Two new fast heuristics for mapping parallel applications on cloud computing |
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| Affiliation: | 1. Saga University, Japan;2. University of Tripoli, Libya;1. Instituto Politécnico Nacional, Centro de Investigación en Biotecnología Aplicada, Carretera Estatal Santa Inés Tecuexcomac, Tepetitla, km 1.5, Tepetitla de Lardizábal, Tlaxcala C.P. 90700, Mexico;2. Universidad Politécnica de Tlaxcala, A. Universidad Politécnica No.1 San Pedro Xalcaltzinco, 90180 Tepeyanco, Tlaxcala, Mexico;1. Department of Physics, Università degli Studi di Milano, Italy;2. CNR-IFN, Milano, Italy;3. Department of Materials Science, Università degli Studi di Milano-Bicocca, Italy;1. Institute of Molecular Science, Key Laboratory of Chemical Biology and Molecular Engineering of the Education Ministry, Shanxi University, Taiyuan, Shanxi 030006, People’s Republic of China;2. Key Laboratory of Materials for Energy Conversion and Storage of Shanxi Province, Shanxi University, Taiyuan, Shanxi 030006, People’s Republic of China;1. Kotelnikov Institute of Radio Engineering and Electronics of RAS, Moscow 125009, Russia;2. Kurnakov Institute of General and Inorganic Chemistry of RAS, Moscow 119991, Russia;3. Kotelnikov Institute of Radio Engineering and Electronics of RAS, Saratov Branch, Saratov 410019, Russia;4. Institute for Photonics and Nanotechnologies, IFN-CNR, Via Cineto Romano 42, 00156 Rome, Italy;5. Management School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, PR China;1. Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;2. Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 1-1 Idaigaoka, Hasamamachi, Yufu 879-5593, Oita, Japan;3. Division of Diabetes Metabolism and Endocrinology, Department of Internal Medicine, The Jikei University School of Medicine, 3-19-18 Nishishimbashi, Minato-ku, Tokyo 105-8471, Japan |
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| Abstract: | In this paper two new heuristics, named Min–min-C and Max–min-C, are proposed able to provide near-optimal solutions to the mapping of parallel applications, modeled as Task Interaction Graphs, on computational clouds. The aim of these heuristics is to determine mapping solutions which allow exploiting at best the available cloud resources to execute such applications concurrently with the other cloud services.Differently from their originating Min–min and Max–min models, the two introduced heuristics take also communications into account. Their effectiveness is assessed on a set of artificial mapping problems differing in applications and in node working conditions. The analysis, carried out also by means of statistical tests, reveals the robustness of the two algorithms proposed in coping with the mapping of small- and medium-sized high performance computing applications on non-dedicated cloud nodes. |
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| Keywords: | Cloud computing Mapping Communicating tasks Heuristics |
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