A MAPE-K Loop Based Model for Virtual Machine Consolidation in Cloud Data Centers

Subject Areas : Journal of Computer & Robotics

Negin Najafizadegan ¹ , Eslam Nazemi ² , Vahid Khajehvand ³

1 - Islamic Azad University, Qazvin Branch, Qazvin, Iran
2 - Shahid Beheshti University
3 - Islamic Azad University, Qazvin Branch, Qazvin, Iran

Received: 2021-02-20 Accepted : 2021-04-12 Published : 2020-12-01

Keywords: Learning Automata, Prediction, Autonomous, virtual machine, Cloud Environment, Service Level Agreement,

Abstract :

Today, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an autonomous model with predictive capability is needed to do virtual machine consolidation at runtime effectively. In fact, using the feedback system of autonomous systems can make this process simpler and more optimized. The goal of this research is to propose a cloud resource management model that makes the virtual machine consolidation process autonomous, and by using a prediction method compromises between service level agreement violations and energy consumption reduction. In this research, an autonomous model is presented which detects overloaded servers in the analysis phase by a prediction algorithm. Also, at the planning phase, a multi heuristic algorithm based on learning automata is proposed to find proper servers for virtual machine placement. Cloudsim version 3.0.3 was used to evaluate the proposed model. The results show that the proposed model has reduced averagely the service level agreement violations, energy and migration counts by 67.08%, 11.61% and 70.64% respectively, compared to other methods.

References:

[1] S. Basu, G. Kannayaram, S. Ramasubbareddy, C.
Venkatasubbaiah, Improved genetic algorithm
for monitoring of virtual machines in cloud
environment, in: Smart Intelligent Computing
and Applications, Springer, 2019 319-
326.https://doi.org/10.1007/978-981-13-1927-
3_34 105
[2] D. Agarwal, S. Jain, Efficient optimal algorithm
of task scheduling in cloud computing
environment, arXiv preprint arXiv:1404.2076,
(2014),
[3] A. Ponraj, Optimistic virtual machine placement
in cloud data centers using queuing approach,
Future Generation Computer Systems, 93 (2019)
338-
344.https://doi.org/10.1016/j.future.2018.10.022
[4] S.B. Shaw, A.K. Singh, Use of proactive and
reactive hotspot detection technique to reduce the
number of virtual machine migration and energy
consumption in cloud data center, Computers &
Electrical Engineering, 47 (2015) 241-254,
[5] M.C. Silva Filho, C.C. Monteiro, P.R. Inácio,
M.M. Freire, Approaches for optimizing virtual
machine placement and migration in cloud
environments: A survey, Journal of Parallel and
Distributed Computing, 111 (2018) 222-
250.https://doi.org/10.1016/j.jpdc.2017.08.010
[6] R.W. Ahmad, A. Gani, S.H.A. Hamid, M.
Shiraz, A. Yousafzai, F. Xia, A survey on virtual
machine migration and server consolidation
frameworks for cloud data centers, Journal of
network and computer applications, 52 (2015)
11-25,
[7] Z. Li, An adaptive overload threshold selection
process using Markov decision processes of
virtual machine in cloud data center, Cluster
Computing, 22 (2019) 3821-
3833.https://doi.org/10.1007/s10586-018-2408-4
[8] M. Masdari, S.S. Nabavi, V. Ahmadi, An
overview of virtual machine placement schemes
in cloud computing, Journal of Network and
Computer Applications, 66 (2016) 106-
127.https://doi.org/10.1016/j.jnca.2016.01.011
[9] H.-P. Jiang, W.-M. Chen, Self-adaptive resource
allocation for energy-aware virtual machine
placement in dynamic computing cloud, Journal
of Network and Computer Applications, 120
(2018) 119-129,
[10] Z. Luo, Z. Qian, Burstiness-aware server
consolidation via queuing theory approach in a
computing cloud, 2013 IEEE 27th International
Symposium on Parallel and Distributed
Processing, (2013) 332-341,
[11] W. Voorsluys, J. Broberg, S. Venugopal, R.
Buyya, Cost of virtual machine live migration in
clouds: A performance evaluation, IEEE
International Conference on Cloud Computing,
(2009) 254-265,
[12] L. Hadded, F.B. Charrada, S. Tata, Optimization
and approximate placement of autonomic
resources for the management of service-based
applications in the cloud, OTM Confederated
International Conferences" On the Move to
Meaningful Internet Systems", (2016) 175-
192.https://doi.org/10.1007/978-3-319-48472-
3_10
[13] P. Jamshidi, A. Ahmad, C. Pahl, Autonomic
resource provisioning for cloud-based software,
Proceedings of the 9th international symposium

