Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
SERVICES '11 Proceedings of the 2011 IEEE World Congress on Services
Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Service Clouds: Distributed Infrastructure for Adaptive Communication Services
IEEE Transactions on Network and Service Management
Lightweight Resource Scaling for Cloud Applications
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
SmartScale: Automatic Application Scaling in Enterprise Clouds
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
An Availability-Aware Approach to Resource Placement of Dynamic Scaling in Clouds
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Automatic Resource Scaling Based on Application Service Requirements
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Dynamic intelligence towards merging cloud and communication services
Information Systems Frontiers
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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.