Abstract
High-performance computing requires numerous virtual machines (VMs) to meet the demand for services of the end-users. Such computation generates high intra-network traffic among the services running in different VMs. Thus, to achieve a high throughput of the application, VMs are to be deployed in physical machines in a way that reduces the communication cost and delays and thereby meets the high quality of services demanded by the users. On the other hand, the service providers aim to consolidate VMs in a minimum number of active physical machines to reduce their operational costs. Finding an optimal solution to minimize the cost of either the deployment or communication, by themselves are NP-Hard problems. Moreover, an attempt to optimize one disregarding the other may give a solution that is better in terms of cost but inferior in terms of throughput (or vice versa). Hence, we formulate the VM placement problem as a multi-objective optimization problem and propose an Ant Colony Optimization-based VM consolidation algorithm to find a solution in real-time. We have also presented a performance comparison of the proposed algorithm with existing mono-objective and multi-objective algorithms.
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Karmakar Kamalesh, Das Rajib K, Khatua Sunirmal (2019a) Resource scheduling of workflow tasks in cloud environment. In: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), IEEE, pp 1–6
Karmakar Kamalesh, Das Rajib K, Khatua Sunirmal (2020) Resource scheduling for tasks of a workflow in cloud environment. In: International Conference on Distributed Computing and Internet Technology, Springer, pp 214–226
Dan Asit, Johnson Robert D, Carrato Tony (2008) Soa service reuse by design. In: Proceedings of the 2nd international workshop on Systems development in SOA environments, ACM, pp 25–28
Erl T (2016) SOA Principles of Service Design (paperback). Prentice Hall Press, United States
Meikel P, Othayoth NR (2008) Energy cost, the key challenge of today‘s data centers: a power consumption analysis of tpc-c results. Proc VLDB Endowment 1(2):1229–1240
Van Heddeghem W, Lambert S, Lannoo B, Colle D, Pickavet M, Demeester P (2014) Trends in worldwide ict electricity consumption from 2007 to 2012. Comput Commun 50:64–76
Yeo Sungkap, Hossain Mohammad M, Huang Jen-Cheng, Lee Hsien-Hsin S (2014) Atac: Ambient temperature-aware capping for power efficient datacenters. In: Proceedings of the ACM Symposium on Cloud Computing, ACM, pp 1–14
Zheng Kuangyu, Wang Xiaodong, Li Li, Wang Xiaorui (2014) Joint power optimization of data center network and servers with correlation analysis. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, IEEE, pp 2598–2606
Dayarathna M, Wen Y, Fan R (2015) Data center energy consumption modeling: A survey. IEEE Commun Surveys Tutorial 18(1):732–794
Li X, Qian Z, Sanglu L, Jie W (2013) Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math Comput Model 58(5–6):1222–1235
Ferdaus Md Hasanul, Murshed Manzur, Calheiros Rodrigo N, Buyya Rajkumar (2014) Virtual machine consolidation in cloud data centers using aco metaheuristic. In: European conference on parallel processing, Springer, pp 306–317
Tawfeek Medhat A, El-Sisi Ashraf B, Keshk Arabi E, Torkey Fawzy A (2014) Virtual machine placement based on ant colony optimization for minimizing resource wastage. In: International Conference on Advanced Machine Learning Technologies and Applications, Springer, pp 153–164
Wang Meng, Meng Xiaoqiao, Zhang Li (2011) Consolidating virtual machines with dynamic bandwidth demand in data centers. In: INFOCOM, 2011 Proceedings IEEE, IEEE, pp 71–75
Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242
Qin Y, Wang H, Zhu F, Zhai L (2018) A multi-objective ant colony system algorithm for virtual machine placement in traffic intense data centers. IEEE Access 6:58912–58923
Liu X-F, Zhan Z-H, Deng JD, Li Y, Tianlong G, Zhang J (2016) An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans Evol Comput 22(1):113–128
Meng Xiaoqiao, Pappas Vasileios, Zhang Li (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: INFOCOM, 2010 Proceedings IEEE, IEEE, pp 1–9
Alicherry Mansoor , Lakshman TV (2013) Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In: 2013 Proceedings IEEE INFOCOM, IEEE, pp 647–655
Kuo Jian-Jhih, Yang Hsiu-Hsien, Tsai Ming-Jer (2014) Optimal approximation algorithm of virtual machine placement for data latency minimization in cloud systems. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, IEEE, pp 1303–1311