on software engineering for adaptive and selfmanaging
systems, (2014) 95-104,
[14] M. Mohamed, M. Amziani, D. Belaïd, S. Tata, T.
Melliti, An autonomic approach to manage
elasticity of business processes in the cloud,
Future Generation Computer Systems, 50 (2015)
49-61,
[15] M. Mohamed, D. Belaïd, S. Tata, Extending
OCCI for autonomic management in the cloud,
Journal of Systems and Software, 122 (2016)
416-429,
[16] A. Beloglazov, R. Buyya, Optimal online
deterministic algorithms and adaptive heuristics
for energy and performance efficient dynamic
consolidation of virtual machines in cloud data
centers, Concurrency and Computation: Practice
and Experience, 24 (2012) 1397-1420.
https://doi.org/10.1002/cpe.1867
[17] Z. Xiao, W. Song, Q. Chen, Dynamic resource
allocation using virtual machines for cloud
computing environment, IEEE transactions on
parallel and distributed systems, 24 (2012) 1107-
1117,
[18] P.A. Dinda, Design, implementation, and
performance of an extensible toolkit for resource
prediction in distributed systems, IEEE
Transactions on Parallel and Distributed
Systems, 17 (2006) 160-
173.https://doi.org/10.1109/TPDS.2006.24
[19] J. Liang, K. Nahrstedt, Y. Zhou, Adaptive multiresource
prediction in distributed resource
sharing environment, IEEE International
Symposium on Cluster Computing and the Grid,
2004. CCGrid 2004., (2004) 293-
300.https://doi.org/10.1109/CCGrid.2004.13365
80
[20] E. Arianyan, H. Taheri, S. Sharifian, Novel
heuristics for consolidation of virtual machines
in cloud data centers using multi-criteria resource
management solutions, The Journal of
Supercomputing, 72 (2016) 688-
717.https://doi.org/10.1007/s11227-015-1603-9
[21] J. Subirats, J. Guitart, Assessing and forecasting
energy efficiency on Cloud computing platforms,
Future Generation Computer Systems, 45 (2015)
70-
94.https://doi.org/10.1016/j.future.2014.11.008
[22] M. Ghobaei‐Arani, A.A. Rahmanian, M. Shamsi,
A. Rasouli‐Kenari, A learning‐based approach
for virtual machine placement in cloud data
centers, International Journal of Communication
Systems, 31 (2018) e3537.
https://doi.org/10.1002/dac.3537
[23] F. Alharbi, Y.-C. Tian, M. Tang, W.-Z. Zhang,
C. Peng, M. Fei, An ant colony system for
energy-efficient dynamic virtual machine
placement in data centers, Expert Systems with
Applications, 120 (2019) 228-
238.https://doi.org/10.1016/j.eswa.2018.11.029
[24] R. Shaw, E. Howley, E. Barrett, An energy
efficient anti-correlated virtual machine
placement algorithm using resource usage
predictions, Simulation Modelling Practice and
Theory, 93 (2019) 322-
342.https://doi.org/10.1016/j.simpat.2018.09.019
[25] F. Farahnakian, A. Ashraf, T. Pahikkala, P.
Liljeberg, J. Plosila, I. Porres, H. Tenhunen,
Using ant colony system to consolidate VMs for
green cloud computing, IEEE Transactions on
Services Computing, 8 (2014) 187-
198.https://doi.org/10.1109/TSC.2014.2382555
[26] H. Hallawi, J. Mehnen, H. He, Multi-Capacity
Combinatorial Ordering GA in Application to
Cloud resources allocation and efficient virtual
machines consolidation, Future Generation
Computer Systems, 69 (2017) 1-
10.https://doi.org/10.1016/j.future.2016.10.025
[27] M.H. Ferdaus, M. Murshed, R.N. Calheiros, R.
Buyya, Virtual machine consolidation in cloud
data centers using ACO metaheuristic, European
conference on parallel processing, (2014) 306-
317.https://doi.org/10.1007/978-3-319-09873-
