A Map-Reduce Based Framework for Heterogeneous Processing Element Cluster Environments
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Efficient Disk I/O Scheduling with QoS Guarantee for Xen-based Hosting Platforms
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Maestro: Replica-Aware Map Scheduling for MapReduce
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
A framework for analyzing monetary cost of database systems in the cloud
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Black box scheduling for resource intensive virtual machine workloads with interference models
Future Generation Computer Systems
Flubber: Two-level disk scheduling in virtualized environment
Future Generation Computer Systems
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Cloud computing enables users to perform their computation tasks in the public virtualized cloud using a pay-as-you-go style. Current pay-as-you-go pricing schemes typically charge on the incurred virtual machine hours. Our case studies demonstrate significant variations in the user costs, indicating significant unfairness among different users from the micro-economic perspective. Further studies reveal the reason for such variations is interference among concurrent virtual machines. The amount of interference cost depends on various factors, including workload characteristics, the number of concurrent VMs, and scheduling in the cloud. In this paper, we adopt the concept of pricing fairness from micro economics, and quantitatively analyze the impact of interference on the pricing fairness. To solve the unfairness caused by interference, we propose a pay-as-you-consume pricing scheme, which charges users according to their effective resource consumption excluding interference. The key idea behind the pay-as-you-consume pricing scheme is a machine learning based prediction model of the relative cost of interference. Our preliminary results with Xen demonstrate the accuracy of the prediction model, and the fairness of the pay-as-you-consume pricing scheme.