Efficient hybriding auto-scaling for OpenStack platforms

Chia Ching Chen, Shao Jui Chen, Fan Yin, Wei Jen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The use of virtualization technology has gradually changed the way a datacenter works in recent years. Nowadays the end-users of a datacenter do not access physical resources directly. Instead, they access virtualized resources, such as VMs and virtual clusters, on top of a pool of physical resources. This new computing paradigm provides the datacenter administrators a more flexible, scalable, manageable, and economical way for resource provisioning/sharing as prior study indicated. When a service on a VM encounters a massive amount of workload, it can scale faster than a non-virtualized datacenter, by dynamically turning on extra virtual/physical machines to share the workload. For example, OpenStack, an open source project for building a virtualized cloud platform, provides a reactive approach for auto-scaling. The approach creates new VMs to share workload when the workload of a monitored VM exceeds a given workload threshold. The weakness of the mechanism is that, sometimes it is too late to handle unexpected workload surges and thus can decrease the quality of the services running on the VM. To this end, we purpose an auto-scaling mechanism for OpenStack It relies on a predictive auto-scaling approach that predicts the upcoming workload by historical workloads. A reactive method is also used to reduce the impact of wrong workload prediction. To prevent the case that the prediction result is not accurate enough, we have verified the performance of our approach via experiments. The results show that, when a massive workload arrives, the proposed approach outperforms other approaches, and uses the same level of resources.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015
EditorsXingang Liu, Peicheng Wang, Yufeng Wang, Mianxiong Dong, Robert C. H. Hsu, Feng Xia, Yuhui Deng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1079-1085
Number of pages7
ISBN (Electronic)9781509018932
DOIs
StatePublished - 2015
EventIEEE International Conference on Smart City, SmartCity 2015 - Chengdu, China
Duration: 19 Dec 201521 Dec 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015

Conference

ConferenceIEEE International Conference on Smart City, SmartCity 2015
Country/TerritoryChina
CityChengdu
Period19/12/1521/12/15

Keywords

  • Auto-scaling
  • Cloud computing
  • Load balancing

Fingerprint

Dive into the research topics of 'Efficient hybriding auto-scaling for OpenStack platforms'. Together they form a unique fingerprint.

Cite this