Scheduling Models, Methods and Algorithms in Grid and Cloud Computing
Keywords:
scheduling, distributed computing, metascheduler, effectiveness
Abstract
Certain intricate problems, e.g., processing the results of physical experiments in the large hadron collider at the CERN, utilize significant distributed computing resources, part of which is shared with their owners. This factor, even within the framework of virtual organizations, causes competition for utilization of resources between both independent users and global (users’) and local job flows of computing resource owners. This circumstance adds much difficulty to the problem of providing the required quality of service in scalable computing. The presently known scheduling algorithms, their combinations and heuristics do not offer tools for producing efficient schedules under the conditions of heterogeneous distributed environments with a dynamically changing composition of computing resources. Under such conditions, so called economic models for allocating non-dedicated resources and for scheduling distributed computing turn out to be highly efficient in fields like grid and cloud computing, and multiagent systems. The article analyzes the current state of investigations in the field of methods and algorithms for efficiently scheduling competing flows of structured and parallel jobs in distributed heterogeneous computing environments, namely, in grid infrastructures and in cloud services. Primary attention is paid to methods and tools for scheduling intricate jobs and their flows with due regard to the preferences of stakeholders (users, resource owners, and administrators), and specific features of applications. With these factors duly taken into account, more efficient utilization of the resources in distributed computing environments may be achieved. The article presents an approach for comprehensively solving the problem of scheduling intricate jobs and their flows. Execution of a user job involves the need to allocate a set of simultaneously accessible computing resources (slot windows). The problem of selecting suitable resources is complicated by such factors as the availability of local high-priority jobs, the dynamics of resources utilization, and advanced reservations. The computing node load schedules are kept in line with the actual demands by means of a scheduling horizon. Online rescheduling implies the need to minimize the computational complexity of the relevant algorithms. Competition among the multitude of different job flows is a factor that degrades the level of resource accessibility and decreases the number of alternative scheduling options. One possible approach for solving this problem is to use anticipation scheduling, a method that makes it possible to increase the number of possible alternative schedules by means of special slot selection mechanisms. The anticipation scheduling procedure opens the possibility to evaluate the efficiency of different resource combinations. The obtained data serve as a basis for producing a feasible job flow execution schedule close to the optimal solution.
References
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Для цитирования: Топорков В.В., Емельянов Д.М. Модели, методы и алгоритмы планирования в грид и облачных вычислениях // Вестник МЭИ. 2018. № 6. С. 75—86. DOI: 10.24160/1993-6982-2018-6-75-86.
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1. Dimitriadou S.K., Karatza H.D. Job Scheduling in a Distributed System Using Backfilling with Inaccurate Runtime Computations. Proc. Intern. Conf. Complex, Intelligent and Software Intensive Syst. Krakow, 2010:329—336.
2. Toporkov V.V. Heuristic Strategies for Preferencebased Scheduling in Virtual Organizations of Utility Grids. J. Ambient Intelligence and Humanized Comp. 2015;6(6):733—740.
3. Buyya R., Abramson D., Giddy J. Economic Models for Resource Management and Scheduling in Grid Computing. J. Concurrency and Computation. 2002; 14 (5):1507—1542.
4. Kurowski K., Nabrzyski J., Oleksiak A., Weglarz J. Multicriteria Aspects of Grid Resource Management. Grid Resource Management. International Series in Operations Research & Management Sci. 2003;64:271— 293.
5. Rodero I. e. a. Enabling Interoperability among Grid Meta-schedulers. J. Grid Comp. 2013;11 (2):311—336.
6. Ernemann C., Hamscher V., Yahyapour R. Economic Scheduling in Grid Computing. DJSSPP. 2002;18:128—152.
7. Baranov A., Telegin P., Tikhomirov A. Comparison of Auction Methods for Job Scheduling with Absolute Priorities. PaCT. LNCS. 2017;10421:387—395.
8. Rzadca K., Trystram D., Wierzbicki A. Fair Gametheoretic Resource Management in Dedicated Grids. IEEE Intern. Symp. Cluster Comp. and the Grid. Rio De Janeiro, 2007:343—350.
9. Vasile M., Pop F., Tutueanu R., Cristea V., Kolodziej J. Resource-aware Hybrid Scheduling Algorithm in Heterogeneous Distributed Computing. J. Future Generation Comp. Syst. 2015;51:61—71.
10. Penmatsa S., Chronopoulos A.T. Cost Minimization in Utility Computing Systems. J. Concurrency and Computation: Practice and Experience. 2014;16 (1):287—307.
11. Mutz A., Wolski R., Brevik J. Eliciting Honest Value Information in a Batch-queue Environment. Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. N.-Y., 2007:291—297.
