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Shruti.P.Mahambre, MadhuKumar SD, Bellur U.  2007.  A Taxonomy of QoS-Aware, Adaptive Event-Dissemination Middleware. IEEE Internet Computing. 11:35-44., Number 4 Abstract

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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
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
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.

Shabeera, TP, MadhuKumar SD, Sameera.  2017.  Curtailing Job Completion Time in MapReduce Clouds. Computers & Electrical Engineering, International Journal Elsevier. 58:190-201.
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
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, 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.  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)
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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Shabeera, TP, MadhuKumar SD.  2017.  A HEURISTIC BASED ALGORITHM FOR DISTANCE-AWARE VIRTUAL MACHINE ALLOCATION IN CLOUD, March 2017. Second International Conference on Internet of Things, Data and Cloud Computing ( ICC'17). , Cambridge University, United Kingdom:ACM