DOI: https://doi.org/10.32515/2414-3820.2025.55.323-331
Distributed Object Technologies in Information Systems
About the Authors
Roman Minailenko, Associate Professor, PhD of technical sciences (Candidate of Technical Sciences), Associate Professor of the Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0000-0563-0798, e-mail: aron70@ukr.net
Oksana Konoplitska-Slobodeniuk, Lecturer at the Department of Cybersecurity and Software, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-9981-5194, e-mail: ksuha80@gmail.com
Iryna Lysenko, PhD in Technical Sciences (Candidate of Technical Sciences), Senior Lecturer, Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4394-4960, e-mail: min_max@i.ua
Abstract
The article shows that the development of information and telecommunications technologies is at a stage when in a distributed information and telecommunications environment, not only the need for access and exchange of information, but also the implementation of various types of analysis and processing of this information is becoming increasingly important. The widespread introduction of computer technologies into all types of activities, the constant increase in their computing power, the use of computer networks of various scales requires the use of a significant amount of high-performance distributed computing, which in turn leads to a shortage of computing resources when performing various computing processes. An effective way to solve these problems is to use parallel and distributed computing.
Currently, issues are being discussed regarding the description of products, technologies and methodologies for creating small and medium-sized information systems. At the same time, technologies and methodologies for building large information systems, which combine a set of local information systems, are practically not considered and discussed. The consequence of this is that even at the design stage, technologies for creating a large information system are chosen that do not meet the requirements. For this reason, the projects being implemented do not receive proper development.
The modern level of development of society defines the IT industry as a leading and strategic direction of concentration of intellectual and financial resources. Information and tools for its management (software products of various functional purposes) have acquired the status of information resources (IR). The latter are concentrated within the framework of IS. The unification of resources on the basis of information and communication interaction of IS brings them to the level of corporate information resources. This unification is often called the Unified Information Space (UIS). The implementation of UIS at the level of the state, corporation, enterprise is possible in the case of the creation with subsequent observance of the standard for the interaction between IS and their individual applications
In the concept of a single information space, it is necessary to provide that the information resources of the IS, in relation to it, act both as data and as various IS applications. Then, in each of the ISs, part of the data processing methods is implemented as applications accessible from other ISs, in particular, in the case of interaction of two ISs, the first is used by services provided by the second, as a result of which it receives already processed data that can be subjected to further processing by the components of the first IS. This approach corresponds to a distributed, peer-to-peer architecture of interaction. According to this architecture, any applications from different ISs can act as both a client and a server in relation to each other, jointly solving certain tasks. This approach minimizes duplication of applications. The distribution of applications across different ISs makes it possible to achieve an optimal balance of application loading and hardware, which will lead to the effective use of information resources of the systems as a whole.
Modern technologies make it possible to create an integrated environment within the IS, and within the framework of the EIP concept, which has the following properties:
− does not depend on hardware and system software;
− is based on international and industrial standards;
− allows you to develop a single information model of representing an enterprise as a set of managed resources and activity flows, configured to implement the rules for managing the collective activities of each specific enterprise;
− ensures system extensibility, i.e. simplicity and ease of adding new components to existing IS;
− allows you to integrate old applications (legacy applications) into new IS;
− assumes natural integration of created IS, which guarantees viability and evolutionary development;
− allows you to accumulate, replicate and develop formalized knowledge of specialists;
− significantly reduces the total costs of creating IS.
Keywords
computer, distributed computing, information systems, computing resources
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References
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2. Braun, R. A., 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.
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5. 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.
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8. 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.
9. Muthuvelu, N. A, 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.
10. 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.
11. 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.
12. 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.
13. El-Rewini, H., Lewis, T., & Ali, H. (2010). Task Scheduling in Parallel and Distributed Systems . Prentice Hall, 290 p.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. Liou, J. A., & Palis, M.A. (1996). 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.
21. Avrunin, O.H., Bodianskyi, Ye.V., Kalashnyk, M.V., Semenets, V.V., & Filatov, V.O. (2018). Modern intelligent technologies of functional medical diagnostics. Kharkiv: KhNURE [in Ukrainian].
Citations
1. Минайленко Р.М. Паралельні та розподілені обчислення: навч. посіб. Кропивницький: Видавець Лисенко В. Ф., 2021. 153 с.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Sys-tems / H. Chen, M. Maheswaran . 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.
9. A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids / N. Muthuvelu, J. Liu et al. rid 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.
10. 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.
11. 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.
12. 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.
13. El-Rewini, H., Lewis, T., Ali, H. Task Scheduling in Parallel and Distributed Systems . Prentice Hall, 2010. 290 p.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. Yang, T. 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.
20. Liou, J. A., Palis, M.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.
21. Сучасні інтелектуальні технології функціональної медичної діагностики / О.Г. Аврунін, Є.В. Бодянський, М.В. Калашник та ін. Харків: ХНУРЕ, 2018. 236 с.
Copyright (c) 2025 Roman Minailenko, Oksana Konoplitska-Slobodeniuk, Iryna Lysenko
Distributed Object Technologies in Information Systems
About the Authors
Roman Minailenko, Associate Professor, PhD of technical sciences (Candidate of Technical Sciences), Associate Professor of the Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0000-0563-0798, e-mail: aron70@ukr.net
Oksana Konoplitska-Slobodeniuk, Lecturer at the Department of Cybersecurity and Software, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-9981-5194, e-mail: ksuha80@gmail.com
Iryna Lysenko, PhD in Technical Sciences (Candidate of Technical Sciences), Senior Lecturer, Department of Cybersecurity and Software Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4394-4960, e-mail: min_max@i.ua
Abstract
Keywords
Full Text:
PDFReferences
1. Mynaylenko, R.M. (2021). Parallel and distributed computing: a textbook. Kropyvnytskyi: Publisher Lysenko V. F. [in Ukrainian].
2. Braun, R. A., 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.
3. Casanova, H., Legrand, A. 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.
4. 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.
5. 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.
6. 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.
7. 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): Proceedings of the 10th IEEE International Symposium (San Francisco, Cal-ifornia, USA, August 7–9, 2001). IEEE Computer Society, P. 406–415.
8. 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.
9. Muthuvelu, N. A, 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.
10. 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.
11. 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.
12. 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.
13. El-Rewini, H., Lewis, T., & Ali, H. (2010). Task Scheduling in Parallel and Distributed Systems . Prentice Hall, 290 p.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. Liou, J. A., & Palis, M.A. (1996). 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.
21. Avrunin, O.H., Bodianskyi, Ye.V., Kalashnyk, M.V., Semenets, V.V., & Filatov, V.O. (2018). Modern intelligent technologies of functional medical diagnostics. Kharkiv: KhNURE [in Ukrainian].
Citations
1. Минайленко Р.М. Паралельні та розподілені обчислення: навч. посіб. Кропивницький: Видавець Лисенко В. Ф., 2021. 153 с.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Sys-tems / H. Chen, M. Maheswaran . 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.
9. A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids / N. Muthuvelu, J. Liu et al. rid 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.
10. 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.
11. 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.
12. 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.
13. El-Rewini, H., Lewis, T., Ali, H. Task Scheduling in Parallel and Distributed Systems . Prentice Hall, 2010. 290 p.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. Yang, T. 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.
20. Liou, J. A., Palis, M.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.
21. Сучасні інтелектуальні технології функціональної медичної діагностики / О.Г. Аврунін, Є.В. Бодянський, М.В. Калашник та ін. Харків: ХНУРЕ, 2018. 236 с.