Publications

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Conference Paper
Ojus Thomas, L, MadhuKumar SD, Chandran P.  2016.  6. ECSim:A Simulation Tool for Performance Evaluation of Erasure Coded Storage Systems. Symposium on Emerging Topics in Computing and Communications (SETCAC'16). , Jaipur
Shabeera, TP, Chandran P, Kumar MSD.  2012.  Authenticated and persistent skip graph: a data structure for cloud based data-centric applications. Proceedings of the International Conference on Advances in Computing, Communications and Informatics. :155–160., New York, NY, USA: ACM Abstract
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Chacko, A, MadhuKumar SD.  2017.  Big data provenance research directions. Region 10 Conference, TENCON 2017-2017 IEEE. :651–656.: IEEE Abstract
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Ojus Thomas, L, MadhuKumar SD, Chandran P.  2016.  Erasure Coded Storage Systems for Cloud Storage - Challenges and Opportunities, August. IEEE International Conference on Data Science and Engineering (ICDSE 2016). , Kochi
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
Badharudheen, P, MadhuKumar SD, Chacko AM.  2014.  Making an Application Provenance-Aware through UML - A General Scheme, March 2014. SNDS 2014. , Trivandrum, India
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
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
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
Conference Proceedings
MadhuKumar, SD, S.M.M.M.KalyanKumar, SureshChandraMoharana, Chimakurthi L.  2011.  A Cloud Based Scheme for Constraint-less Mobile Computing. International Conference on Emerging Technology Trends in Cloud Computing. , Kollam,India
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
Manish Parashar, Umesh Bellur, S.D Madhu Kumar, Priya Chandran, Murali Krishnan, Kamesh Madduri, Sushil K. Prasad, C. Chandra Sekhar, Nanjangud C. Narendra, Carlos Valera, Sanjay Chaudhary, Kavi Arya, Xiaolin Li (Eds.).  2014.  Seventh International Conference on Contemporary Computing, {IC3} 2014, Noida, India, August 7-9, 2014. : {IEEE} Abstract
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MadhuKumar, SD, Chandran P.  1996.  Windows Based Application Software Developer, July 1996. EDUCOMP' 96 Internatonal Conference on Educational Computing. :178-185., Chandigargh, India
Journal Article
Shabeera, TP, MadhuKumar SD, Chandran P.  2016.  Curtailing job completion time in MapReduce clouds through improved Virtual Machine allocation. Elsevier- Computers & Electrical Engineering. :-. AbstractWebsite

Abstract Cloud-based MapReduce platforms offer ready to use MapReduce clusters. The problem of allocating Virtual Machines (VMs) carrying out the computation, for minimizing data transfer delay is a crucial one in this context, as the MapReduce tasks are communication intensive. The interaction between \{VMs\} may face varying delays, if the \{VMs\} are hosted in different Physical Machines (PMs). This work aims to optimize the data transfer delay between VMs, which is denoted by the distance between the VMs. We propose an approximation algorithm for \{VM\} allocation in data centers wherein the distances between \{VMs\} satisfy triangular inequality and an optimization algorithm for \{VM\} allocation in data centers where the distances between \{VMs\} do not satisfy triangular inequality. Simulations on CloudSim demonstrate the performance of our algorithms and the results affirm the reduction in job completion time compared to other allocation schemes.