12. Blanco H., Guirado F., Lrida, J.L., Albornoz V.M. MIP Model Scheduling for Multi-clusters. Euro-par. Heidelberg: Springer, 2012:196—206.
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17. EGI Federated Clouds Task Force [Ofits. Sayt] https://https://www.egi.eu (Data Obrashcheniya 19.07.2017).
18. Carroll T., Grosu D. Divisible Load Scheduling: An Approach Using Coalitional Games. Proc. VI Interna. Symp. Parallel and Distributed Comp. 2007:36—45.
19. Kim K., Buyya R. Fair Resource Sharing in Hierarchical Virtual Organizations for Global Grids. Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. Austin, 2007:50—57.
20. Skowron P., Rzadca K. Non-monetary Fair Scheduling Cooperative Game Theory Approach. Proc. 25 Annual ACM Symp. Parallelism in Algorithms and Architectures. N.-Y., 2013:288—297.
21. Dalheimer M. Pfreundt F., Merz P. Agent-based Grid Scheduling with Calana. Proc. Parallel Processing and Appl. Math. VI Intern. Conf. 2006:741—750.
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27. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms in Distributed Computing. Journal of Supercomputing. 2014;69 (1):53—60.
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41. Toporkov V. e. a. Preference-based Fair Resource Sharing and Scheduling Optimization in Grid VOs. Procedia Computer Sci. 2014;29:831—843.
42. Toporkov V. e. a. Heuristic Cycle-based Scheduling with Backfilling for Large-scale Distributed Environments. Advances in Intelligent Systems and Computing (AISC). Heidelberg: Springer, 2014;286:455—465.
43. Toporkov V., Tselishchev A., Yemelyanov D., Potekhin P. Metascheduling Strategies in Distributed Computing with Non-dedicated Resources. Dependability Problems of Complex Information Systems, Advances in Intelligent Syst. and Comp. (AISC). Springer International Publ., 2014;307:129—148.
44. Toporkov V., Yemelyanov D, Tselishchev A. Effective Slot Selection and Co-allocation Algorithms for Economic Scheduling in Distributed Computing. Procedia Comp. Sci. 2013;18:2424—2427.
45. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms for Economic Scheduling in Distributed Computing with High QoS Rates. New Results in Computer Systems and Dependability. AISC. Heidelberg: Springer, 2013;224:459—468.
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49. Toporkov V., Toporkova A., Bobchenkov A., Yemelyanov D. Resource Selection Algorithms for Economic Scheduling in Distributed Systems. Proc.Comp. Sci. Elsevier, 2011;4:2267—2276.
50. Graham R.L., Lawler E.L., Lenstra J.K., Kan A.R. Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals Discrete Mathematics. 1979;5:287—326.
51. Yu J., Buyya R., Ramamohanarao K. Workflow Scheduling Algorithms for Grid Computing. Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence. Berlin: Springer, 2008;146:173—214.
52. Casanova H., Giersch A., Legrand A., Quinson M., Suter F. Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms. J. Parallel and Distributed Comp. 2014;74 (10):2899—2917.
53. Chen W., Deelman E. Workflowsim: A toolkit for Simulating Scientific Workflows in Distributed Environments. IEEE E-science (e-science) VIII Intern. Conf. 2012:1—8.
54. Topcuoglu H., Hariri S., Wu M.Y. Performanceeffective and Low-complexity Task Scheduling For Heterogeneous Computing. IEEE Trans. Parallel and Distributed Syst. 2002;13 (3):260—274.
55. Hagras T., Janecek J. A Simple Scheduling Heuristic for Heterogeneous Computing Environment. Proc. II Intern. Symp. Parallel and Distributed Comp. 2003:104—110.
56. Bittencourt L.F., Sakellariou R., Madeira E.R.M. Dag Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm. Proc. XVIII Euromicro Conf. Parallel, Distributed and Networkbased. 2010:27—34.
57. Arabnejad H., Barbosa J.G. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table. IEEE Trans. Parallel and Distributed Syst. 2014;25 (3):682—694.
58. Armstrong R., Hensgen D. Kidd T. The Rela-tive Performance of Various Mapping Algorithms is Independent of Sizable Variances in Run-Time Predictions. IEEE Heterogeneous Computing Workshop. 1998:79—87.
59. Maheswaran M. e. a. Dynamic Matching and Scheduling of a Class of Independent Tasks Onto Heterogeneous Computing Systems. IEEE Heterogeneous Computing Workshop. IEEE. 1999:30—44.
60. Freund R.F. e. a. Scheduling Resources In Multiuser, Heterogeneous, Computing Environments with Smart-net. IEEE Heterogeneous Computing Workshop. 1998:184—199.
61. Nazarenko A., Sukhoroslov O. An Experimental Study of Workow Scheduling Algorithms for Heterogeneous Systems. PaCT. LNCS. Springer Intern. Publ., 2017; 10421:327—341.
