A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

[1]  Ching-Chi Lin,et al.  Automatic Resource Scaling Based on Application Service Requirements , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[2]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[3]  Xi Chen,et al.  An Availability-Aware Approach to Resource Placement of Dynamic Scaling in Clouds , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[4]  Parastoo Mohagheghi,et al.  Software Engineering Challenges for Migration to the Service Cloud Paradigm: Ongoing Work in the REMICS Project , 2011, 2011 IEEE World Congress on Services.

[5]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[6]  Moustafa Ghanem,et al.  Lightweight Resource Scaling for Cloud Applications , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[7]  Prasad Saripalli,et al.  Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[8]  Eddy Caron,et al.  Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[9]  Balaji Viswanathan,et al.  SmartScale: Automatic Application Scaling in Enterprise Clouds , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[10]  Zhenhuan Gong,et al.  PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.

[11]  Seyed Masoud Sadjadi,et al.  Service Clouds: Distributed Infrastructure for Adaptive Communication Services , 2007, IEEE Transactions on Network and Service Management.

[12]  Ching-Chi Lin,et al.  Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[13]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..