Publications

Export 12 results:
Sort by: Author Title [ Type  (Asc)] Year
Conference Paper
MadhuKumar, SD, Bellur U, Kiran EK.  2007.  Adding Underlay Aware Fault Tolerance to Hierarchical Event Broker Networks. The Second International Conference on Software and Data Technologies, ICSOFT 07. :99-105., Barcelona Abstract

n/a

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
n/a
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
n/a
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
Kovilakath, VP, Kumar MSD.  2012.  Semantic broken link detection using structured tagging scheme. Proceedings of the International Conference on Advances in Computing, Communications and Informatics. :16–20., New York, NY, USA: ACM Abstract
n/a
Gilesh, MP, Kumar MSD, Jacob L, Bellur U.  2017.  Towards a Complete Virtual Data Center Embedding Algorithm Using Hybrid Strategy. 37th {IEEE} International Conference on Distributed Computing Systems, {ICDCS} 2017, Atlanta, GA, USA, June 5-8, 2017. :2616–2617. Abstract
n/a
Xu, J, Palanisamy B, Tang Y, Kumar MSD.  2017.  {PADS:} Privacy-Preserving Auction Design for Allocating Dynamically Priced Cloud Resources. 3rd {IEEE} International Conference on Collaboration and Internet Computing, {CIC} 2017, San Jose, CA, USA, October 15-17, 2017. :87–96. Abstract
n/a
Conference Proceedings
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
n/a
Journal Article
Mathew, AB, MadhuKumar SD, Krishnan MK, Salam SM.  2017.  Efficient query retrieval in Neo4jHA using metaheuristic social data allocation scheme. Computers & Electrical Engineering. AbstractWebsite

n/a

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
n/a
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

n/a