A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds |
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| Affiliation: | 1. State Key Laboratory for Novel Software Technology, Software Institute, Nanjing University, Nanjing 210093, China;2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, USA;4. School of Computer, Hangzhou Dianzi University, Hangzhou 310018, China;5. Centre for Creative Computing (CCC), Bath Spa University, England, UK;1. State Key Laboratory for Novel Software Technology, Software Institute, Nanjing University, 210093 China;2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 100876 China;3. Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122 USA;4. School of Computer, Hangzhou Dianzi University, Hangzhou 310018 China;1. State Key Laboratory for Novel Software Technology, Software Institute, Nanjing University, 210093, China;2. Department of Computer Science and Engineering, Southern Methodist University, Dallas 75275-0122, TX, USA;3. Human Language Technology Research Institute, University of Texas at Dallas, Dallas 75083-0688, TX, USA |
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| Abstract: | Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm. |
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| Keywords: | Scientific workflow scheduling Cloud computing Big data application Security awareness Particle swarm optimization (PSO) Deadline constraint |
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