A Dynamic Hybrid Resource Provisioning Approach for Running Large-scale Computational Applications on Cloud Spot and On-demand Instances

被引：16

作者：

Lu, Sifei ^{[1
]}

Li, Xiaorong ^{[1
]}

Wang, Long ^{[1
]}

Kasim, Henry ^{[1
]}

Palit, Henry ^{[1
]}

Hung, Terence ^{[1
]}

Legara, Erika Fille Tupas ^{[1
]}

Lee, Gary ^{[1
]}

机构：

[1] Inst High Performance Comp, Singapore, Singapore

来源：

2013 19TH IEEE INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS 2013) | 2013年

关键词：

component; Cloud computing; resource provisioning; cost analysis; fault tolerance; spot instance; spot price; reliability; workflow scheduling;

D O I：

10.1109/ICPADS.2013.117

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S-3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.

引用

页码：657 / 662

页数：6

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