DOI: https://doi.org/10.32515/2664-262X.2021.4(35).16-23
Architectural Features of Distributed Computing Systems
About the Authors
Roman Minailenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, e-mail: aron70@ukr.net, ORCID ID: 0000-0002-3783-0476
Olexandr Sobinov, lecturer, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, ORCID ID: 0000-0002-9465-4990
Oksana Konoplitska-Slobodenyuk, lecturer, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, ORCID ID: 0000-0001-9981-5194
Kostiantyn Buravchenko, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Abstract
Recently, there has been an increasing penetration of information technology in almost all areas of human life. The development of information technology is associated with the emergence of new tasks that require significant computing resources and can not be solved on a conventional computer.
A large amount of computing requires the creation of so-called supercomputers, which is not always technically possible. But there is another way to solve this problem, when a complex task is divided into a number of subtasks that run in parallel. And here come in handy distributed computing system. In general, a distributed computing system is a virtual machine that consists of several nodes connected by a network. That is, a certain three-dimensional problem is divided into several simple subtasks and connections are established between them. But such a system will be operational only when the tasks between the nodes are distributed correctly, and the sequence of their execution will take place according to a given algorithm.
The article analyzes the architectural features of distributed computing systems. The main task of distributed computing technologies is to provide access to globally distributed resources and solve problems that require significant computing power and can not be implemented on a conventional computer. The complexity of global tasks is due to the fact that the necessary data can be accessed on different computers. In addition, distributed computing systems, which are formed from autonomous resources, can change their architecture dynamically. Management of such distributed computer systems requires the search for new computational models and the search for architectural solutions to build new systems that would meet the current level of development of information technology.
Keywords
computer, distributed computing, information technology, architectural features
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References
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9. Shan H., Oliker L. et al. (2004). Scheduling in Heterogeneous Grid Environments: The Effects of Data Migration Advanced Computing and Communication (ADCOM 2004): Proceedings of the 12th IEEE International Conference (Ahmedabad Gujarat, India, De-cember 15–18, 2004). IEEE Computer Society, P. 1–8 [in English].
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14. Sakellariou, R &, Zhao, H. (2017). A Low-cost Rescheduling Policy for Efficient Mapping of Workflows on Grid Systems . Scientific Programming. Vol. 12, No. 4. P. 253–262 [in English].
15. Darbha, S. & Agrawal, D.P. (1998). Optimal Scheduling Algorithm for Distributed Memory Machines. IEEE Transactions on Parallel and Distributed Systems. Vol. 9, No. 1. P. 87–95 [in English].
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17. Bajaj, R. & Agrawal, D.P. (2004). Improving Scheduling of Tasks in A Heterogeneous Environment . IEEE Transactions on Parallel and Distributed Systems. Vol. 15, No. 2. P. 107–118 [in English].
18. Yang, T. & Gerasoulis, A. (1994). DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors. EEE Transactions on Parallel and Distributed Systems. Vol. 5, No. 9. P. 951–967 [in English].
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GOST Style Citations
Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems / R. Braun, H. Siegel et al. Parallel and Distributed Computing. 2001. Vol. 61, No. 6. P. 810–837.
Heuristics for Scheduling Parameter Sweep Applications in Grid Environ-ments / H. Casanova, A. Legrand et al. Heterogeneous Computing Workshop (HCW'00): Proceedings of the 9th Workshop (Cancun, Mexico, May 1, 2000). IEEE Computer Society, 2000. P. 349–363.
You, S.Y. Task Scheduling Algorithm in GRID Considering Heterogeneous Environment / S.Y. You, H.Y. Kim et al. Parallel and Distributed Processing Techniques and Ap-plications (PDPTA '04): Proceedings of the International Conference (Nevada, USA, June 21–24, 2004). CSREA Press, 2004. Vol. 1. P. 240–245.
Cooper, K. New Grid Scheduling and Rescheduling Methods in the GrADS Project /Cooper, A. Dasgupta et al. International Parallel and Distributed Processing Sym-posium (IPDPS'04): Proceedings of the 18th International Symposium (Santa Fe, New Mexico USA, April 26–30, 2004). IEEE Computer Society, 2004. P. 199–206.
Improving Grid Level Throughput Using Job Migration And Rescheduling / K. Kurowski, B. Ludwiczak et al. Scientific Programming. 2004. Vol. 12, No. 4. P. 263–273.
Takefusa, A. A Study of Deadline Scheduling for Client-Server Systems on the Computa-tional Grid / A. Takefusa, S. Matsuoka et al. High Performance Distributed Computing (HPDC-10): Proceedings of the 10th IEEE International Symposium (San Francisco, Cal-ifornia, USA, August 7–9, 2001). IEEE Computer Society, 2001. P. 406–415.
