Paper Info
Title
Predictive Data Grouping and Placement for Cloud-Based Elastic Server Infrastructures
Abstract
Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload.
Year
DOI
Venue
2011
10.1109/CCGrid.2011.49
Cluster, Cloud and Grid Computing
Keywords
Field
DocType
data locality,predictive data,predictive data grouping,dynamic workload,dynamic resource,cloud-based elastic server infrastructures,data spike,adequate data,elastic web infrastructure,high locality,elastic large scale server,data placement,forecasting,data handling,web servers,internet,economies of scale,predictive models,elastic,autoregressive model,prediction model,prediction,indexing terms,cloud,load balance,music,file servers,prediction algorithms,cloud computing
File server,Workload,Load balancing (computing),Computer science,Server,Provisioning,Group method of data handling,Distributed computing,Cloud computing,Web server
Conference
ISBN
Citations 
PageRank 
978-0-7695-4395-6
16
0.80
References 
Authors
17
4
Name
Order
Citations
PageRank
Juan M. Tirado1614.54
Daniel Higuero2483.97
Florin Isaila323424.01
Jesus Carretero423929.04