Hierarchical Cloud Service Workflow Scheduling Optimization Schema Using Heuristic Generic Algorithmg

• Autorzy: Cheng B.

Warianty tytułu

PL

Optymalizacja planowania przepływu hierarchicznego serwisów w chmurze przy wykorzystaniu algorytmu genetycznego

• Języki publikacji: EN

Abstrakty

EN

The rapid growth of visualization technologies and Cloud computing have opened a new paradigm for utilizing the existing resource pools for on-demand and scalable computing, which enables the workflow management system to meet quality-of-service (QoS) requirements of the applications. It becomes crucial for cloud customers to choose the best Cloud services in order to minimize the running costs, and how to match and select the optimum cloud service will be a challenge problem. In this paper, we present an efficient Cloud services workflow scheduling and optimization schema using heuristic generic algorithm, and focus on the hierarchical Cloud service workflow scheduling, Cloud workflow tasks parallel split, syntax and semantic based Cloud workflow tasks matching algorithm, and multiple QoS constraints based Cloud workflow scheduling and optimization, and also presents the experiment conducted to evaluate the efficiency of our algorithm.

PL

Rozwój technik wizualizacji I wykorzystanie chmury w obliczeniach komputerowych otworzyło problem jak wykorzystać zasoby danych do obliczeń skalowalnych i na życzenie. Dla klientów chmury jest bardzo ważne wybór jak najlepszego serwisu dla minimalizacji kosztów. W artykule zaprezentowano skuteczne planowanie przepływu serwisów w chmurze przy użyciu algorytmu genetycznego.

Słowa kluczowe

EN

cloud service; workflow; scheduling; optimization; heuristic generic algorithm

PL

chmura obliczeniowa; optymalizacja; algorytm genetyczny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 1b
• Strony: 92--95
• Opis fizyczny: Bibliogr. 10 poz., rys., wykr.

Twórcy

autor

Cheng B.

• State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; Beijing, China,

Bibliografia

• [1] Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 2009, 25(6), 599–616.
• [2] Zhangjun Wu, Xiao Liu, Zhiwei Ni, Dong Yuan, Yun Yang. A market-oriented hierarchical scheduling strategy in cloud workflow systems. The Journal of Supercomputing, 2011, 59(1), 1-38.
• [3] Zhen Ye, Xiaofang Zhou, and Athman Bouguettaya. Genetic Algorithm Based QoS-Aware Service Compositions in Cloud Computing. In Proceedings of Database Systems for Advanced Applications, 2011, 321–334.
• [4] Gerardo Canfora, Massimiliano Di Penta, Raffaele Esposito, Maria Luisa Villani., An Approach for QoS-aware Service Composition based on Genetic Algorithms. In Proceedings of Genetic and Evolutionary Computation Conference,2005.
• [5] Z. Wu, Z. Ni, L. Gu, X. Liu, A Revised Discrete Particle Swarm Optimisation for Cloud Workflow Scheduling. In Proceedings of 2010 International Conference on Computational Intelligence and Security, 2010,184-188.
• [6] OWL-S. DAML Services, http://www.daml.org/services/owl-s/.
• [7] Nebil Ben Mabrouk, Sandrine Beauche, Elena Kuznetsova, Nikolaos Georgantas, and Val erie Issarny. QoS-aware Service Composition in Dynamic Service Oriented Environment. Lecture Notes in Computer Science, 2009, 123- 142
• [8] Yu J, Buyya R. Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program, 2006, (3), 217–230.
• [9] Abraham A, Buyya R. Nature’s heuristics for scheduling jobs on computational grids. In Proceedings of 8th International Conference on Advanced Computing and Communication, 2000, 1-12.
• [10] Zeng L Z, Benatalah B, Dumas M. Quality driven web services composition. In Proceedings of the 12th International Conference on World Wide Web, 2003, 411-421