Paper Info
Title
Virtual Machine Profiling for Analyzing Resource Usage of Applications.
Abstract
From the cloud provider perspective, applications are usually black boxes hosted on Virtual Machines. Managing these black boxes without knowing anything about the features of the workload can generate inefficiencies in the performance. In fact, this information can be relevant to take deployment decisions which consist both in considering the interferences between applications with similar resources demands and predicting future peak demands avoiding performance degradation. Monitoring applications in cloud facilities and data centers is the only approach to manage and ensure the performance level of the hosted applications. This paper considers applications as black boxes and, using monitoring data analysis of the VMs on which applications are running, provides a methodology for building an application profile reflecting relevant behavioral features of a VM. This information is precious to lead deployment and adaptive decisions in data center management. The approach is validated on a real monitoring data set of an Italian data center.
Year
DOI
Venue
2018
10.1007/978-3-319-94376-32
Lecture Notes in Computer Science
Keywords
Field
DocType
VM profile,Intensiveness,Periodicity,Applications resource usage,Data center
Software deployment,Application profile,Virtual machine,Profiling (computer programming),Workload,Computer science,Black box,Data center,Cloud computing,Distributed computing
Conference
Volume
ISSN
Citations 
10969
0302-9743
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Xuesong Peng121.77
Barbara Pernici23401488.75
Monica Vitali36411.82