62.Radchenko G.I., Lyzhin I.A., Nepovinnyh E.A. Implementatsiya i Sravnitel'noe Testirovanie Algoritma Problemno-orientirovannogo Planirovaniya Potokovyh Prilozheniy v Oblachnyh Sredah PO-HEFT. Superkomp'yuternye Dni v Rossii: Trudy Mezhdunar. Konf. 2016: 165—179. (in Russian).
63. Toporkov V., Yemelyanov D., Bobchenkov A. Job-flow Anticipation Scheduling in Grid. Proc. Comp. Sci. 2017;108:1394—1403.
64. Toporkov V., Yemelyanov D., Toporkova A. Anticipation Preference-based Heuristic Scheduling in Grid Virtual Organizations. Proc. Intern. Conf. Parallel Processing Workshops. 2017:271—280.
65. Toporkov V., Toporkova A., Yemelyanov D. Heuristic of Anticipation for Fair Scheduling and Resource Allocation in Grid VOs. Studies in Computational Intelligence. Springer Verlag, 2018;737:27—37.
---
For citation: Toporkov V.V., Yemelyanov D.M. Scheduling Models, Methods and Algorithms in Grid and Cloud Computing. MPEI Vestnik. 2018;6:75—86. (in Russian). DOI: 10.24160/1993-6982-2018-6-75-86.
2. Toporkov V.V. Heuristic Strategies for Preferencebased Scheduling in Virtual Organizations of Utility Grids // J. Ambient Intelligence and Humanized Comp. 2015. V. 6(6). Pp. 733—740.
3. Buyya R., Abramson D., Giddy J. Economic Models for Resource Management and Scheduling in Grid Computing // J. Concurrency and Computation. 2002. V. 14 (5). Pp. 1507—1542.
4. Kurowski K., Nabrzyski J., Oleksiak A., Weglarz J. Multicriteria Aspects of Grid Resource Management // Grid Resource Management. International Series in Operations Research & Management Sci. 2003. V. 64. Pp. 271—293.
5. Rodero I. e. a. Enabling Interoperability among Grid Meta-schedulers // J. Grid Comp. 2013. V. 11 (2). P. 311—336.
6. Ernemann C., Hamscher V., Yahyapour R. Economic Scheduling in Grid Computing // DJSSPP. 2002. V. 18. Pp. 128—152.
7. Baranov A., Telegin P., Tikhomirov A. Comparison of Auction Methods for Job Scheduling with Absolute Priorities // PaCT. LNCS. 2017. V. 10421. Pp. 387—395.
8. Rzadca K., Trystram D., Wierzbicki A. Fair Game-theoretic Resource Management in Dedicated Grids // IEEE Intern. Symp. Cluster Comp. and the Grid. Rio De Janeiro, 2007. Pp. 343—350.
9. Vasile M., Pop F., Tutueanu R., Cristea V., Kolodziej J. Resource-aware Hybrid Scheduling Algorithm in Heterogeneous Distributed Computing // J. Future Generation Comp. Syst. 2015. V. 51. Pp. 61—71.
10. Penmatsa S., Chronopoulos A.T. Cost Minimization in Utility Computing Systems // J. Concurrency and Computation: Practice and Experience. 2014. V. 16 (1). Pp. 287—307.
11. Mutz A., Wolski R., Brevik J. Eliciting Honest Value Information in a Batch-queue Environment // Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. N.-Y., 2007. Pp. 291—297.
12. Blanco H., Guirado F., Lrida, J.L., Albornoz V.M. MIP Model Scheduling for Multi-clusters // Euro-par. Heidelberg: Springer, 2012. Pp. 196—206.
13. Takefusa A., Nakada H., Kudoh T., Tanaka Y. An Advance Reservation-based Co-allocation Algorithm for Distributed Computers and Network Bandwidth on QoS-guaranteed Grids // JSSPP. Lecture Notes in Comp. Sci. Heidelberg: Springer, 2010. V. 6253. Pp. 16—34.
14. Сухорослов О.В. Комбинированное использование высокопроизводительных ресурсов и грид инфраструктур в рамках облачной платформы Everest // Суперкомпьютерные дни в России: Труды Междунар. конф. 2015. С. 706—711.
15. Bencivenni M. e. a. Accessing Grid and Cloud Services Through a Scientific Web Portal // J. Grid Comp. 2015. V. 13. Pp. 159—175.
16. Ronchieri E. e. a. Accessing Scientific Applications through the WNoDeS Cloud Virtualization Framework // Proc. Intern. Symp. Grids and Clouds (ISGC). Taipei, 2013. Pp. 3—12.
