Genetic-based algorithms for resource management in virtualized IVR applications

详细信息    查看全文

• 作者：Nadjia Kara (1)
  Mbarka Soualhia (1)
  Fatna Belqasmi (2)
  Christian Azar (2)
  Roch Glitho (2)
  
  1. ETS ; University of Quebec ; 1100 ; Notre-Dame street West ; Montreal ; Quebec ; H3C 1K3 ; Canada
  2. Concordia University ; 7141 ; Sherbrook street West ; Montreal ; Quebec ; H4B 1R6 ; Canada
  
• 关键词：Resource management ; Cloud computing ; Virtualization ; IVR applications ; Genetic algorithms
• 刊名：Journal of Cloud Computing
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：3
• 期：1
• 全文大小：2,586 KB
• 参考文献：1. Xu S, Gao W, Li Z, Zhang S, Zhao J (2010) Design of Hierarchical and Configurable IVR System. Second International Conference on Computational Intelligence and Natural Computing Proceedings (CINC), pp 205鈥?08
  2. Khan, A, Zugenmaier, A, Jurca, D, Kellerer, W (2012) Network Virtualization: A Hypervisor for the Internet?. IEEE Commun Mag 50: pp. 136-143 CrossRef
  3. Vaquero, LM, Rodero-Merino, L, Caceres, J, Lindner, M (2009) A break in the clouds: towards a cloud definition. ACM SIGCOMM Comp Commun Rev 39: pp. 50-55 CrossRef
  4. Belqasmi F, Azar C, Soualhia M, Kara N, Glitho R (2011) A Virtualized Infrastructure for Interactive Voice Response Applications in the Cloud. ITU-T Kaleidoscope the Fully Networked Human - Innovations for Future Networks and Services, pp 1鈥?
  5. Belqasmi, F, Azar, C, Soualhia, M, Kara, N, Glitho, R (2013) A case study of Virtualized Infrastructure and its accompanying platform for IVR Applications in Clouds. IEEE Network Mag 28: pp. 33-41 CrossRef
  6. Zomaya, YA, The, YH (2001) Observation on using genetic algorithms for dynamic load-balancing. IEEE Trans Parallel Distributed Syst 12: pp. 899-911 CrossRef
  7. Xhafa, F, Carretero, J, Abraham, A (2008) Genetic Algorithm Based Schedulers for Grid Computing Systems. Int J Innovative Comput, Inf Control 3: pp. 1-19
  8. Kidwell MD, Cook DJ (1994) Genetic Algorithm for Dynamic Task scheduling. Proc. IEEE 14th Annual International Phoenix conference on Computers and communications, pp 61鈥?7
  9. Carretero, J, Xhafa, F (2007) Genetic algorithm based schedulers for grid computing systems. Int J Innovative Comput, Inf Control 3: pp. 1-19
  10. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning鈥? Reading, Mass. Addison-Wesley. ISBN 0201157675
  11. Zomaya, AY, Ward, C, Macey, B (1999) Genetic Scheduling for parallel processor systems: Comparative studies and performance issues. IEEE Trans Parallel Distributed Syst 10: pp. 795-812 CrossRef
  12. Kaur, K, Chhabra, A, Singh, G (2010) Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. Int J Comput Sci Secur 4: pp. 149-264
  13. Omara, FA, Arafa, MM (2010) Genetic algorithms for task scheduling problem. J Parallel Distributed Comput 70: pp. 13-22 CrossRef
  14. Probir, R, Mejbah Ul Alam, MD, Nishita, D (2012) Heuristic based task scheduling in multiprocessor systems with genetic algorithm by choosing the eligible processor. Int J Distributed Parallel Syst (IJDPS) 3: pp. 111-121 CrossRef
  15. Prabhu, S (2011) Multi-Objective Optimization based on genetic algorithm in Grid Scheduling. Int J Advanc Res Technol 1: pp. 54-58
  16. Tayal, S (2011) Tasks scheduling optimization for the cloud computing systems. Int J Advanc Eng Sci Technol 5: pp. 111-115
  17. Bach, FR, Jordan, MI (2003) Kernel independent component analysis. J Mach Learn Res 3: pp. 1-48
  18. Ganapathi, A, Kuno, H, Daval, U, Wiener, J, Fox, A, Jordan, M, Patterson, D (2009) Proceedings of IEEE International Conference on Data Engineering.
  19. Ganapathi A, Chen Y, Fox A, Katz R, Katz R, Patterson D (2010) Statistics-driven workload modeling for the cloud. 26th IEEE International Conference on Data Engineering, pp 87鈥?2
  20. Gao, Y, Rong, H, Huang, JZ (2005) Adaptive grid job scheduling with genetic algorithms. Elsevier J Future Generation Comp Syst 21: pp. 151-161 CrossRef
  21. Kim S, Weissman JB (2004) A genetic algorithm based approach for scheduling decomposable data grid applications. International Conference on Parallel Processing, pp 406鈥?13
  22. Barrett, E, Howley, E, Duggan, J (2011) A learning architecture for scheduling workflow applications in the cloud. 9th IEEE European Conference on Web Services, 83
  23. Yu, J, Buyya, R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Programming J 14: pp. 217-230
  24. Morariu, O, Morariu, C, Borangiu, T (2012) A genetic algorithm for workload scheduling in cloud based e-learning. Proceedings of the 2th International Worshop on Cloud Computing Platforms. pp. 1-6 CrossRef
  25. Zhong H, Tao K, Zhang X (2010) An approach to optimized Resource scheduling algorithm for Open-Source Cloud Systems. The 5th Annual China Grid Conference, pp 124鈥?29
  
• 刊物主题：Computer Communication Networks; Special Purpose and Application-Based Systems; Information Systems Applications (incl. Internet); Computer Systems Organization and Communication Networks; Computer System Implementation; Software Engineering/Programming and Operating Systems;
• 出版者：Springer Berlin Heidelberg
• ISSN：2192-113X

文摘

Interactive Voice Response (IVR) is a technology that allows automatic human-computer interactions, via a telephone keypad or voice commands. The systems are widely used in many industries, including telecommunications and banking. Virtualization is a potential technology that can enable the easy development of IVR applications and their deployment on the cloud. IVR virtualization will enable efficient resource usage by allowing IVR applications to share different IVR substrate components such as the key detector, the voice recorder and the dialog manager. Resource management is part and parcel of IVR virtualization and poses a challenge in virtualized environments where both processing and network constraints must be considered. Considering several objectives to optimize the resource usage makes it even more challenging. This paper proposes IVR virtualization task scheduling and computational resource sharing (among different IVR applications) strategies based on genetic algorithms, in which different objectives are optimized. The algorithms used by both strategies are simulated and the performance measured and analyzed.