Cluster-oriented virtual machine low latency consolidation algorithm
Authors: 
Michal Polak, Jan FeslAuthors Info & Claims
Pages 113 - 120
Abstract
With the growing amount of data processed in the virtual environment, many researchers focus their efforts on optimizing the load distribution on data centers according to various criteria. In this article, we propose optimization at the network infrastructure load of the data center. The new heuristic algorithm, based on grouping virtual machines into clusters, was compared with heuristics based on a genetic algorithm. The performed measurements indicate that clustering-based heuristics, although data-dependent, shows promising characteristics with significantly lower computational complexity. The algorithm was tested on a rigorous number of instances, proving its general usability.
References
[1]
Mohammed Amoon. 2018. A multi criteria-based approach for virtual machines consolidation to save electrical power in Cloud Data Centers. IEEE Access 6(2018), 24110–24117. https://doi.org/10.1109/access.2018.2830183
[2]
Tao Chen, Xiaofeng Gao, and Guihai Chen. 2016. Optimized virtual machine placement with traffic-aware balancing in data center networks. Scientific Programming 2016 (2016), 1–10. https://doi.org/10.1155/2016/3101658
[3]
Moon-Hyun Kim, Jun-Yeong Lee, Syed Asif Raza Shah, Tae-Hyung Kim, and Seo-Young Noh. 2021. Min-max exclusive virtual machine placement in cloud computing for Scientific Data Environment. Journal of Cloud Computing 10, 1 (2021). https://doi.org/10.1186/s13677-020-00221-7
[4]
Kangkang Li, Huanyang Zheng, and Jie Wu. 2013. Migration-based virtual machine placement in Cloud Systems. 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet) (2013). https://doi.org/10.1109/cloudnet.2013.6710561
[5]
Weiwei Lin, Wentai Wu, and Ligang He. 2020. An on-line virtual machine consolidation strategy for dual improvement in performance and energy conservation of server clusters in Cloud Data Centers. IEEE Transactions on Services Computing(2020), 1–1. https://doi.org/10.1109/tsc.2019.2961082
[6]
Andrea Lodi, Silvano Martello, and Michele Monaci. 2002. Two-dimensional packing problems: A survey. European Journal of Operational Research 141, 2 (2002), 241–252. https://doi.org/10.1016/s0377-2217(02)00123-6
[7]
N. Madani, A. Lebbat, S. Tallal, and H. Medromi. 2014. Power-aware virtual machines consolidation architecture based on CPU load scheduling. 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) (2014). https://doi.org/10.1109/aiccsa.2014.7073221
[8]
Xiaoqiao Meng, Vasileios Pappas, and Li Zhang. 2010. Improving the scalability of data center networks with traffic-aware virtual machine placement. 2010 Proceedings IEEE INFOCOM(2010). https://doi.org/10.1109/infcom.2010.5461930
[9]
Fikru Feleke Moges and Surafel Lemma Abebe. 2019. Energy-aware VM placement algorithms for the OpenStack Neat Consolidation Framework. Journal of Cloud Computing 8, 1 (2019). https://doi.org/10.1186/s13677-019-0126-y
[10]
Tevfik Yapicioglu and Sema Oktug. 2013. A traffic-aware virtual machine placement method for Cloud Data Centers. 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (2013). https://doi.org/10.1109/ucc.2013.62
[11]
Maede Yavari, Akbar Ghaffarpour Rahbar, and Mohammad Hadi Fathi. 2019. Temperature and energy-aware consolidation algorithms in cloud computing. Journal of Cloud Computing 8, 1 (2019). https://doi.org/10.1186/s13677-019-0136-9
[12]
Qinghua Zheng, Jia Li, Bo Dong, Rui Li, Nazaraf Shah, and Feng Tian. 2015. Multi-objective optimization algorithm based on BBO for Virtual Machine Consolidation Problem. 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS) (2015). https://doi.org/10.1109/icpads.2015.59
[13]
Biyu Zhou, Jie Wu, Fa Zhang, and Zhiyong Liu. 2017. Resource optimization for survivable embedding of virtual clusters in cloud data centers. 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC) (2017). https://doi.org/10.1109/pccc.2017.8280436
Index Terms
- Cluster-oriented virtual machine low latency consolidation algorithm
Recommendations
Performance Analysis for Pareto-Optimal Green Consolidation Based on Virtual Machines Live Migration
Huge energy requirement of cloud data centers is prime concern. Dynamic Virtual Machine VM consolidation based on VM live migration to switched-off or put some of the under-loaded host Physical Machines PMs into a low power consumption mode can ...
Improving performance by network-aware virtual machine clustering and consolidation
Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the ...
Live gang migration of virtual machines
HPDC '11: Proceedings of the 20th international symposium on High performance distributed computingThis paper addresses the problem of simultaneously migrating a group of co-located and live virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as "live gang migration". ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2022
286 pages
ISBN:9781450396226
DOI:10.1145/3543712
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 September 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCTA 2022
ICCTA 2022: 2022 8th International Conference on Computer Technology Applications
May 12 - 14, 2022
Vienna, Austria
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format