Publications

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2016
MadhuKumar, SD.  2016.  Current Research Directions on Big data Analytics-opening Keynote. ICIA-16: Proceedings of the International Conference on Informatics and Analytics. :pp:22-23., Pondicherry, India: Published by ACM , ICPS Abstract

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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.

Shiven, S, Sachin S, Gilesh MP, MadhuKumar SD.  2016.  Evaluation of Performance Characteristics and Limitations of FlowVisor. International Conference in Information Science(ICIS -2016). , Kochi
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).
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.

AnuMary, C, MadhuKumar SD, Munavar F.  2016.  Provenance-Aware NoSQL Databases. International Symposium on Security in Computing and Communications (SSCC'16). , Jaipur: Springer
2015
Binesh, J, MadhuKumar SD.  2015.  Telecom grade cloud computing: Challenges and opportunities, Feb 2015. IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). , Calicut India
Shilpa, P, MadhuKumar SD.  2015.  Feature Oriented Sentiment Analysis in Social Networking Sites to Track Malicious Campaigners, 8, December. Third edition of the International Conference on Recent Advances in Computational Systems ( IEEE RAICS-2015). , Trivandrum
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
AnuMary, C, AjeebBasheer M, MadhuKumar SD.  2015.  Capturing Provenance for Big Data Analytics done Using SQL interface, 4, December. IEEE UP Section Conference on Electrical, Computer and Electronics (IEEE UPCON2015). , Allahabad
AnitaBrigit, M, MadhuKumar SD.  2015.  Analysis of Data Management and Query handling in Social Networks using NoSQL Databases. Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015) ( Accepted). , Cochin, India: IEEE Explore
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)
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)
2014
Anita, BM, Priyabrat P, MadhuKumar SD.  2014.  Efficient Information Retrieval using Lucene, LIndex and HIndex in Hadoop, November 2014. 11th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA' 2014). , Doha
Badharudheen, P, MadhuKumar SD, Chacko AM.  2014.  Making an Application Provenance-Aware through UML - A General Scheme, March 2014. SNDS 2014. , Trivandrum, India
GaneshBabu, Shabeera TP, MadhuKumar SD.  2014.  Dynamic Colocation Algorithm For Hadoop, 27 September. International Conference on Advances in Computing, Communications and Informatics (ICACCI-2014). , Noida, New Delhi
RahulPrasad, K, Shabeera TP, MadhuKumar SD.  2014.  Dynamic Cluster Configuration Algorithm in MapReduce Cloud. International Journal of Computer Science and Information Technologies(ijcsit). 5 (3):4028-4033.
AnitaBrigit, M, MadhuKumar SD.  2014.  An Efficient Index Based Query Handling Model for Neo4j. IJCST. Vol. 3( no. 2):pp.12–18.
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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2013
Shabeera, TP, MadhuKumar SD.  2013.  'Bandwidth-Aware Data Placement Scheme for Hadoop, December 2013. IEEE Recent Advances in Intelligent Computational Systems (IEEE RAICS). , Trivandrum, India
VijuPoonthottam, MadhuKumar SD.  2013.  A Dynamic Data Placement Scheme for Hadoop Using Real-time Access Patterns, August 2013. International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013). , Mysore India
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
2012
MadhuKumar, SD, Reshmi G.  2012.  An Adaptive Method for Node Revocation in Peer-to-Peer Networks. International Conference on Cloud Computing Technology and Management (ICCCTAM 12). , Dubai. UAE
MadhuKumar, SD, Anju KS, Asha, Mary J, Indu J, Subha N, Reshma, Ann T.  2012.  Virtual Machine Placement Based on VM-PM Compatibility. The Mediterranean Journal of Computers and Networks . 8(2):35-40.Website
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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