Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
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DOI：
10.1016/j.future.2012.12.012
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发表时间：
2013-06
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影响因子：
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通讯作者：
R. V. D. Bossche;K. Vanmechelen;J. Broeckhove
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文献类型：
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作者：
R. V. D. Bossche;K. Vanmechelen;J. Broeckhove
云计算在工业和研究中都得到了广泛的接受，公共云产品现在经常与私有基础设施结合使用。技术方面，如网络延迟、带宽限制、数据保密性和安全性的影响，以及经济方面，如沉没成本和价格不确定性，是采用这种混合云模式的关键驱动因素。混合云的使用需要确定哪些工作负载要外包，以及要外包给哪个云提供商。这些决策应将在一个或多个公共云提供商上运行总工作负载的分区的成本降至最低，同时考虑截止日期限制和数据要求等应用程序要求。要考虑的各种成本因素、定价模式和云提供商产品，进一步要求在混合云中采用自动化调度方法。在这项工作中，我们通过提出一套算法来解决这个问题，以经济高效地在公有云提供商和私有基础设施上调度受最后期限限制的任务袋应用程序。我们的算法同时考虑了计算和数据传输成本以及网络带宽限制。我们从成本节约、截止日期和计算效率三个方面评估了它们在现实环境中的性能，并调查了运行时估计中的错误对这些性能度量的影响。
Cloud computing has found broad acceptance in both industry and research, with public cloud offerings now often used in conjunction with privately owned infrastructure. Technical aspects such as the impact of network latency, bandwidth constraints, data confidentiality and security, as well as economic aspects such as sunk costs and price uncertainty are key drivers towards the adoption of such a hybrid cloud model. The use of hybrid clouds introduces the need to determine which workloads are to be outsourced, and to what cloud provider. These decisions should minimize the cost of running a partition of the total workload on one or multiple public cloud providers while taking into account the application requirements such as deadline constraints and data requirements. The variety of cost factors, pricing models and cloud provider offerings to consider, further calls for an automated scheduling approach in hybrid clouds. In this work, we tackle this problem by proposing a set of algorithms to cost-efficiently schedule the deadline-constrained bag-of-tasks applications on both public cloud providers and private infrastructure. Our algorithms take into account both computational and data transfer costs as well as network bandwidth constraints. We evaluate their performance in a realistic setting with respect to cost savings, deadlines met and computational efficiency, and investigate the impact of errors in runtime estimates on these performance metrics.