Abstract-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, autoscaling 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 autoscaling, realizing the full potential of autoscaling 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 autoscaling in the cloud. Second, it develops a model-predictive algorithm for workload forecasting that is used for resource autoscaling. Finally, empirical results are provided that demonstrate that resources can be allocated and deallocated by our algorithm in a way that satisfies both the application QoS while keeping operational costs low.
Roy develops a model-predictive algorithm (ARIMA model) REF for workload forecasting which then is used for resource auto-scaling.
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
2011 IEEE 4th International Conference on Cloud Computing
2011 IEEE 4th International Conference on Cloud Computing
Computer Science