How to cite this paper
Adamuthe, A & Kupwade, V. (2024). Simple and efficient duelist algorithm variations for energy-aware virtual machine placement in cloud data centers.Decision Science Letters , 13(3), 751-766.
Refrences
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Abdel-Basset, M., Abdle-Fatah, L., & Sangaiah, A. K. (2019). An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in a cloud computing environment. Cluster Computing, 22(4), 8319-8334.
Abohamama, A. S., & Hamouda, E. (2020). A hybrid energy–aware virtual machine placement algorithm for cloud environments. Expert Systems with Applications, 150, 113306.
Adamuthe, A., & Kagwade, S. (2022). Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment. Decision Science Letters, 11(2), 113-126.
Alharbi, F., Tian, Y. C., Tang, M., Zhang, W. Z., Peng, C., & Fei, M. (2019). An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Systems with Applications, 120, 228-238.
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Alsadie, D. (2022). Virtual Machine Placement Methods using Metaheuristic Algorithms in a Cloud Environment-A Comprehensive Review. International Journal of Computer Science & Network Security, 22(4), 147-158.
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Beloglazov, A., & Buyya, R. (2010, May). Energy efficient allocation of virtual machines in cloud data centers. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 577-578). IEEE.
Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768
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Biyanto, T. R., Fibrianto, H. Y., Nugroho, G., Hatta, A. M., Listijorini, E., Budiati, T., & Huda, H. (2016a). Duelist algorithm: an algorithm inspired by how duelist improve their capabilities in a duel. In Advances in Swarm Intelligence: 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016, Proceedings, Part I 7 (pp. 39-47). Springer International Publishing.
Biyanto, T. R., Irawan, S., Ginting, H. J., & Fitri, A. I. (2017a). Operating Conditions Optimization of Steam Injection in Enhanced Oil Recovery Using Duelist Algorithm. International Journal of Industrial and Manufacturing Engineering, 11(2), 272-277.
Biyanto, T. R., Matradji, Syamsi, M. N., Fibrianto, H. Y., Afdanny, N., Rahman, A. H., ... & Putra, Y. A. (2017b, November). Optimization of energy efficiency and conservation in green building design using Duelist, Killer-Whale and Rain-Water Algorithms. In IOP conference series: materials science and engineering (Vol. 267, No. 1, p. 012036). IOP Publishing.
Biyanto, T. R., Ramasamy, M., Jameran, A. B., & Fibrianto, H. Y. (2016b). Thermal and hydraulic impacts consideration in refinery crude preheat train cleaning scheduling using recent stochastic optimization methods. Applied Thermal Engineering, 108, 1436-1450.
Breitgand, D., & Epstein, A. (2011, May). SLA-aware placement of multi-virtual machine elastic services in compute clouds. In 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops (pp. 161-168). IEEE.
Buyya, R., Broberg, J., & Goscinski, A. M. (Eds.). (2010). Cloud computing: Principles and paradigms. John Wiley & Sons.
Chaisiri, S., Lee, B. S., & Niyato, D. (2009, December). Optimal virtual machine placement across multiple cloud providers. In 2009 IEEE Asia-Pacific Services Computing Conference (APSCC) (pp. 103-110). IEEE.
Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., & Zhao, F. (2008, April). Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services. In NSDI (Vol. 8, pp. 337-350).
Dang, H. T., & Hermenier, F. (2013, November). Higher SLA satisfaction in datacenters with continuous VM placement constraints. In Proceedings of the 9th workshop on hot topics in dependable systems (pp. 1-6).
Energy Efficiency Predictions for Data Centres in 2023. (2022, December 30). Data Centre Magazine. Retrieved from https://www.datacentremagazine.com/articles/efficiency-to-loom-large-for-data-centre-industry-in-2023
Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of computer and system sciences, 79(8), 1230-1242.
Ghribi, C., Hadji, M., & Zeghlache, D. (2013, May). Energy-efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (pp. 671-678). IEEE.
IEA. (2022). Data Centres and Data Transmission Networks. IEA, Paris. Retrieved from https://www.iea.org/reports/data-centres-and-data-transmission-networks
Jayasinghe, D., Pu, C., Eilam, T., Steinder, M., Whally, I., & Snible, E. (2011, July). Improving performance and availability of services hosted on iaas clouds with structural constraint-aware virtual machine placement. In 2011 IEEE International Conference on Services Computing (pp. 72-79). IEEE.
LASHIN, M. M., GAAFER, A. M., & Al Nemer, G. N. (2020). Optimization of dimensional, surface quality and material removal rate in turning using response surface methodology and duelist algorithm. International Journal of Mechanical and Production Engineering Research and Development, 10(1), 499-514.
