Sierra: practical power-proportionality for data center storage
Proceedings of the sixth conference on Computer systems
Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud
Proceedings of the 20th international symposium on High performance distributed computing
Utilization-based pricing for power management and profit optimization in data centers
Journal of Parallel and Distributed Computing
Mobile online gaming via resource sharing
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
Projecting disk usage based on historical trends in a cloud environment
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Overlay network resource allocation using a decentralized market-based approach
Future Generation Computer Systems
Overlay network resource allocation using a decentralized market-based approach
Future Generation Computer Systems
Storage provisioning and allocation in a large cloud environment
Proceedings of the 2012 workshop on Management of big data systems
CloudBay: Enabling an Online Resource Market Place for Open Clouds
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
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We present a practical, market-based solution to the resource provisioning problem in a set of heterogeneous resource clusters. We focus on provisioning rather than immediate scheduling decisions to allow users to change long-term job specifications based on market feedback. Users enter bids to purchase quotas, or bundles of resources for long-term use. These requests are mapped into a simulated clock auction which determines uniform, fair resource prices that balance supply and demand. The reserve prices for resources sold by the operator in this auction are set based on current utilization, thus guiding the users as they set their bids towards under-utilized resources. By running these auctions at regular time intervals, prices fluctuate like those in a real-world economy and provide motivation for users to engineer systems that can best take advantage of available resources. These ideas were implemented in an experimental resource market at Google. Our preliminary results demonstrate an efficient transition of users from more congested resource pools to less congested resources. The disparate engineering costs for users to reconfigure their jobs to run on less expensive resource pools was evidenced by the large price premiums some users were willing to pay for more expensive resources. The final resource allocations illustrated how this framework can lead to significant, beneficial changes in user behavior, reducing the excessive shortages and surpluses of more traditional allocation methods.