Jung Gueyoung, Hiltunen Matti A, Joshi Kaustubh R, Schlichting Richard D, Pu Calton (2010) Mistral: Dynamically managing power, performance, and adaptation cost in cloud infrastructures. In: Distributed Computing Systems (ICDCS), 2010 IEEE 30th International Conference on, IEEE, pp 62–73
Kuo Chin-Fu, Yeh Ting-Hsi, Lu Yung-Feng, ChangBao-Rong (2015) Efficient allocation algorithm for virtual machines in cloud computing systems. In: Proceedings of the ASE BigData & SocialInformatics 2015, ACM, p 8
Gupta Madnesh K, Amgoth Tarachand (2016) Resource-aware algorithm for virtual machine placement in cloud environment. In: Contemporary Computing (IC3), 2016 Ninth International Conference on, IEEE, pp 1–6
Karmakar Kamalesh, Khatua Sunirmal, Das Rajib K (2017) Efficient virtual machine placement in cloud environment. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp 1004–1009
Ballani Hitesh, Costa Paolo, Karagiannis Thomas, Rowstron Ant (2011) Towards predictable datacenter networks. In: ACM SIGCOMM Computer Communication Review, vol 41, ACM, pp 242–253
Biran Ofer, Corradi Antonio, Fanelli Mario, Foschini Luca, Nus Alexander, Raz Danny, Silvera Ezra (2012) A stable network-aware vm placement for cloud systems. In: Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on, IEEE, pp 498–506
Yu Lei, Shen Haiying (2014) Bandwidth guarantee under demand uncertainty in multi-tenant clouds. In: Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on, IEEE, pp 258–267
Karmakar Kamalesh, Das Rajib K, Khatua Sunirmal (2019) Minimizing communication cost for virtual machine placement in cloud data center. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON). IEEE, pp 1553–1558
Xu Jing, Fortes Jose AB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, IEEE Computer Society, pp 179–188
Mi Haibo, Wang Huaimin, Yin Gang, Zhou Yangfan, Shi Dianxi, Yuan Lin (2010) Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: Services Computing (SCC), 2010 IEEE International Conference on, IEEE, pp 514–521
Kessaci Yacine, Melab Nouredine, Talbi El-Ghazali (2012) An energy-aware multi-start local search heuristic for scheduling vms on the opennebula cloud distribution. In: 2012 International Conference on High Performance Computing & Simulation (HPCS), IEEE, pp 112–118
Malekloo Mohammadhossein, Kara Nadjia (2014) Multi-objective aco virtual machine placement in cloud computing environments. In: 2014 IEEE Globecom Workshops (GC Wkshps), IEEE, pp 112–116
Zheng Q, Li R, Li X, Shah N, Zhang J, Tian F, Chao K-M, Li J (2016) Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Futur Gener Comput Syst 54:95–122
Duong-Ba Thuan Hong, Nguyen Thinh, Bose Bella, Tran Tuan Tho (2018) A dynamic virtual machine placement and migration scheme for data centers. IEEE Trans Services Comput
Su Shoubao, Su Yu, Shao Fei, Guo Haifeng (2015) A power-aware virtual machine mapper using firefly optimization. In: 2015 Third International Conference on Advanced Cloud and Big Data, IEEE, pp 96–103
Terra-Neves Miguel, Lynce Inês, Manquinho Vasco (2018) Virtual machine consolidation using constraint-based multi-objective optimization. J Heuristics 1–37
Ihara Diego, López-Pires Fabio, Baran Benjamin (2015) Many-objective virtual machine placement for dynamic environments. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), IEEE, pp 75–79
López-Pires F, Barán B (2017) Many-objective virtual machine placement. J Grid Comput 15(2):161–176
Ye X, Yin Y, Lan L (2017) Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment. IEEE access 5:16006–16020
Feller E, Rohr C, Margery D, Morin C (2012) Energy management in iaas clouds: a holistic approach. In: 2012 IEEE Fifth International Conference on Cloud Computing, IEEE, pp 204–212
Mathew V, Sitaraman Ramesh K, Shenoy P (2012) Energy-aware load balancing in content delivery networks. In: 2012 Proceedings IEEE INFOCOM, IEEE, pp 954–962
Lin M, Wierman A, Andrew LLH, Thereska E (2013) Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans Netw (TON) 21(5):1378–1391
Lin Minghong, Wierman Adam, Andrew Lachlan LH, Thereska Eno. Dynamic right-sizing for power-proportional data centers—extended version
Kaur Kuljeet, Garg Sahil, Aujla Gagangeet Singh, Kumar Neeraj, Zomaya Albert (2019) A multi-objective optimization scheme for job scheduling in sustainable cloud data centers. IEEE Trans Cloud Comput
Lin L, Wei DSL, Ma R, Li J, Guan H (2020) Online traffic-aware linked vm placement in cloud data centers. Sci China Inf Sci 63:1–23
Alashaikh AS, Alanazi EA (2019) Incorporating ceteris paribus preferences in multiobjective virtual machine placement. IEEE Access 7:59984–59998