17. EGI Federated Clouds Task Force [Офиц. сайт] https://https://www.egi.eu (дата обращения 19.07.2017).
18. Carroll T., Grosu D. Divisible Load Scheduling: An Approach Using Coalitional Games // Proc. VI Interna. Symp. Parallel and Distributed Comp. 2007. Pp. 36—45.
19. Kim K., Buyya R. Fair Resource Sharing in Hierarchical Virtual Organizations for Global Grids // Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. Austin, 2007. Pp. 50—57.
20. Skowron P., Rzadca K. Non-monetary Fair Scheduling Cooperative Game Theory Approach // Proc. 25 Annual ACM Symp. Parallelism in Algorithms and Architectures. N.-Y., 2013. Pp. 288—297.
21. Dalheimer M. Pfreundt F., Merz P. Agent-based Grid Scheduling with Calana // Proc. Parallel Processing and Appl. Math. VI Intern. Conf. 2006. Pp. 741—750.
22. Jackson D., Snell Q., Clement M. Core Algorithms of the Maui Scheduler // Revised Papers from VII Intern. Workshop on Job Scheduling Strategies for Parallel Proc. 2002. Pp. 87—102.
23. Thain T., Livny M. Distributed Computing in Practice: the Condor Experience // J. Concurrency and Computation: Practice and Experience. 2005. V. 17. Pp. 323—356.
24. Богданова В.Г., Бычков И.В., Корсуков А.С., Опарин Г.А., Феоктистов А.Г. Мультиагентный подход к управлению распределенными вычислениями в кластерной GRID-системе // Известия РАН. Серия «Теория и системы управления». 2014. № 5. С. 95—105.
25. Toporkov V. e. a. Metascheduling and Heuristic Co-allocation Strategies in Distributed Computing // J. Comp. and Informatics. 2015. V. 34 (1). Pp. 45—76.
26. Toporkov V., Yemelyanov D., Bobchenkov A., Potekhin P. Fair Resource Allocation and Metascheduling in Grid with VO Stakeholders Preferences // IEEE 45 Intern. Conf. on Parallel Processing Workshops. 2016. Pp. 375—384.
27. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms in Distributed Computing // Journal of Supercomputing. 2014. V. 69 (1). Pp. 53—60.
28. Farahabady M.H., Lee Y.C., Zomaya A.Y. Pareto-optimal Cloud Bursting // IEEE Transac. on Parallel and Distributed Systs. 2014. V. 25. Pp. 2670—2682.
29. Cafaro M., Mirto M., Aloisi, G. Preference-Based Matchmaking of Grid Resources with CP-Nets // J. Grid Comp. 2013. V. 11 (2). Pp. 211—237.
30. Garg S.K., Konugurthi P., Buyya R. A Linear Programming-driven Genetic Algorithm for Metascheduling on Utility Grids // J. Par., Emergent and Distr. Syst. 2011. V. 26. Pp. 493—517.
31. Aida K., Casanova H. Scheduling Mixed-parallel Applications with Advance Reservations // 17 th IEEE Int. Symposium on HPDC. N.-Y., 2008. Pp. 65—74.
32. Ando S., Aida K. Evaluation of Scheduling Algorithms for Advance Reservations // Information Proc. Soc. Japan SIG Notes. 2007. Pp. 37—42.
33. Elmroth E., Tordsson J. A Standards-based Grid Resource Brokering Service Supporting Advance Reservations, Coallocation and Cross-Grid Interoperability // J. of Concurrency and Computation. 2009. V. 21(18). Pp. 2298—2335.
34. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms in Distributed Computing with Non-dedicated and Heterogeneous Resources // PaCT. LNCS. Heidelberg: Springer, 2013. V. 7979. Pp. 120—134.
35. Azzedin F., Maheswaran M., Arnason N. A Synchronous Co-allocation Mechanism for Grid Computing Systems // Cluster Comp. 2004. V. 7. Pp. 39—49.
36. Castillo C., Rouskas G.N., Harfoush K. Resource Co-allocation for Large-scale Distributed Environments // 18 th ACM Intern. Symp. High Performance Distributed Comp. N.-Y. 2009. Pp. 137—150.
37. Olteanu A., Pop F., Dobre C., Cristea V. A Dynamic Rescheduling Algorithm for Resource Management in Large Scale Dependable Distributed Systems // Computers and Mathematics with Appl. 2012. V. 63 (9). Pp. 1409—1423.
38. Топорков В.В., Емельянов Д.М. Экономическая модель планирования и справедливого разделения ресурсов в распределенных вычислениях // Программирование. 2014. № 1. С. 54—65.
39. Топорков В.В., Бобченков А.В., Емельянов Д.М., Целищев А.С. Методы и эвристики планирования в распределенных вычислениях с неотчуждаемыми ресурсами // Вестник ЮУрГУ. Cерия «Вычислительная математика и информатика». 2014. Т. 3. № 2. С. 43—62.
40. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Heuristic Co-allocation Strategies in Distributed Computing with Non-dedicated Resources // Studies in Computational Intelligence. Heidelberg: Springer, 2014. V. 511. Pp. 109—118.
41. Toporkov V. e. a. Preference-based Fair Resource Sharing and Scheduling Optimization in Grid VOs // Procedia Computer Sci. 2014. V. 29. Pp. 831—843.
42. Toporkov V. e. a. Heuristic Cycle-based Scheduling with Backfilling for Large-scale Distributed Environments // Advances in Intelligent Systems and Computing (AISC). Heidelberg: Springer, 2014. V. 286. Pp. 455—465.
43. Toporkov V., Tselishchev A., Yemelyanov D., Potekhin P. Metascheduling Strategies in Distributed Computing with Non-dedicated Resources // Dependability Problems of Complex Information Systems, Advances in Intelligent Syst. and Comp. (AISC). Springer International Publ., 2014. V. 307. Pp. 129—148.
44. Toporkov V., Yemelyanov D, Tselishchev A. Effective Slot Selection and Co-allocation Algorithms for Economic Scheduling in Distributed Computing // Procedia Comp. Sci. 2013. V. 18. Pp. 2424—2427.
45. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms for Economic Scheduling in Distributed Computing with High QoS Rates // New Results in Computer Systems and Dependability. AISC. Heidelberg: Springer, 2013. V. 224. Pp. 459—468.
46. Топорков В.В. Пакетная обработка заданий в распределенных вычислительных средах с неотчуждаемыми ресурсами // Автоматика и телемеханика. 2012. № 10. С. 52—70.
47. Toporkov V., Tselishchev A., Yemelyanov D., Bobchenkov A. Composite Scheduling Strategies in Distributed Computing with Non-dedicated Resources // Procedia Comp. Sci. Elsevier, 2012. V. 9. Pp. 176—185.
48. Toporkov V., Tselishchev A., Yemelyanov D., Bobchenkov A. Dependable Strategies for Job-flows Dispatching and Scheduling in Virtual Organizations of Distributed Computing Environments // Complex Systems and Dependability. Berlin, Heidelberg: Springer-Verlag, 2012;170:240—255.
49. Toporkov V., Toporkova A., Bobchenkov A., Yemelyanov D. Resource Selection Algorithms for Economic Scheduling in Distributed Systems // Proc. Comp. Sci. Elsevier, 2011. V. 4. Pp. 2267—2276.
50. Graham R.L., Lawler E.L., Lenstra J.K., Kan A.R. Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey // Annals Discrete Mathematics. 1979;5:287—326.
51. Yu J., Buyya R., Ramamohanarao K. Workflow Scheduling Algorithms for Grid Computing // Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence. Berlin: Springer, 2008. V. 146. Pp. 173—214.
52. Casanova H., Giersch A., Legrand A., Quinson M., Suter F. Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms // J. Parallel and Distributed Comp. 2014. V. 74 (10). Pp. 2899—2917.
53. Chen W., Deelman E. Workflowsim: A toolkit for Simulating Scientific Workflows in Distributed Environments // IEEE E-science (e-science) VIII Intern. Conf. 2012. Pp. 1—8.
54. Topcuoglu H., Hariri S., Wu M.Y. Performanceeffective and Low-complexity Task Scheduling For Heterogeneous Computing // IEEE Trans. Parallel and Distributed Syst. 2002. V. 13 (3). Pp. 260—274.
55. Hagras T., Janecek J. A Simple Scheduling Heuristic for Heterogeneous Computing Environment // Proc. II Intern. Symp. Parallel and Distributed Comp. 2003. Pp. 104—110.
56. Bittencourt L.F., Sakellariou R., Madeira E.R.M. Dag Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm // Proc. XVIII Euromicro Conf. Parallel, Distributed and Networkbased. 2010. Pp. 27—34.
57. Arabnejad H., Barbosa J.G. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table // IEEE Trans. Parallel and Distributed Syst. 2014. V. 25 (3). Pp. 682—694.
58. Armstrong R., Hensgen D. Kidd T. The Rela-tive Performance of Various Mapping Algorithms is Independent of Sizable Variances in Run-Time Predictions // IEEE Heterogeneous Computing Workshop. 1998. Pp. 79—87.
59. Maheswaran M. e. a. Dynamic Matching and Scheduling of a Class of Independent Tasks Onto Heterogeneous Computing Systems // IEEE Heterogeneous Computing Workshop. IEEE. 1999. Pp. 30—44.
60. Freund R.F. e. a. Scheduling Resources In Multiuser, Heterogeneous, Computing Environments with Smart-net // IEEE Heterogeneous Computing Workshop. 1998. Pp. 184—199.
