Paper Info
Title
Towards Faster Response Time Models for Vertical Elasticity
Abstract
Resource provisioning in cloud computing is typically coarse-grained. For example, entire CPU cores may be allocated for periods of up to an hour. The Resource-as-a-Service cloud concept has been introduced to improve the efficiency of resource utilization in clouds. In this concept, resources are allocated in terms of CPU core fractions, with granularities of seconds. Such infrastructures could be created using existing technologies such as lightweight virtualization using LXC or by exploiting the Xen hyper visor's capacity for vertical elasticity. However, performance models for determining how much capacity to allocate to each application are currently lacking. To address this deficit, we evaluate two performance models for predicting mean response times: the previously proposed queue length model and the novel inverse model. The models are evaluated using 3 applications under both open and closed system models. The inverse model reacted rapidly and remained stable even with targets as low as 0.5 seconds.
Year
DOI
Venue
2014
10.1109/UCC.2014.86
UCC '14 Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing
Keywords
Field
DocType
cloud computing,resource allocation,virtual machines,cpu core fractions,lxc,xen hyper visor capacity,closed system model,inverse model,mean response time prediction,open system model,performance models,queue length model,resource provisioning,resource utilization,resource-as-a-service cloud concept,response time models,vertical elasticity,cloud
Virtualization,Resource management,Computer science,Queue,Hypervisor,Response time,Real-time computing,Provisioning,Multi-core processor,Cloud computing
Conference
ISSN
Citations 
PageRank 
2373-6860
22
0.90
References 
Authors
15
4
Name
Order
Citations
PageRank
Ewnetu Bayuh Lakew1818.83
Cristian Klein2463.82
Francisco Hernandez-Rodriguez3220.90
Erik Elmroth41675149.84