Cao G (2019) Topology-aware multi-objective virtual machine dynamic consolidation for cloud datacenter. Sustain Comput: Informat Syst 21:179–188
Arregoces M, Portolani M (2003) Data center fundamentals. Cisco Press, United States
Al-Fares Mohammad, Loukissas Alexander, Vahdat Amin (2008) A scalable, commodity data center network architecture. In: ACM SIGCOMM Computer Communication Review, vol 38, ACM, pp 63–74
Vahdat Amin, Al-Fares Mohammad, Loukissas Alexander. Scalable commodity data center network architecture, July 9 2013. US Patent 8,483,096
Petrini Fabrizio, Vanneschi Marco (1997) k-ary n-trees: High performance networks for massively parallel architectures. In: Proceedings 11th International Parallel Processing Symposium, IEEE, pp 87–93
Greenberg Albert, Hamilton James R, Jain Navendu, Kandula Srikanth, Kim Changhoon, Lahiri Parantap, Maltz David A, Patel Parveen, Sengupta Sudipta (2009) Vl2: a scalable and flexible data center network. In ACM SIGCOMM computer communication review, vol 39, ACM, pp 51–62
Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, Sengupta S (2011) Vl2: a scalable and flexible data center network. Commun ACM 54(3):95–104
Guo Chuanxiong, Wu Haitao, Tan Kun, Shi Lei, Zhang Yongguang, Lu Songwu (2008) Dcell: a scalable and fault-tolerant network structure for data centers. In: ACM SIGCOMM Computer Communication Review, vol 38, ACM, pp 75–86
Guo C, Guohan L, Li D, Haitao W, Zhang X, Shi Y, Tian C, Zhang Y, Songwu L (2009) Bcube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 39(4):63–74
Garey MR, Johnson DS (1979) Computers and intractability, vol 174. freeman San Francisco, Unites States
Branke J, Branke J, Deb K, Miettinen K, Slowiński R (2008) Multiobjective optimization: Interactive and evolutionary approaches, vol 5252. Springer Science & Business Media, Berlin
Ajiro Yasuhiro, Tanaka Atsuhiro (2007) Improving packing algorithms for server consolidation. Int. CMG Conference 253:399–406
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–768
Lian Zhen, Li Xin, Qin Xiaolin (2017) Topology-aware vm placement for network optimization in cloud data centers. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), IEEE, pp 558–565
Nurmi D, Wolski R, Grzegorczyk C, Obertelli G, Soman S, Youseff L, Zagorodnov D (2008) Eucalyptus: A technical report on an elastic utility computing archietecture linking your programs to useful systems ucsb computer science technical report number 2008–10. Computer Science DepartmentUniversity of California, Santa, California
Nurmi Daniel, Wolski Rich, Grzegorczyk Chris, Obertelli Graziano, Soman Sunil, Youseff Lamia, Zagorodnov Dmitrii (2009) The eucalyptus open-source cloud-computing system. In: Cluster Computing and the Grid, 2009. CCGRID’09. 9th IEEE/ACM International Symposium on, IEEE, pp 124–131
EC Amazon (2010) Amazon elastic compute cloud (amazon ec2). Amazon Elastic Compute Cloud (Amazon EC2)
Bazarbayev Sobir, Hiltunen Matti, Joshi Kaustubh, Sanders William H, Schlichting Richard (2013) Content-based scheduling of virtual machines (vms) in the cloud. In: Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on, IEEE, pp 93–101
Benson Theophilus, Akella Aditya, Maltz David A (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, ACM, pp 267–280
Kandula Srikanth, Sengupta Sudipta, Greenberg Albert, Patel Parveen, Chaiken Ronnie (2009) The nature of data center traffic: measurements & analysis. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, ACM, pp 202–208
Benson Theophilus, Anand Ashok, Akella Aditya, Zhang Ming (2011) Microte: Fine grained traffic engineering for data centers. In: Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies, ACM, p 8
Li Yanfei, Wang Ying, Yin Bo, Guan Lu (2012) An online power metering model for cloud environment. In: 2012 IEEE 11th International Symposium on Network Computing and Applications, IEEE, pp 175–180
Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput: Practice Experience 24(13):1397–1420
Acknowledgements
This research is an outcome of the R&D work supported by the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by the Digital India Corporation, Ref. No. MLA/MUM/GA/10(37)C.
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Karmakar, K., Das, R.K. & Khatua, S. An ACO-based multi-objective optimization for cooperating VM placement in cloud data center. J Supercomput 78, 3093–3121 (2022). https://doi.org/10.1007/s11227-021-03978-z
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DOI: https://doi.org/10.1007/s11227-021-03978-z