Chen, H., Maheswaran M. Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Sys-tems . International Parallel and Distributed Processing Symposium (IPDPS 2002): Proceedings of the 16th International Symposium (Fort Lauderdale, FL, USA, April 15-19, 2002). IEEE Computer Society, 2002. P. 88–97.
Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids / N. Muthuvelu, J. Liu et al. Grid Computing and e-Research (AusGrid 2005): Proceedings of the 3rd Australasian Workshop (Newcas-tle, NSW, Australia, January 30 – February 4, 2005). Australian Computer Society, 2005. P. 41–48.
Scheduling in Heterogeneous Grid Environments: The Effects of Data Migration / H. Shan, L. Oliker et al. Advanced Computing and Communication (ADCOM 2004): Proceedings of the 12th IEEE International Conference (Ahmedabad Gujarat, India, De-cember 15–18, 2004). IEEE Computer Society, 2004. P. 1–8.
Dong, F., Akl S.G. Scheduling algorithms for grid computing: State of the art and open problems. Technical Report No. 2006-504 . Queen’s University, Canada, 2006. P. 55.
Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests / V. Subramani, R. Kettimuthu et al. High Performance Distributed Computing (HPDC 2002): Proceedings of 11th IEEE Symposium (Edinburgh, Scotland, July 23–26, 2002). IEEE Computer Society, 2002. P. 359–366.
El-Rewini, H. Task Scheduling in Parallel and Distributed Systems / H. El-Rewini, T. Lewis, H. Ali — Prentice Hall, 2010. 290 p.
Radulescu, A., Gemund A.J.C. On the Complexity of List Scheduling Algorithms for Distributed Memory Systems . Supercomputing (SC’99): Proceedings of 13th International Conference (Portland, Oregon, USA, November 13–19, 1999). IEEE Computer Society, 1999. P. 68–75.
Sakellariou, R., Zhao H. A Low-cost Rescheduling Policy for Efficient Mapping of Workflows on Grid Systems . Scientific Programming. 2017. Vol. 12, No. 4. P. 253–262.
Darbha, S., Agrawal, D.P. Optimal Scheduling Algorithm for Distributed Memory Machines. IEEE Transactions on Parallel and Distributed Systems. 1998. Vol. 9, No. 1. P. 87–95.
Ranaweera, S., Agrawal D.P. A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems. International Parallel and Distributed Processing Sym-posium (IPDPS'00): Proceedings of 14TH International Symposium (Cancun, Mexico, May 1–5, 2018). IEEE Computer Society, 2005. P. 445–450.
Bajaj, R., Agrawal, D.P. Improving Scheduling of Tasks in A Heterogeneous Environment . IEEE Transactions on Parallel and Distributed Systems. 2004. Vol. 15, No. 2. P. 107–118.
Yang, T., Gerasoulis, A. DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors. EEE Transactions on Parallel and Distributed Systems. 1994. Vol. 5, No. 9. P. 951–967.
Liou, J., Palis, M.A. A Comparison of General Approaches to Multiprocessor Scheduling . International Parallel Processing Symposium (IPPS '97): Proceedings the 11th International Symposium (Geneva, Switzerland, April 1–5, 1997). IEEE Computer Society, 1996. P. 152–156.
Copyright (c) 2021 Roman Minailenko, Olexandr Sobinov, Oksana Konoplitska-Slobodenyuk, Kostiantyn Buravchenko
Architectural Features of Distributed Computing Systems
About the Authors
Roman Minailenko, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, e-mail: aron70@ukr.net, ORCID ID: 0000-0002-3783-0476
Olexandr Sobinov, lecturer, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, ORCID ID: 0000-0002-9465-4990
Oksana Konoplitska-Slobodenyuk, lecturer, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, ORCID ID: 0000-0001-9981-5194
Kostiantyn Buravchenko, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Abstract
Keywords
Full Text:
PDFReferences
1. Braun R., Siegel H. et al. (2001). Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems .Parallel and Distributed Computing. Vol. 61, No. 6. P. 810–837[in English].
2. H. Casanova, A. Legrand et al. (2000). Heuristics for Scheduling Parameter Sweep Applications in Grid Environ-ments. Heterogeneous Computing Workshop (HCW'00): Proceedings of the 9th Workshop (Cancun, Mexico, May 1, 2000). IEEE Computer Society, P. 349–363 [in English].