61. Nazarenko A., Sukhoroslov O. An Experimental Study of Workow Scheduling Algorithms for Heterogeneous Systems // PaCT. LNCS. Springer Intern. Publ., 2017. V. 10421. Pp. 327—341.
62. Радченко Г.И., Лыжин И.А., Неповинных Е.А. Имплементация и сравнительное тестирование алгоритма проблемно-ориентированного планирования потоковых приложений в облачных средах PO-HEFT // Суперкомпьютерные дни в России: Труды Междунар. конф. 2016. С. 165—179.
63. Toporkov V., Yemelyanov D., Bobchenkov A. Job-flow Anticipation Scheduling in Grid // Proc. Comp. Sci. 2017. V. 108. Pp. 1394—1403.
64. Toporkov V., Yemelyanov D., Toporkova A. Anticipation Preference-based Heuristic Scheduling in Grid Virtual Organizations // Proc. Intern. Conf. Parallel Processing Workshops. 2017. Pp. 271—280.
65. Toporkov V., Toporkova A., Yemelyanov D. Heuristic of Anticipation for Fair Scheduling and Resource Allocation in Grid VOs // Studies in Computational Intelligence. Springer Verlag, 2018. V. 737. Pp. 27—37.
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Для цитирования: Топорков В.В., Емельянов Д.М. Модели, методы и алгоритмы планирования в грид и облачных вычислениях // Вестник МЭИ. 2018. № 6. С. 75—86. DOI: 10.24160/1993-6982-2018-6-75-86.
#
1. Dimitriadou S.K., Karatza H.D. Job Scheduling in a Distributed System Using Backfilling with Inaccurate Runtime Computations. Proc. Intern. Conf. Complex, Intelligent and Software Intensive Syst. Krakow, 2010:329—336.
2. Toporkov V.V. Heuristic Strategies for Preferencebased Scheduling in Virtual Organizations of Utility Grids. J. Ambient Intelligence and Humanized Comp. 2015;6(6):733—740.
3. Buyya R., Abramson D., Giddy J. Economic Models for Resource Management and Scheduling in Grid Computing. J. Concurrency and Computation. 2002; 14 (5):1507—1542.
4. Kurowski K., Nabrzyski J., Oleksiak A., Weglarz J. Multicriteria Aspects of Grid Resource Management. Grid Resource Management. International Series in Operations Research & Management Sci. 2003;64:271— 293.
5. Rodero I. e. a. Enabling Interoperability among Grid Meta-schedulers. J. Grid Comp. 2013;11 (2):311—336.
6. Ernemann C., Hamscher V., Yahyapour R. Economic Scheduling in Grid Computing. DJSSPP. 2002;18:128—152.
7. Baranov A., Telegin P., Tikhomirov A. Comparison of Auction Methods for Job Scheduling with Absolute Priorities. PaCT. LNCS. 2017;10421:387—395.
8. Rzadca K., Trystram D., Wierzbicki A. Fair Gametheoretic Resource Management in Dedicated Grids. IEEE Intern. Symp. Cluster Comp. and the Grid. Rio De Janeiro, 2007:343—350.
9. Vasile M., Pop F., Tutueanu R., Cristea V., Kolodziej J. Resource-aware Hybrid Scheduling Algorithm in Heterogeneous Distributed Computing. J. Future Generation Comp. Syst. 2015;51:61—71.
10. Penmatsa S., Chronopoulos A.T. Cost Minimization in Utility Computing Systems. J. Concurrency and Computation: Practice and Experience. 2014;16 (1):287—307.
11. Mutz A., Wolski R., Brevik J. Eliciting Honest Value Information in a Batch-queue Environment. Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. N.-Y., 2007:291—297.
12. Blanco H., Guirado F., Lrida, J.L., Albornoz V.M. MIP Model Scheduling for Multi-clusters. Euro-par. Heidelberg: Springer, 2012:196—206.
13. Takefusa A., Nakada H., Kudoh T., Tanaka Y. An Advance Reservation-based Co-allocation Algorithm for Distributed Computers and Network Bandwidth on QoS-guaranteed Grids. JSSPP. Lecture Notes in Comp. Sci. Heidelberg: Springer, 2010;6253:16—34.
14.Suhoroslov O.V. Kombinirovannoe Ispol'zovanie Vysokoproizvoditel'nyh Resursov i Grid Infrastruktur v Ramkah Oblachnoy Platformy Everest. Superkomp'yuternye Dni v Rossii: Trudy Mezhdunar. Konf. 2015:706—711. (in Russian).
15. Bencivenni M. e. a. Accessing Grid and Cloud Services Through a Scientific Web Portal. J. Grid Comp. 2015;13:159—175.
