A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds
A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds
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DOI：
10.1016/j.future.2015.12.014
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Zhongjin Li;Jidong Ge;Hongji Yang;LiGuo Huang;Haiyang Hu;Hao Hu;B. Luo
中科院分区：
文献类型：
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作者：
Zhongjin Li;Jidong Ge;Hongji Yang;LiGuo Huang;Haiyang Hu;Hao Hu;B. Luo
对于作为大数据应用程序的各种科学工作流程来说，安全性变得越来越重要，并且通常需要在大规模分布式基础设施上执行相当长的时间。云计算平台就是这样一个可以实现资源按需动态扩展的基础设施。然而，基于按使用付费和按小时计费的定价模式，用户应注意从云数据中心租用虚拟机（VM）所产生的成本。同时，工作流任务通常是异构的，需要不同的实例系列（即计算优化、内存优化、存储优化等）。在本文中，我们提出了一种用于云中科学工作流程异构任务的安全和成本感知调度（SCAS）算法。我们提出的算法基于元启发式优化技术，粒子群优化（PSO），其编码策略旨在最小化总工作流执行成本，同时满足截止日期和风险率约束。使用三个现实世界的科学工作流程应用程序以及 CloudSim 模拟框架进行的大量实验证明了我们算法的有效性和实用性。
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.