3. You S.Y., Kim H.Y. et al. (2004). Task Scheduling Algorithm in GRID Considering Heterogeneous Environment. Parallel and Distributed Processing Techniques and Ap-plications (PDPTA '04): Proceedings of the International Conference (Nevada, USA, June 21–24, 2004). CSREA Press. Vol. 1. P. 240–245 [in English].
4. Cooper, K., Dasgupta A. et al. (2004). New Grid Scheduling and Rescheduling Methods in the GrADS Project . International Parallel and Distributed Processing Sym-posium (IPDPS'04): Proceedings of the 18th International Symposium (Santa Fe, New Mexico USA, April 26–30, 2004). IEEE Computer Society, P. 199–206 [in English].
5. Kurowski K., Ludwiczak B. et al. (2004). Improving Grid Level Throughput Using Job Migration And Rescheduling / Scientific Programming. Vol. 12, No. 4. P. 263–273 [in English].
6. Takefusa, A., Matsuoka S. et al. (2001). A Study of Deadline Scheduling for Client-Server Systems on the Computa-tional Grid . High Performance Distributed Computing (HPDC-10): Procedings of the 10th IEEE International Symposium (San Francisco, Cal-ifornia, USA, August 7–9, 2001). IEEE Computer Society, P. 406–415 [in English].
7. Chen, H. & Maheswaran, M. (2002). Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Sys-tems . International Parallel and Distributed Processing Symposium (IPDPS 2002): Proceedings of the 16th International Symposium (Fort Lauderdale, FL, USA, April 15-19, 2002). IEEE Computer Society, P. 88–97 [in English].
8. Muthuvelu, N., Liu, J. et al. (2005). Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids . Grid Computing and e-Research (AusGrid 2005): Proceedings of the 3rd Australasian Workshop (Newcas-tle, NSW, Australia, January 30 – February 4, 2005). Australian Computer Society, P. 41–48 [in English].
9. Shan H., Oliker L. et al. (2004). Scheduling in Heterogeneous Grid Environments: The Effects of Data Migration Advanced Computing and Communication (ADCOM 2004): Proceedings of the 12th IEEE International Conference (Ahmedabad Gujarat, India, De-cember 15–18, 2004). IEEE Computer Society, P. 1–8 [in English].
10. Dong, F. & Akl, S.G. (2006). Scheduling algorithms for grid computing: State of the art and open problems. Technical Report No. 2006-504 . Queen’s University, Canada, P. 55 [in English].
11. Subramani, V., Kettimuthu, R. et al. (2002). Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests . High Performance Distributed Computing (HPDC 2002): Proceedings of 11th IEEE Symposium (Edinburgh, Scotland, July 23–26, 2002). IEEE Computer Society, P. 359–366 [in English].
12. El-Rewini, H. & Lewis, T. (2010). Task Scheduling in Parallel and Distributed Systems. H. Ali - Prentice Hall, 290 p. [in English].
13. Radulescu, A. & Gemund, A.J.C. (1999). On the Complexity of List Scheduling Algorithms for Distributed Memory Systems . Supercomputing (SC’99): Proceedings of 13th International Conference (Portland, Oregon, USA, November 13–19, 1999). IEEE Computer Society, P. 68–75 [in English].
14. Sakellariou, R &, Zhao, H. (2017). A Low-cost Rescheduling Policy for Efficient Mapping of Workflows on Grid Systems . Scientific Programming. Vol. 12, No. 4. P. 253–262 [in English].
15. Darbha, S. & Agrawal, D.P. (1998). Optimal Scheduling Algorithm for Distributed Memory Machines. IEEE Transactions on Parallel and Distributed Systems. Vol. 9, No. 1. P. 87–95 [in English].
16. Ranaweera, S. & Agrawal, D.P. (2005). A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems. International Parallel and Distributed Processing Sym-posium (IPDPS'00): Proceedings of 14TH International Symposium (Cancun, Mexico, May 1–5, 2018). IEEE Computer Society, P. 445–450 [in English].
17. Bajaj, R. & Agrawal, D.P. (2004). Improving Scheduling of Tasks in A Heterogeneous Environment . IEEE Transactions on Parallel and Distributed Systems. Vol. 15, No. 2. P. 107–118 [in English].
18. Yang, T. & Gerasoulis, A. (1994). DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors. EEE Transactions on Parallel and Distributed Systems. Vol. 5, No. 9. P. 951–967 [in English].
19. Liou, J. & Palis, M.A. (1996). A Comparison of General Approaches to Multiprocessor Scheduling . International Parallel Processing Symposium (IPPS '97): Proceedings the 11th International Symposium (Geneva, Switzerland, April 1–5, 1997). IEEE Computer Society, P. 152–156 [in English].