Pushpa, R., & Siddappa, M. (2022, January). Adaptive Hybrid Optimization Based Virtual Machine Placement in Cloud Computing. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1-9). IEEE.
Ruki Biyanto, T., Yernias Fibrianto, H., Nugroho, G., Listijorini, E., Budiati, T., & Huda, H. (2015). Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel. arXiv e-prints, arXiv-1512.
Shuja, J., Gani, A., Shamshirband, S., Ahmad, R. W., & Bilal, K. (2016). Sustainable cloud data centers: a survey of enabling techniques and technologies. Renewable and Sustainable Energy Reviews, 62, 195-214.
Singh, A. K., Swain, S. R., Saxena, D., & Lee, C. N. (2023). A bio-inspired virtual machine placement toward sustainable cloud resource management. IEEE Systems Journal.
Speitkamp, B., & Bichler, M. (2010). A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Transactions on services computing, 3(4), 266-278.
Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005(2005), 2005.
Tang, M., & Pan, S. (2015). A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural processing letters, 41, 211-221.
Usmani, Z., & Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data center. Procedia Computer Science, 78, 491-498.
Van den Bossche, R., Vanmechelen, K., & Broeckhove, J. (2010, July). Cost-optimal scheduling in hybrid iaas clouds for deadline constrained workloads. In 2010 IEEE 3rd international conference on cloud computing (pp. 228-235). IEEE.
Wood, T., Shenoy, P., Venkataramani, A., & Yousif, M. (2009). Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks, 53(17), 2923-2938.
Wu, G., Tang, M., Tian, Y. C., & Li, W. (2012). Energy-efficient virtual machine placement in data centers by genetic algorithm. In Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III 19 (pp. 315-323). Springer Berlin Heidelberg.
Xing, H., Zhu, J., Qu, R., Dai, P., Luo, S., & Iqbal, M. A. (2022). An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing. Swarm and Evolutionary Computation, 68, 101012.
Zhu, W., Zhuang, Y., & Zhang, L. (2017). A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Generation Computer Systems, 69, 66-74.
Abdel-Basset, M., Abdle-Fatah, L., & Sangaiah, A. K. (2019). An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in a cloud computing environment. Cluster Computing, 22(4), 8319-8334.
Abohamama, A. S., & Hamouda, E. (2020). A hybrid energy–aware virtual machine placement algorithm for cloud environments. Expert Systems with Applications, 150, 113306.
Adamuthe, A., & Kagwade, S. (2022). Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment. Decision Science Letters, 11(2), 113-126.
Alharbi, F., Tian, Y. C., Tang, M., Zhang, W. Z., Peng, C., & Fei, M. (2019). An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Systems with Applications, 120, 228-238.
Alicherry, M., & Lakshman, T. V. (2013, April). Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In 2013 Proceedings IEEE INFOCOM (pp. 647-655). IEEE.
Alsadie, D. (2022). Virtual Machine Placement Methods using Metaheuristic Algorithms in a Cloud Environment-A Comprehensive Review. International Journal of Computer Science & Network Security, 22(4), 147-158.
Batista, D. M., Da Fonseca, N. L., & Miyazawa, F. K. (2007, March). A set of schedulers for grid networks. In Proceedings of the 2007 ACM symposium on Applied computing (pp. 209-213).
Beloglazov, A., & Buyya, R. (2010, May). Energy efficient allocation of virtual machines in cloud data centers. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 577-578). IEEE.
Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768
Biran, O., Corradi, A., Fanelli, M., Foschini, L., Nus, A., Raz, D., & Silvera, E. (2012, May). A stable network-aware vm placement for cloud systems. In 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012) (pp. 498-506). IEEE.
Biyanto, T. R., Fibrianto, H. Y., Nugroho, G., Hatta, A. M., Listijorini, E., Budiati, T., & Huda, H. (2016a). Duelist algorithm: an algorithm inspired by how duelist improve their capabilities in a duel. In Advances in Swarm Intelligence: 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016, Proceedings, Part I 7 (pp. 39-47). Springer International Publishing.
Biyanto, T. R., Irawan, S., Ginting, H. J., & Fitri, A. I. (2017a). Operating Conditions Optimization of Steam Injection in Enhanced Oil Recovery Using Duelist Algorithm. International Journal of Industrial and Manufacturing Engineering, 11(2), 272-277.
