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Johan Alfredo Romero-Ramírez, Carlos Enrique Montenegro-Marín, Vicente García-Díaz and Juan Manuel Cueva Lovelle

R eferences [1] J. Morris, Practical Data Migration, BCS. 2012, pp. 7, 8, 9, 10, 77, 164. [2] C. Drumm, M Schmitt, H. H. Do and E. Rahm, “QuickMig. Automatic Schema Matching for Data Migration Projects,” in Proc. of the sixteenth ACM conf. on Conf. on information and knowledge management , CIKM’07, ACM New York, NY, USA, 2007, pp. 107–116. [3] A. Bilke, J. Bleiholder, C. Böhm, K. Draba, F. Naumann, M. Weis, “Automatic Data Fusion with HumMer,” in Proc. of the 31st int. conf. on Very large data bases

Open access

Todor Balabanov, Iliyan Zankinski and Maria Barova

Conference on Genetic Algorithms, Morgan Kaufmann, 1989, pp. 434-439. 7. Uchida, T., T. Matsuzawa, Y. Inoguchi. The Inuence of Elitism Strategy on Migration Intervals of a Distributed Genetic Algorithm. – Proceedings in Adaptation, Learning and Optimization, Vol. 2 , 2015, pp. 363-374. 8. Balabanov, T. MPI Parallel Implementation of Genetic Algorithm Based Rubik’s Cube Solver. 9. Ivanov, V. Using a PicoBlaze Processor to Traffic Light Control. – Cybernetics and Information Technologies

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B. Prabadevi and N. Jeyanthi

Statement. No RFC 5556, 2009. 14. Kim, M. C. C., J. Rexfor d. Floodless in SEATTLE: A Scalable Ethernet Architecture for Large Enterprises. - In: Proc. of ACM SIGCOMM Conference on Data communication (SIGCOMM’08), 2008. 15. Umar, K., M. K. Gardner, E. J. Brown, W.-C. Feng. Seamless Migration of Virtual Machines Across Networks. - In: 22nd International Conference on Computer Communication and Networks (ICCCN’13), IEEE, 2013, pp. 1-7. 16. Mauricio, A., M. Portolan i. Data-Center Network Architecture. U.S. Patent 7

Open access

Perla Ravi Theja and S. K. Khadar Babu

References 1. Fraser, C. K., et al. Live Migration of Virtual Machines. - In: Proc. of 2nd USENIX Symposium on Networked Systems Design and Implementation, Berkeley, CA, 2005, pp. 273-286. 2. Vogels, W. Beyond Server Consolidation. - ACM Queue, 2008, No 1, pp. 20-26. 3. Feller, E., C. Morin et al. A Case for Fully Decentralized Dynamic VM Consolidation in Clouds. - In: Proc. of 4th IEEE International Conference, Cloud Computing Technology and Science, Taipei, Taiwan, 2012, pp. 26-33. 4. Murtazaev

Open access

Darko Andročec and Neven Vrček


Ontologies can be used to describe common cloud functionalities and enable common terminology to assist in cloud interoperability. In this work, we have developed the ontology for resources and operations and the ontology of interoperability problems. The aim is to clearly describe and categorize the existing functionalities, features and specificities of commercial platform asaservice offers. The first ontology also provides data type mappings among different Paa Sstorages and cross-Paa Sdata types used in inputs and outputs of the remote APIoperations to provideacommon layer for information exchange and data migration among different Paa Sproviders. The ontologies were evaluated by tools and by human experts. Furthermore, the ontologies were used in cloud interoperability prototype to show their practical applicability.

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Vladimir N. Ivanov 6. Luisov, A.V. Eye and Light. Energoatomizdat, Leningrad, 1983 (In Russian). 7. Fain, V. S. Pattern Recognition and Machine Understanding of Natural Language. Moscow, Science, 1987 (In Russian). 8. Srinivas, K. Implementation of FPGA in the Design of Embedded Systems Highway Traffic Light Controller. National Institute of Technology, Rourkela, May 2007. 9. Physical Encyclopedia in 5 Volumes. Moscow, Soviet Encyclopedia, 1983 (In Russian). 10. Royer, M., M. Goode. FPGA to ASIC Migration. - Integrated

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Ketaki Bhalchandra Naik, G. Meera Gandhi and S. H. Patil

References 1. Buyya, R. K., et al. Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. – Future Generation Computer Systems, Vol. 25 , 2009, No 6, pp. 599-616. 2. Li, W., J. Tordsson, E. Elmroth. Modelling for Dynamic Cloud Scheduling via Migration of Virtual Machines. – In: Proc. of 3rd IEEE Int. Conf. Cloud Comput. Technol. Sci. CloudCom’2011, 2011, pp. 163-171. 3. Davis, L. J., L. J. Davis. Selection of Load Balancing Parameters. – Vol. 9655 , 2015, No October. 4. Ullman, J

Open access

Naiqiang Fan, Yingmin Wang and Yarong Lv

SAR in High Squint Mode Based on Range Walk Removal. – Jounal of Electronics & Information Technology, Vol. 32 , 2010, No 3, pp. 593-598. 5. Xiong, J., et al. Simulation Study of Imaging for Squint-Looking Airborne SAR Based on RWC. – Fire Control & Command Control, Vol. 37 , 2012, No 4, pp. 176-180. 6. Zhang, S.-X., et al. Focus Improvement of High-Squint SAR Based on Azimuth Dependence of Quadratic Range Cell Migration Correction. – IEEE Geoscience and Remote Sensing Letters, Vol. 10 , 2013, No 1, pp. 150-154. 7. An, D.-X., et al. Extended

Open access

N Jeyanthi and P. C. Mogankumar

References 1. Zissis, D., D. Lekkas. Addressing Cloud Computing Security Issues. - Future Generation Computer Systems, Vol. 28, 2012, 583-592. 2. Du, P., A. Nakao. OverCourt: DDoS Mitigation through Credit-Based Traffic Segregation and Path Migration. - Computer Communications, Vol. 33, 15 December 2010, No 18, 2164-2175. 3. Lent, R. Evaluating a Migration-Based Response to DoS Attacks in a System of Distributed Auctions. - Computers & Security, Vol. 31, May 2012, No 3, 327-343. 4. Liu, Xiao

Open access

N. Jeyanthi and N. Ch. S. N. Iyengar

References 1. Janczewski, L. J., D. Reamer, J. Brendel. Handling Distributed Denial-of-Service Attacks. - Information Security Technical Report, Vol. 6 , 2001, 37-44. 2. Kumar, P. A. R., S. Selvakumar. Distributed Denial of Service Attack Detection Using an Ensemble of Neural Classifier. - Computer Communications, Vol. 34 , 2011, 1328-1341. 3. Lent, R. Evaluating a Migration-Based Response to DoS Attacks in a System of Distributed Auctions. - Computers & Amp Security, Vol. 31 , 2012, 327