Author "Karthik, C."

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A green greedy process scheduler for cloud data centers
(Institute of Electrical and Electronics Engineers Inc., 2014) Karthik, C.; Gupta, A.; Chandrasekaran, K.
In this paper we have addressed a major problem in current day data centers- power consumption. Power consumption in data centers has become a major problem these days, both from economic and environmental perspective. Various factors affect the power consumption, one of them being the scheduling of tasks on the data center servers. Basically we achieved a real-time simulation of two cloud scheduling algorithms and compared the power efficiency of the two algorithms in terms of two main temperature parameters of the servers-idle temperature and critical temperature. We assumed that we were given all the task parameters such as running time etc. and then we calculated a final temperature that a system will reach on running that particular task. Then we decided which system could accommodate that task based on that systems critical temperature and chose the best system among those based on a score proposed in the paper. Â© 2014 IEEE.
A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory
(Elsevier, 2015) Bhat, T.P.; Karthik, C.; Chandrasekaran, K.
Traditional approaches to data mining may perform well on extraction of information necessary to build a classification rule useful for further categorisation in supervised classification learning problems. However most of the approaches require fail to hide the identity of the subject to whom the data pertains to, and this can cause a big privacy breach. This document addresses this issue by the use of a graph theoretical approach based on k-partitioning of graphs, which paves way to creation of a complex decision tree classifier, organised in a prioritised hierarchy. Experimental results and analytical treatment to justify the correctness of the approach are also included. Â© 2015 The Authors.
A green greedy process scheduler for cloud data centers
(2014) Karthik, C.; Gupta, A.; Chandrasekaran, K.
In this paper we have addressed a major problem in current day data centers- power consumption. Power consumption in data centers has become a major problem these days, both from economic and environmental perspective. Various factors affect the power consumption, one of them being the scheduling of tasks on the data center servers. Basically we achieved a real-time simulation of two cloud scheduling algorithms and compared the power efficiency of the two algorithms in terms of two main temperature parameters of the servers-idle temperature and critical temperature. We assumed that we were given all the task parameters such as running time etc. and then we calculated a final temperature that a system will reach on running that particular task. Then we decided which system could accommodate that task based on that systems critical temperature and chose the best system among those based on a score proposed in the paper. � 2014 IEEE.
Green intelligence for cloud data centers
(2016) Karthik, C.; Sharma, M.; Maurya, K.; Chandrasekaran, K.
In this paper the problem of energy consumption by large data centers has been tackled. Power consumption is major problem from both economic and environmental point of view. One of the main components of data centers is virtualization. We have addressed the problem of Virtual Machine (VM) consolidation in the data center servers using the technique of Bin Completion. Bin Completion is basically an artificial intelligence based algorithm used for bin packing problem. We have scaled up and modified the algorithm to fit our problem statement of VM consolidation and analysed the results obtained against Best Fit algorithm. After that we did an extensive study of the application of machine learning algorithms for the purpose of CPU utilisation prediction and analysed its effects on the overall energy consumption of a data center as well as the SLA violations. � 2016 IEEE.
Green intelligence for cloud data centers
(Institute of Electrical and Electronics Engineers Inc., 2016) Karthik, C.; Sharma, M.; Maurya, K.; Chandrasekaran, K.
In this paper the problem of energy consumption by large data centers has been tackled. Power consumption is major problem from both economic and environmental point of view. One of the main components of data centers is virtualization. We have addressed the problem of Virtual Machine (VM) consolidation in the data center servers using the technique of Bin Completion. Bin Completion is basically an artificial intelligence based algorithm used for bin packing problem. We have scaled up and modified the algorithm to fit our problem statement of VM consolidation and analysed the results obtained against Best Fit algorithm. After that we did an extensive study of the application of machine learning algorithms for the purpose of CPU utilisation prediction and analysed its effects on the overall energy consumption of a data center as well as the SLA violations. Â© 2016 IEEE.
A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory
(2015) Bhat, T.P.; Karthik, C.; Chandrasekaran, K.
Traditional approaches to data mining may perform well on extraction of information necessary to build a classification rule useful for further categorisation in supervised classification learning problems. However most of the approaches require fail to hide the identity of the subject to whom the data pertains to, and this can cause a big privacy breach. This document addresses this issue by the use of a graph theoretical approach based on k-partitioning of graphs, which paves way to creation of a complex decision tree classifier, organised in a prioritised hierarchy. Experimental results and analytical treatment to justify the correctness of the approach are also included. � 2015 The Authors.