Publications

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Akash, GJ, Ojus TL, MadhuKumar SD, ChandranPriya, Alfredo C.  2017.  RAPID: A Fast Data Update Protocol in Erasure Coded Storage Systems for Big Data, May 2017. 2nd IEEE/ACM International Workshop on Distributed Big Data Management (DBDM 2017), In conjunction with IEEE/ACM CCGRID2017. , Madrid, Spain
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AnuMary, C, MadhuKumar SD, Munavar F.  2016.  Provenance-Aware NoSQL Databases. International Symposium on Security in Computing and Communications (SSCC'16). , Jaipur: Springer
Howlader, P, Pal KK, MadhuKumar SD, Cuzzocrea A.  2018.  Predicting Facebook-Users’ Personality based on Status and Linguistic Features via Flexible Regression Analysis Techniques, April 2018. ACM SAC 2018 Cognitive Computing Track. , Pau, France
Chimakurthi, L, MadhuKumar SD, SureshChandraMoharana, Kalyankumar SMMM.  2011.  Power Efficient Resource Allocation for Clouds Using Ant Colony Framework , April 2011. International Conference on Advances in Computing and Communication ICACC-2011. , NIT Hamirpur, India
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Shabeera, TP, MadhuKumar SD, Salam SM, Krishnan MK.  2016.  Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Elsevier-Engineering Science and Technology, an International Journal. :-. AbstractWebsite

Abstract Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs) and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and \{VMs\} as independent problems. This paper proposes an approach which considers \{VM\} placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of \{VMs\} and data in Physical Machines (PMs) which are physically closer. The \{VM\} and data placement problem (referred as MinDistVMDataPlacement problem) is defined in this paper and has been proved to be NP- Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO), which selects a set of adjacent \{PMs\} for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of \{VMs\} are placed on these \{PMs\} to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects \{PMs\} in close proximity and the jobs executed in the \{VMs\} allocated by the proposed scheme outperforms other allocation schemes.

Shabeera, TP, MadhuKumar SD, Salam SM, Krishnan MK.  2017.  Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Engineering Science and Technology, an International Journal. 20:616-628., Number 2 AbstractWebsite

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Shabeera, TP, MadhuKumar SD.  2015.  Optimizing Virtual Machine Allocation in MapReduce Cloud for improved Data Locality. Inderscience International Journal of Big Data Intelligence (IJBD). Vol.2 No.1(March 2015)
BabithaBalachandran, MadhuKumar SD.  2013.  Optimization Of Reconfiguration Time During Client Movement On Siena, August 2013. International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013. , Mysore India
Gilesh, MP, Jain S, MadhuKumar SD, Jacob L, Bellur U.  2019.  Opportunistic live migration of virtual machines. International Journal of Concurrency and Computation Practice and Experience, Wiley. 32(5)
Jain, S, MP G, MadhuKumar SD, Jacob L.  2018.  On the Necessity of Right Optimizations for Live Migration of Virtual Machines. IEEE TENSYMP. , Sydney, Australia: IEEE Explore
Arun, KS, Govindan VK, Kumar MSD.  2017.  On integrating re-ranking and rank list fusion techniques for image retrieval, May. International Journal of Data Science and Analytics. : Springer AbstractWebsite
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AnitaBrigit, M, MadhuKumar SD.  2015.  Novel Research Framework on SN’s NoSQL Databases for Efficient Query Processing. Inderscience International Journal of Reasoning-based Intelligent Systems. (Accepted)
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Athira, AB, Kumar SDM, Tiwari, Chacko AM.  2023.  Multimodal Data Fusion Framework For Fake News Detection, Dec 2022. IEEE 19th India Council International Conference (INDICON 2022). , Kochi
Lee, OT, Sharma V, MadhuKumar SD, Chandran P.  2019.  Modelling Multi-level consistency in Erasure Code Based Storage System. ACM SAC 2019. , Cyprus
Ojus, OTL, Akash AGJ, MadhuKumar SD, Priya PC.  2017.  A Method for Storage Node Allocation in Erasure Code Based Storage Systems. Collaboration and Internet Computing (CIC), 2017 IEEE 3rd International Conference on. :449–454.: IEEE Abstract

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Badharudheen, P, MadhuKumar SD, Chacko AM.  2014.  Making an Application Provenance-Aware through UML - A General Scheme, March 2014. SNDS 2014. , Trivandrum, India
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Amritapatole, MadhuKumar SD, Chandran P, Shabeera TP.  2015.  Load-Aware Replica Placement in Multiuser Hadoop Environment Using MST, 8, December. Third edition of the International Conference on Recent Advances in Computational Systems(IEEE RAICS -2015). , Trivandrum
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Ajwalia, SM, Chacko AM, MadhuKumar SD.  2020.  Integrating Cloud and Health Informatics: Approaches, Applications, and Challenges- Chapter 2. Cloud Security. : CRC press
Ajwalia, SM, Chacko AM, MadhuKumar SD.  2019.  Integrating Cloud and Health Informatics: Approaches, Applications, and Challenges. 1st International Conference on Machine Learning, Image Processing, Network Security and Data Sciences(MIND 2019), Kurukshetra.
AnuMary, C, Anish G, Madhusudhan G, MadhuKumar SD.  2016.  Improving execution speed of Incremental runs of MapReduce using Provenance. (International Journal of Big Data Intelligence (IJBDI), InderScience (In Press).
Anu Mary Chacko, Anish Gupta, MS, MadhuKumar SD.  2017.  Improving execution speed of incremental runs of MapReduce using provenance. International Journal of Big Data Intelligence. 4:186-194. Abstract

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Pratima, HV, Leee OT, MadhuKumar SD, ChandranPriya.  2018.  Improved Epoch expiry and Load handling mechanism for RAPID - The Fast Data update Protocol in Erasure Coded Storage Systems. International Conference on Data Science and Engineering (ICDSE 2018). , Cochin University: IEEE explore
Manakattu, SS, Kumar MSD.  2012.  An improved biased random sampling algorithm for load balancing in cloud based systems. Proceedings of the International Conference on Advances in Computing, Communications and Informatics. :459–462., New York, NY, USA: ACM Abstract
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Gilesh, MP, MadhuKumar SD, Lillykutty J.  2017.  HyViDE: A Framework for Virtual Data Center Network Embedding. ACM SAC 2017 Track on Cloud Computing. , Marrakesh, Morocco: ACM
V, AK, Jaisooraj, Chandran P, MadhuKumar SD.  2019.  Hybrid-RPL: A Step towards Ensuring Scalable Routing in Internet of Things. International Conference on Advanced Communication and Computational Technology (ICACCT) 2019. , NIT Kurukshtra