ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
A Survey on Cloud Computing Elasticity
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Rebalancing in a multi-cloud environment
Proceedings of the 4th ACM workshop on Scientific cloud computing
A framework for dynamically generating predictive models of workflow execution
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
A cost-aware auto-scaling approach using the workload prediction in service clouds
Information Systems Frontiers
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Large-scale component-based enterprise applications that leverage Cloud resources expect Quality of Service(QoS) guarantees in accordance with service level agreements between the customer and service providers. In the context of Cloud computing, auto scaling mechanisms hold the promise of assuring QoS properties to the applications while simultaneously making efficient use of resources and keeping operational costs low for the service providers. Despite the perceived advantages of auto scaling, realizing the full potential of auto scaling is hard due to multiple challenges stemming from the need to precisely estimate resource usage in the face of significant variability in client workload patterns. This paper makes three contributions to overcome the general lack of effective techniques for workload forecasting and optimal resource allocation. First, it discusses the challenges involved in auto scaling in the cloud. Second, it develops a model-predictive algorithm for workload forecasting that is used for resource auto scaling. Finally, empirical results are provided that demonstrate that resources can be allocated and deal located by our algorithm in a way that satisfies both the application QoS while keeping operational costs low.