Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes

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.