Paper Info
Title
Measuring Cloud Workload Burstiness
Abstract
Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of data enters. In this paper, we review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for Cloud systems where various workloads are co-hosted on the same platform. We discuss Sample Entropy (Samp En), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for Cloud workloads.
Year
DOI
Venue
2014
10.1109/UCC.2014.87
UCC
Field
DocType
ISSN
Resource management,Sample entropy,Workload,Computer science,Server,Quality of service,Real-time computing,Burstiness,The Internet,Cloud computing
Conference
2373-6860
Citations 
PageRank 
References 
10
0.54
21
Authors
5
Name
Order
Citations
PageRank
Ahmed Ali-Eldin144224.01
Oleg Seleznjev23211.61
Sara Sjöstedt-De Luna3100.88
Johan Tordsson4127666.49
Erik Elmroth51675149.84