16. Ronchieri E. e. a. Accessing Scientific Applications through the WNoDeS Cloud Virtualization Framework. Proc. Intern. Symp. Grids and Clouds (ISGC). Taipei, 2013:3—12.
17. EGI Federated Clouds Task Force [Ofits. Sayt] https://https://www.egi.eu (Data Obrashcheniya 19.07.2017).
18. Carroll T., Grosu D. Divisible Load Scheduling: An Approach Using Coalitional Games. Proc. VI Interna. Symp. Parallel and Distributed Comp. 2007:36—45.
19. Kim K., Buyya R. Fair Resource Sharing in Hierarchical Virtual Organizations for Global Grids. Proc. VIII IEEE/ACM Intern. Conf. Grid Comp. Austin, 2007:50—57.
20. Skowron P., Rzadca K. Non-monetary Fair Scheduling Cooperative Game Theory Approach. Proc. 25 Annual ACM Symp. Parallelism in Algorithms and Architectures. N.-Y., 2013:288—297.
21. Dalheimer M. Pfreundt F., Merz P. Agent-based Grid Scheduling with Calana. Proc. Parallel Processing and Appl. Math. VI Intern. Conf. 2006:741—750.
22. Jackson D., Snell Q., Clement M. Core Algorithms of the Maui Scheduler. Revised Papers from VII Intern. Workshop on Job Scheduling Strategies for Parallel Proc. 2002:87—102.
23. Thain T., Livny M. Distributed Computing in Practice: the Condor Experience. J. Concurrency and Computation: Practice and Experience. 2005;17:323—356.
24. Bogdanova V.G., Bychkov I.V., Korsukov A.S., Oparin G.A.,Feoktistov A.G. Mul'tiagentnyy podhod k upravleniyu raspredelennymi vychisleniyami v klasternoy GRID-sisteme. Izvestiya RAN. Seriya «Teoriya i sistemy upravleniya». 2014;5:95—105. (in Russian).
25. Toporkov V. e. a. Metascheduling and Heuristic Co-allocation Strategies in Distributed Computing. J. Comp. and Informatics. 2015;34 (1):45—76.
26. Toporkov V., Yemelyanov D., Bobchenkov A., Potekhin P. Fair Resource Allocation and Metascheduling in Grid with VO Stakeholders Preferences. IEEE 45 Intern. Conf. on Parallel Processing Workshops. 2016:375—384.
27. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms in Distributed Computing. Journal of Supercomputing. 2014;69 (1):53—60.
28. Farahabady M.H., Lee Y.C., Zomaya A.Y. Pareto-optimal Cloud Bursting. IEEE Transac. on Parallel and Distributed Systs. 2014;25:2670—2682.
29. Cafaro M., Mirto M., Aloisi, G. Preference-Based Matchmaking of Grid Resources with CP-Nets. J. Grid Comp. 2013;11 (2):211—237.
30. Garg S.K., Konugurthi P., Buyya R. A Linear Programming-driven Genetic Algorithm for Metascheduling on Utility Grids. J. Par., Emergent and Distr. Syst. 2011;26:493—517.
31. Aida K., Casanova H. Scheduling Mixed-parallel Applications with Advance Reservations. 17 th IEEE Int. Symposium on HPDC. N.-Y., 2008:65—74.
32. Ando S., Aida K. Evaluation of Scheduling Algorithms for Advance Reservations. Information Proc. Soc. Japan SIG Notes. 2007:37—42.
33. Elmroth E., Tordsson J. A Standards-based Grid Resource Brokering Service Supporting Advance Reservations, Coallocation and Cross-Grid Interoperability. J. of Concurrency and Computation. 2009;21(18): 2298—2335.
34. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms in Distributed Computing with Non-dedicated and Heterogeneous Resources. PaCT. LNCS. Heidelberg: Springer, 2013;7979: 120—134.
35. Azzedin F., Maheswaran M., Arnason N. A Synchronous Co-allocation Mechanism for Grid Computing Systems. Cluster Comp. 2004;7:39—49.
36. Castillo C., Rouskas G.N., Harfoush K. Resource Co-allocation for Large-scale Distributed Environments. 18 th ACM Intern. Symp. High Performance Distributed Comp. N.-Y. 2009:137—150.
37. Olteanu A., Pop F., Dobre C., Criste. V. A Dynamic Rescheduling Algorithm for Resource Management in Large Scale Dependable Distributed Systems. Computers and Mathematics with Appl. 2012;63 (9):1409—1423.
38. Toporkov V.V., Emel'yanov D.M. Ekonomicheskaya Model' Planirovaniya i Spravedlivogo Razdeleniya Resursov V Raspredelennyh Vychisleniyah. Programmirovanie. 2014;1:54—65. (in Russian).