9_26
[28] F. Teng, L. Yu, T. Li, D. Deng, F. Magoulès,
Energy efficiency of VM consolidation in IaaS
clouds, The Journal of Supercomputing, 73
(2017) 782-809.https://doi.org/10.1007/s11227-
016-1797-5
[29] A. Beloglazov, J. Abawajy, R. Buyya, Energyaware
resource allocation heuristics for efficient
management of data centers for cloud
computing, Future generation computer systems,
28 (2012) 755-
768.https://doi.org/10.1016/j.future.2011.04.017
[30] A. Horri, M.S. Mozafari, G. Dastghaibyfard,
Novel resource allocation algorithms to
performance and energy efficiency in cloud
computing, The Journal of Supercomputing, 69
(2014) 1445-
1461.https://doi.org/10.1007/s11227-014-1224-8
[31] E. Arianyan, H. Taheri, S. Sharifian, Novel
energy and SLA efficient resource management
heuristics for consolidation of virtual machines
in cloud data centers, Computers & Electrical
Engineering, 47 (2015) 222-
240.https://doi.org/10.1016/j.compeleceng.2015.
05.006
[32] S. Singh, I. Chana, M. Singh, R. Buyya,
SOCCER: self-optimization of energy-efficient
cloud resources, Cluster Computing, 19 (2016)
1787-1800.https://doi.org/10.1007/s10586-016-
0623-4
[33] S. Singh, I. Chana, R. Buyya, STAR: SLA-aware
autonomic management of cloud resources, IEEE
Transactions on Cloud Computing,(2017).https://doi.org/10.1109/TCC.2017.264878
8
[34] S. Singh, I. Chana, EARTH: Energy-aware
autonomic resource scheduling in cloud
computing, Journal of Intelligent & Fuzzy
Systems, 30 (2016) 1581-1600.10.3233/IFS-
151866
[35] S.S. Gill, I. Chana, M. Singh, R. Buyya,
CHOPPER: an intelligent QoS-aware autonomic
resource management approach for cloud
computing, Cluster Computing, 21 (2018) 1203-
1241.https://doi.org/10.1007/s10586-017-1040-z
[36] M. Ghobaei-Arani, S. Jabbehdari, M.A.
Pourmina, An autonomic resource provisioning
approach for service-based cloud applications: A
hybrid approach, Future Generation Computer
Systems, 78 (2018) 191-
210.https://doi.org/10.1016/j.future.2017.02.022
[37] E. Outin, J.-E. Dartois, O. Barais, J.-L. Pazat,
Enhancing cloud energy models for optimizing
datacenters efficiency, 2015 International
Conference on Cloud and Autonomic
Computing, (2015) 93-
100.https://doi.org/10.1109/ICCAC.2015.10
[38] M. Maurer, I. Breskovic, V.C. Emeakaroha, I.
Brandic, Revealing the MAPE loop for the
autonomic management of cloud infrastructures,
2011 IEEE symposium on computers and
communications (ISCC), (2011) 147-
152.https://doi.org/10.1109/ISCC.2011.5984008
[39] J.O. Kephart, D.M. Chess, The vision of
autonomic computing, Computer, 36 (2003) 41-
50.https://doi.org/10.1109/MC.2003.1160055
[40] B. Jacob, R. Lanyon-Hogg, D.K. Nadgir, A.F.
Yassin, A practical guide to the IBM autonomic
computing toolkit, IBM Redbooks, 4 (2004),
[41] S. Younesszadeh, M.R. Meybodi, A link
prediction method based on learning automata in
social networks, Journal of Computer &
Robotics, 11 (2018) 43-55,
[42] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.
De Rose, R. Buyya, CloudSim: a toolkit for
modeling and simulation of cloud computing
environments and evaluation of resource
provisioning algorithms, Software: Practice and
experience, 41 (2011) 23-50.
https://doi.org/10.1002/spe.995
[43] K. Park, V.S. Pai, CoMon: a mostly-scalable
monitoring system for PlanetLab, ACM SIGOPS
Operating Systems Review, 40 (2006) 65-
74.https://doi.org/10.1145/1113361.1113374