Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Probability Distributions Involving Gaussian Random Variables: A Handbook for Engineers, Scientists and Mathematicians
On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Empirical prediction models for adaptive resource provisioning in the cloud
Future Generation Computer Systems
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)
Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
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As an economic and scalable solution of providing interactive and adaptive media content across different devices, cloud-based media processing has recently attracted lots of attention from both academic and industry. Within a media cloud, a large number of media processing tasks will be dynamically spawn to run in parallel and then share resource with each other in the cloud. In such a dynamic and shared environment, how to appropriately provision cloud resource to different tasks with regards to both system efficiency and quality of service remains a challenging issue. Existing resource allocation algorithms consider generic task and only focus on maximizing system efficiency, but failed to meet the QoS requirement of media tasks. In this paper, we investigate the relationship between the properities of media tasks and their resource comsumption and accordingly propose a dynamic resource allocation solution. Our solution leverages machine learning technique to predict resource requirement and survival functions to prevent QoS degradation.