39. Toporkov V.V., Bobchenkov A.V., Emel'yanov D.M., TSelishchev A.S. Metody i Evristiki Planirovaniya V Raspredelennyh Vychisleniyah S Neotchuzhdaemymi Resursami. Vestnik YUUrGU. Seriya «Vychislitel'naya Matematika i Informatika». 2014;3;2:43—62. (in Russian).
40. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Heuristic Co-allocation Strategies in Distributed Computing with Non-dedicated Resources. Studies in Computational Intelligence. Heidelberg: Springer, 2014;511:109—118.
41. Toporkov V. e. a. Preference-based Fair Resource Sharing and Scheduling Optimization in Grid VOs. Procedia Computer Sci. 2014;29:831—843.
42. Toporkov V. e. a. Heuristic Cycle-based Scheduling with Backfilling for Large-scale Distributed Environments. Advances in Intelligent Systems and Computing (AISC). Heidelberg: Springer, 2014;286:455—465.
43. Toporkov V., Tselishchev A., Yemelyanov D., Potekhin P. Metascheduling Strategies in Distributed Computing with Non-dedicated Resources. Dependability Problems of Complex Information Systems, Advances in Intelligent Syst. and Comp. (AISC). Springer International Publ., 2014;307:129—148.
44. Toporkov V., Yemelyanov D, Tselishchev A. Effective Slot Selection and Co-allocation Algorithms for Economic Scheduling in Distributed Computing. Procedia Comp. Sci. 2013;18:2424—2427.
45. Toporkov V., Toporkova A., Tselishchev A., Yemelyanov D. Slot Selection Algorithms for Economic Scheduling in Distributed Computing with High QoS Rates. New Results in Computer Systems and Dependability. AISC. Heidelberg: Springer, 2013;224:459—468.
46. Toporkov V.V. Paketnaya Obrabotka Zada-niy V Raspredelennyh Vychislitel'nyh Sredah S Neotchuzhdaemymi Resursami. Avtomatika i Telemekhanika. 2012;10: 52—70. (in Russian).
47. Toporkov V., Tselishchev A., Yemelyanov D., Bobchenkov A. Composite Scheduling Strategies in Distributed Computing with Non-dedicated Resources. Procedia Comp. Sci. Elsevier, 2012;9:176—185.
48. Toporkov V., Tselishchev A., Yemelyanov D., Bobchenkov A. Dependable Strategies for Job-flows Dispatching and Scheduling in Virtual Organizations of Distributed Computing Environments . Complex Systems and Dependability. Berlin, Heidelberg: Springer-Verlag, 2012;170:240—255.
49. Toporkov V., Toporkova A., Bobchenkov A., Yemelyanov D. Resource Selection Algorithms for Economic Scheduling in Distributed Systems. Proc.Comp. Sci. Elsevier, 2011;4:2267—2276.
50. Graham R.L., Lawler E.L., Lenstra J.K., Kan A.R. Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals Discrete Mathematics. 1979;5:287—326.
51. Yu J., Buyya R., Ramamohanarao K. Workflow Scheduling Algorithms for Grid Computing. Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence. Berlin: Springer, 2008;146:173—214.
52. Casanova H., Giersch A., Legrand A., Quinson M., Suter F. Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms. J. Parallel and Distributed Comp. 2014;74 (10):2899—2917.
53. Chen W., Deelman E. Workflowsim: A toolkit for Simulating Scientific Workflows in Distributed Environments. IEEE E-science (e-science) VIII Intern. Conf. 2012:1—8.
54. Topcuoglu H., Hariri S., Wu M.Y. Performanceeffective and Low-complexity Task Scheduling For Heterogeneous Computing. IEEE Trans. Parallel and Distributed Syst. 2002;13 (3):260—274.
55. Hagras T., Janecek J. A Simple Scheduling Heuristic for Heterogeneous Computing Environment. Proc. II Intern. Symp. Parallel and Distributed Comp. 2003:104—110.
56. Bittencourt L.F., Sakellariou R., Madeira E.R.M. Dag Scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm. Proc. XVIII Euromicro Conf. Parallel, Distributed and Networkbased. 2010:27—34.
57. Arabnejad H., Barbosa J.G. List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table. IEEE Trans. Parallel and Distributed Syst. 2014;25 (3):682—694.
58. Armstrong R., Hensgen D. Kidd T. The Rela-tive Performance of Various Mapping Algorithms is Independent of Sizable Variances in Run-Time Predictions. IEEE Heterogeneous Computing Workshop. 1998:79—87.
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For citation: Toporkov V.V., Yemelyanov D.M. Scheduling Models, Methods and Algorithms in Grid and Cloud Computing. MPEI Vestnik. 2018;6:75—86. (in Russian). DOI: 10.24160/1993-6982-2018-6-75-86.
Published
2019-02-18
Issue
Section
Informatics, computer engineering and control (05.13.00)
