Exploiting hardware heterogeneity within the same instance type of Amazon EC2
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
On scheduling dag s for volatile computing platforms: Area-maximizing schedules
Journal of Parallel and Distributed Computing
More for your money: exploiting performance heterogeneity in public clouds
Proceedings of the Third ACM Symposium on Cloud Computing
Whare-map: heterogeneity in "homogeneous" warehouse-scale computers
Proceedings of the 40th Annual International Symposium on Computer Architecture
Efficient autonomic cloud computing using online discrete event simulation
Journal of Parallel and Distributed Computing
A survey on techniques for improving the energy efficiency of large-scale distributed systems
ACM Computing Surveys (CSUR)
The prediction of network efficiency in the smart grid
Electronic Commerce Research
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Cloud computing has emerged as a highly cost-effective computation paradigm for IT enterprise applications, scientific computing, and personal data management. Because cloud services are provided by machines of various capabilities, performance, power, and thermal characteristics, it is challenging for providers to understand their cost effectiveness when deploying their systems. This article analyzes a parallelizable task in a heterogeneous cloud infrastructure with mathematical models to evaluate the energy and performance trade-off. As the authors show, to achieve the optimal performance per utility, the slowest node's response time should be no more than three times that of the fastest node. The theoretical analysis presented can be used to guide allocation, deployment, and upgrades of computing nodes for optimizing utility effectiveness in cloud computing services.