Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers
Future Generation Computer Systems
Auto-scaling to minimize cost and meet application deadlines in cloud workflows
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
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
Developing a cost-effective virtual cluster on the cloud
GECON'11 Proceedings of the 8th international conference on Economics of Grids, Clouds, Systems, and Services
A coordinator for scaling elastic applications across multiple clouds
Future Generation Computer Systems
Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
Future Generation Computer Systems
Addressing response time of cloud-based mobile applications
Proceedings of the first international workshop on Mobile cloud computing & networking
A solution for optimizing recovery time in cloud computing
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
A QoS and profit aware cloud confederation model for IaaS service providers
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments
The Journal of Supercomputing
Scheduling highly available applications on cloud environments
Future Generation Computer Systems
Accurate Resource Prediction for Hybrid IaaS Clouds Using Workload-Tailored Elastic Compute Units
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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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With the recent emergence of public cloud offerings, surge computing –outsourcing tasks from an internal data center to a cloud provider in times of heavy load– has become more accessible to a wide range of consumers. Deciding which workloads to outsource to what cloud provider in such a setting, however, is far from trivial. The objective of this decision is to maximize the utilization of the internal data center and to minimize the cost of running the outsourced tasks in the cloud, while fulfilling the applications’ quality of service constraints. We examine this optimization problem in a multi-provider hybrid cloud setting with deadline-constrained and preemptible but non-provider-migratable workloads that are characterized by memory, CPU and data transmission requirements. Linear programming is a general technique to tackle such an optimization problem. At present, it is however unclear whether this technique is suitable for the problem at hand and what the performance implications of its use are. We therefore analyze and propose a binary integer program formulation of the scheduling problem and evaluate the computational costs of this technique with respect to the problem’s key parameters. We found out that this approach results in a tractable solution for scheduling applications in the public cloud, but that the same method becomes much less feasible in a hybrid cloud setting due to very high solve time variances.