Biyanto, T. R., Matradji, Syamsi, M. N., Fibrianto, H. Y., Afdanny, N., Rahman, A. H., ... & Putra, Y. A. (2017b, November). Optimization of energy efficiency and conservation in green building design using Duelist, Killer-Whale and Rain-Water Algorithms. In IOP conference series: materials science and engineering (Vol. 267, No. 1, p. 012036). IOP Publishing.
Biyanto, T. R., Ramasamy, M., Jameran, A. B., & Fibrianto, H. Y. (2016b). Thermal and hydraulic impacts consideration in refinery crude preheat train cleaning scheduling using recent stochastic optimization methods. Applied Thermal Engineering, 108, 1436-1450.
Breitgand, D., & Epstein, A. (2011, May). SLA-aware placement of multi-virtual machine elastic services in compute clouds. In 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops (pp. 161-168). IEEE.
Buyya, R., Broberg, J., & Goscinski, A. M. (Eds.). (2010). Cloud computing: Principles and paradigms. John Wiley & Sons.
Chaisiri, S., Lee, B. S., & Niyato, D. (2009, December). Optimal virtual machine placement across multiple cloud providers. In 2009 IEEE Asia-Pacific Services Computing Conference (APSCC) (pp. 103-110). IEEE.
Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., & Zhao, F. (2008, April). Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services. In NSDI (Vol. 8, pp. 337-350).
Dang, H. T., & Hermenier, F. (2013, November). Higher SLA satisfaction in datacenters with continuous VM placement constraints. In Proceedings of the 9th workshop on hot topics in dependable systems (pp. 1-6).
Energy Efficiency Predictions for Data Centres in 2023. (2022, December 30). Data Centre Magazine. Retrieved from https://www.datacentremagazine.com/articles/efficiency-to-loom-large-for-data-centre-industry-in-2023
Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of computer and system sciences, 79(8), 1230-1242.
Ghribi, C., Hadji, M., & Zeghlache, D. (2013, May). Energy-efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (pp. 671-678). IEEE.
IEA. (2022). Data Centres and Data Transmission Networks. IEA, Paris. Retrieved from https://www.iea.org/reports/data-centres-and-data-transmission-networks
Jayasinghe, D., Pu, C., Eilam, T., Steinder, M., Whally, I., & Snible, E. (2011, July). Improving performance and availability of services hosted on iaas clouds with structural constraint-aware virtual machine placement. In 2011 IEEE International Conference on Services Computing (pp. 72-79). IEEE.
LASHIN, M. M., GAAFER, A. M., & Al Nemer, G. N. (2020). Optimization of dimensional, surface quality and material removal rate in turning using response surface methodology and duelist algorithm. International Journal of Mechanical and Production Engineering Research and Development, 10(1), 499-514.
Pushpa, R., & Siddappa, M. (2022, January). Adaptive Hybrid Optimization Based Virtual Machine Placement in Cloud Computing. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1-9). IEEE.
Ruki Biyanto, T., Yernias Fibrianto, H., Nugroho, G., Listijorini, E., Budiati, T., & Huda, H. (2015). Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel. arXiv e-prints, arXiv-1512.
Shuja, J., Gani, A., Shamshirband, S., Ahmad, R. W., & Bilal, K. (2016). Sustainable cloud data centers: a survey of enabling techniques and technologies. Renewable and Sustainable Energy Reviews, 62, 195-214.
Singh, A. K., Swain, S. R., Saxena, D., & Lee, C. N. (2023). A bio-inspired virtual machine placement toward sustainable cloud resource management. IEEE Systems Journal.
Speitkamp, B., & Bichler, M. (2010). A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Transactions on services computing, 3(4), 266-278.
Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005(2005), 2005.
Tang, M., & Pan, S. (2015). A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural processing letters, 41, 211-221.
Usmani, Z., & Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data center. Procedia Computer Science, 78, 491-498.
Van den Bossche, R., Vanmechelen, K., & Broeckhove, J. (2010, July). Cost-optimal scheduling in hybrid iaas clouds for deadline constrained workloads. In 2010 IEEE 3rd international conference on cloud computing (pp. 228-235). IEEE.
Wood, T., Shenoy, P., Venkataramani, A., & Yousif, M. (2009). Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks, 53(17), 2923-2938.
Wu, G., Tang, M., Tian, Y. C., & Li, W. (2012). Energy-efficient virtual machine placement in data centers by genetic algorithm. In Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III 19 (pp. 315-323). Springer Berlin Heidelberg.
Xing, H., Zhu, J., Qu, R., Dai, P., Luo, S., & Iqbal, M. A. (2022). An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing. Swarm and Evolutionary Computation, 68, 101012.
Zhu, W., Zhuang, Y., & Zhang, L. (2017). A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Generation Computer Systems, 69, 66-74.