NDLI: Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Roy, N. Dubey, A. Gokhale, A.
Copyright Year	2011
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, 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.
Starting Page	500
Ending Page	507
File Size	407947
Page Count	8
File Format	PDF
ISBN	9781457708367
ISSN	21596190
DOI	10.1109/CLOUD.2011.42
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Performance models Autoscaling Quality of service Predictive models Prediction algorithms Throughput Cost function Virtual machining Time factors Resource management Workload forecasting
Content Type	Text
Resource Type	Article

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