Virtual Infrastructure Management in Private and Hybrid Clouds
IEEE Internet Computing
Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Decision Model for Cloud Computing under SLA Constraints
MASCOTS '10 Proceedings of the 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
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CCGRID '11 Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers
Future Generation Computer Systems
Using service level agreements for optimising cloud infrastructure services
ServiceWave'10 Proceedings of the 2010 international conference on Towards a service-based internet
Modeling for Dynamic Cloud Scheduling Via Migration of Virtual Machines
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
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CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Statistical Modeling of Spot Instance Prices in Public Cloud Environments
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Virtual machine placement for predictable and time-constrained peak loads
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OPTIMIS: A holistic approach to cloud service provisioning
Future Generation Computer Systems
Pricing Cloud Compute Commodities: A Novel Financial Economic Model
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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Runtime virtual machine recontextualization for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
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Until now, most research on cloud service placement has focused on static pricing scenarios, where cloud providers offer fixed prices for their resources. However, with the recent trend of dynamic pricing of cloud resources, where the price of a compute resource can vary depending on the free capacity and load of the provider, new placement algorithms are needed. In this paper, we investigate service placement in dynamic pricing scenarios by evaluating a set of placement algorithms, tuned for dynamic pricing. The algorithms range from simple heuristics to combinatorial optimization solutions. The studied algorithms are evaluated by deploying a set of services across multiple providers. Finally, we analyse the strengths and weaknesses of the algorithms considered. The evaluation suggests that exhaustive search based approach is good at finding optimal solutions for service placement under dynamic pricing schemes, but the execution times are usually long. In contrast, greedy approaches perform surprisingly well with fast execution times and acceptable solutions, and thus can be a suitable compromise considering the tradeoffs between quality of solution and execution time.