Virtual machine placement for predictable and time-constrained peak loads
GECON'11 Proceedings of the 8th international conference on Economics of Grids, Clouds, Systems, and Services
Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
Future Generation Computer Systems
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
CSB-UCC: cloud services brokerage for ubiquitous cloud computing
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
Stretch optimization for virtual screening on multi-user pilot-agent platforms on grid/cloud
Proceedings of the Fourth Symposium on Information and Communication Technology
Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments
The Journal of Supercomputing
Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Cloud brokerage mechanisms are fundamental to reduce the complexity of using multiple cloud infrastructures to achieve optimal placement of virtual machines and avoid the potential vendor lock-in problems. However, current approaches are restricted to static scenarios, where changes in characteristics such as pricing schemes, virtual machine types, and service performance throughout the service life-cycle are ignored. In this paper, we investigate dynamic cloud scheduling use cases where these parameters are continuously changed, and propose a linear integer programming model for dynamic cloud scheduling. Our model can be applied in various scenarios through selections of corresponding objectives and constraints, and offers the flexibility to express different levels of migration overhead when restructuring an existing infrastructure. Finally, our approach is evaluated using commercial clouds parameters in selected simulations for the studied scenarios. Experimental results demonstrate that, with proper parametrizations, our approach is feasible.