EXALAZY: A MODEL FOR LAZY-COPY MIGRATION MECHANISM TO SUPPORT DISTRIBUTED EXASCALE SYSTEM

Details

Hits: 1072

Volume 4 (2), June 2021, Pages 170-187

Ehsan Mousavi Khaneghah¹, Tayebeh Khoshrooynemati¹, Azar Feyziyev²

¹  Shahed University, Tehran, Iran. This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

² Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

There is a possibility of dynamic and interactive nature occurring at any moment of the scientific program implementation process in the computing system. While affecting the computational processes in the system, dynamic and interactive occurrence also affects the function of the elements that make up the management element of the computing system. The effect of dynamic and interactive events on the function of the elements that make up the management element of the computing system causes the time required to run the user program to increase or the function of these elements to change. These changes either increase the execution time of the scientific program or make the system incapable of executing the program. The occurrence of dynamic and interactive nature creates new situations in the computing system that the mechanisms to deal with when designing the computing system are not defined and considered. In this paper, the Lazy-Copy process migration management mechanism, specifically the Lazy-Copy mechanism in distributed large-scale systems, the effects of dynamic and interactive occurrence in the computational system investigate, and the effects of dynamic and interactive occurrence on the system investigate. Computational processes on the migration process and vector algebras try to analyze and enable the Lazy-Copy process migration mechanism in support of distributed large-scale systems despite dynamic and interactive events.

Keywords:

Industry 4.0, Industry 5.0, Industry 6.0, Digital technology, Cosmetics industry, ICT.

DOI: https://doi.org/10.32010/26166127.2021.4.2.170.187

Reference 

Afzal, S., & Kavitha, G. (2019). Load balancing in cloud computing–A hierarchical taxonomical classification. Journal of Cloud Computing, 8(1), 1-24.

Al-Dhuraibi, Y. (2018). Flexible framework for elasticity in cloud computing (Doctoral dissertation, Université lille1).

Anawar, M. R., et al. (2018). Fog computing: An overview of big IoT data analytics. Wireless Communications and Mobile Computing, 2018.

Anzt, H., et al. (2020). Preparing sparse solvers for exascale computing. Philosophical Transactions of the Royal Society A, 378(2166), 20190053.

Ashraf, M. U., et al. (2018). Toward exascale computing systems: An energy efficient massive parallel computational model. International Journal of Advanced Computer Science and Applications, 9(2).

Barak, A., & La’adan, O. (1998). The MOSIX multicomputer operating system for high performance cluster computing. Future Generation Computer Systems, 13(4-5), 361-372.

Chou, C. C., et al. (2019, November). Optimizing post-copy live migration with system-level checkpoint using fabric-attached memory. In 2019 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC) (pp. 16-24). IEEE.

Di, Z., Shao, E., & Tan, G. (2021). High-performance Migration Tool for Live Container in a Workflow. International Journal of Parallel Programming, 49(5), 658-670.

He, T., & Buyya, R. (2021). A Taxonomy of Live Migration Management in Cloud Computing. arXiv preprint arXiv:2112.02593.

Khaneghah, E. M., et al. (2011, December). An efficient live process migration approach for high performance cluster computing systems. In International Conference on Innovative Computing Technology (pp. 362-373). Springer, Berlin, Heidelberg.

Khaneghah, E. M., et al. (2018). ExaMig matrix: Process migration based on matrix definition of selecting destination in distributed exascale environments. Azerbaijan Journal of High Performance Computing, 1(1), 20-41.

Khaneghah, E. M., ShowkatAbad, A. R., & Ghahroodi, R. N. (2018, February). Challenges of process migration to support distributed exascale computing environment. In Proceedings of the 2018 7th international conference on software and computer applications (pp. 20-24).

Kumar, P., & Kumar, R. (2019). Issues and challenges of load balancing techniques in cloud computing: A survey. ACM Computing Surveys (CSUR), 51(6), 1-35.

LaViola, J. J., Hachet, M., & Billinghurst, M. (2011, March). Message from the symposium chairs. In 2011 IEEE Symposium on 3D User Interfaces (3DUI) (pp. vii-vii). IEEE Computer Society.

Masdari, M., & Khoshnevis, A. (2020). A survey and classification of the workload forecasting methods in cloud computing. Cluster Computing, 23(4), 2399-2424.

Morin, C., et al. (2003, August). Kerrighed: a single system image cluster operating system for high performance computing. In European Conference on Parallel Processing (pp. 1291-1294). Springer, Berlin, Heidelberg.

Mousavi Khaneghah, E., Noorabad Ghahroodi, R., & Reyhani ShowkatAbad, A. (2018). A mathematical multi-dimensional mechanism to improve process migration efficiency in peer-to-peer computing environments. Cogent Engineering, 5(1), 1458434.

Noshy, M., Ibrahim, A., & Ali, H. A. (2018). Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications, 110, 1-10.

Pickartz, S., Breitbart, J., & Lankes, S. (2016). Implications of process-migration in virtualized environments. In Proceedings of the 1st COSH Workshop on Co-Scheduling of HPC Applications (p. 31).

Plank, J. S., & Thomason, M. G. (2001). Processor allocation and checkpoint interval selection in cluster computing systems. Journal of Parallel and distributed Computing, 61(11), 1570-1590.

Ranjan, A., et al. (2015, March). DyReCTape: A dynamically reconfigurable cache using domain wall memory tapes. In 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 181-186). IEEE.

Rough, J., & Gościński, A. (1998). PVM on the RHODOS: A Preliminary Performance Study. Deakin University, School of Computing and Mathematics.

Setiawan, I., & Murdyantoro, E. (2016, October). Commodity cluster using single system image based on Linux/Kerrighed for high-performance computing. In 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 367-372). IEEE.

Shah, V., & Donga, J. (2020). Load balancing by process migration in distributed operating system. LAP LAMBERT Academic Publishing.

Stoyanov, R., & Kollingbaum, M. J. (2018, June). Efficient live migration of linux containers. In International Conference on High Performance Computing (pp. 184-193). Springer, Cham.

Stoyanov, R., & Kollingbaum, M. J. (2018, June). Efficient live migration of linux containers. In International Conference on High Performance Computing (pp. 184-193). Springer, Cham.

Talaat, F. M., et al. (2020). A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4951-4966.

Tang, Z., et al. (2018). Migration modeling and learning algorithms for containers in fog computing. IEEE Transactions on Services Computing, 12(5), 712-725.

Thoman, P., et al. (2018). A taxonomy of task-based parallel programming technologies for high-performance computing. The Journal of Supercomputing, 74(4), 1422-1434.

Vivek, V., et al. (2019). Payload fragmentation framework for high-performance computing in cloud environment. The Journal of Supercomputing, 75(5), 2789-2804.

Xu, Y., et al. (2019). Dynamic switch migration in distributed software-defined networks to achieve controller load balance. IEEE Journal on Selected Areas in Communications, 37(3), 515-529.

Yang, K., Gu, J., Zhao, T., & Sun, G. (2011, August). An optimized control strategy for load balancing based on live migration of virtual machine. In 2011 Sixth Annual ChinaGrid Conference (pp. 141-146). IEEE.

Yousafzai, A., et al. (2019). Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing. IEEE internet of things journal, 7(5), 4171-4182.