Paper Info
Title
End-To-End Informed Vm Selection In Compute Clouds
Abstract
The selection of resources, particularly VMs, in current public IaaS clouds is usually done in a blind fashion, as cloud users do not have much information about resource consumption by co-tenant third-party tasks. In particular, communication patterns can play a significant part in cloud application performance and responsiveness, specially in the case of novel latency-sensitive applications, increasingly common in today's clouds. Thus, herein we propose an end-to-end approach to the VM allocation problem using policies based uniquely on round-trip time measurements between VMs. Those become part of a user-level 'Recommender Service' that receives VM allocation requests with certain network-related demands and matches them to a suitable subset of VMs available to the user within the cloud. We propose and implement end-to-end algorithms for VM selection that cover desirable profiles of communications between VMs in distributed applications in a cloud setting, such as profiles with prevailing pair-wise, hub-and-spokes, or clustered communication patterns between constituent VMs. We quantify the expected benefits from deploying our Recommender Service by comparing our informed VM allocation approaches to conventional, random allocation methods, based on real measurements of latencies between Amazon EC2 instances. We also show that our approach is completely independent from cloud architecture details, is adaptable to different types of applications and workloads, and is lightweight and transparent to cloud providers.
Year
Venue
Field
2015
2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION WORKSHOP (ICCW)
Resource consumption,Resource management,Virtual machine,Computer science,End-to-end principle,Computer network,Real-time computing,Cluster analysis,Cloud architecture,Technical report,Distributed computing,Cloud computing
DocType
ISSN
Citations 
Conference
2164-7038
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Mário Meireles Teixeira1163.48
Azer Bestavros23791764.82