nach oben

The Journal of Supercomputing

Erschienen in:

28.01.2021

CMODLB: an efficient load balancing approach in cloud computing environment

verfasst von: Sarita Negi, Man Mohan Singh Rauthan, Kunwar Singh Vaisla, Neelam Panwar

Erschienen in: The Journal of Supercomputing | Ausgabe 8/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A hybrid of supervised (artificial neural network), unsupervised (clustering) machine learning, and soft computing (interval type 2 fuzzy logic system)-based load balancing algorithm, i.e., clustering-based multiple objective dynamic load balancing technique (CMODLB), is introduced to balance the cloud load in the present work. Initially, our previously introduced artificial neural network-based dynamic load balancing (ANN-LB) technique is implemented to cluster the virtual machines (VMs) into underloaded and overloaded VMs using Bayesian optimization-based enhanced K-means (BOEK-means) algorithm. In the second stage, the user tasks are scheduled for underloading VMs to improve load balance and resource utilization. Scheduling of tasks is supported by multi-objective-based technique of order preference by similarity to ideal solution with particle swarm optimization (TOPSIS-PSO) algorithm using different cloud criteria. To realize load balancing among PMs, the VM manager makes decisions for VM migration. VM migration decision is done based on the suitable conditions, if a PM is overloaded, and if another PM is minimum loaded. The former condition balances load, while the latter condition minimizes energy consumption in PMs. VM migration is achieved through interval type 2 fuzzy logic system (IT2FS) whose decisions are based on multiple significant parameters. Experimental results show that the CMODLB method takes 31.067% and 71.6% less completion time than TaPRA and BSO, respectively. It has maintained 65.54% and 68.26% less MakeSpan than MaxMin and R.R algorithms, respectively. The proposed method has achieved around 75% of resource utilization, which is highest compared to DHCI and CESCC. The use of novel and innovative hybridization of machine learning, multi-objective, and soft computing methods in the proposed algorithm offers optimum scheduling and migration processes to balance PMs and VMs.

Vorheriger Artikel Toward graph classification on structure property using adaptive motif based on graph convolutional network

Nächster Artikel A selfish herd optimization algorithm based on the simplex method for clustering analysis

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Sadiku NOM, Musa M, S, D Momoh, O, (2014) Cloud computing: Opportunities and challenges. IEEE Potentials 3(1):34–36CrossRef

Diaz M, Martin C, Rubio B (2016) State-of-the-art challenges, and open issues in an integration of internet of things and cloud computing. J Netw Comput Appl 67:99–117. https://doi.org/10.1016/j.jnca.2016.01.010CrossRef

Milani AS, Navimipour NJ (2016) Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J Netw Comput Appl 71:86–98. https://doi.org/10.1016/j.jnca.2016.06.003CrossRef

Zhi-H Zhan, Xiao-F Liu, Yue-Jiao Gong, Zhang J (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surv 47(4):1–33. https://doi.org/10.1145/2788397CrossRef

Hua H, Guangquan X, Shanchen P, Zenghua Z (2016) AMTS: adaptive multi-objective task scheduling strategy in cloud computing. China Commun 13(4):162–171. https://doi.org/10.1109/CC.2016.7464133CrossRef

Yuan H, Bi J, Tan W, Zhou M, Li BH, Li J (2017) TTSA: an effective scheduling approach for delay bounded tasks in hybrid clouds. IEEE Trans Cybern 47(11):3658–3668. https://doi.org/10.1109/TCYB.2016.2574766CrossRef

Zhong Z, Chen K, Zhai X, Zhou S (2016) Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci Technol 21(6):660–667. https://doi.org/10.1109/TST.2016.7787008CrossRefMATH

Xu X, Cao L, Wang X (2016) Resource pre-allocation algorithms for low-energy task scheduling of cloud computing. J Syst Eng Electron 27(2):457–469. https://doi.org/10.1109/JSEE.2016.00047CrossRef

Sharma SCM, Rath AK (2017) Multi-Rumen Anti-Grazing approach of load balancing in cloud network. Int J Info Technol 9(2):129–138. https://doi.org/10.1007/s41870-017-0022-yCrossRef

10.

Singha A, Junejab D, Malhotra M (2015) Autonomous agent based load balancing algorithm in cloud computing. Procedia Comput Sci 45:832–841. https://doi.org/10.1016/j.procs.2015.03.168CrossRef

11.

Gutierrez-Garcia JO, Ramirez-Nafarrate A (2015) Agent-based load balancing in cloud data centers. Cluster Comput 18(3):1041–1062. https://doi.org/10.1007/s10586-015-0460-xCrossRef

12.

Chun-C L, Hui-H C, Der-J D (2014) Dynamic multiservice load balancing in cloud-based multimedia system. IEEE Syst J 8(1):225–234. https://doi.org/10.1109/JSYST.2013.2256320CrossRef

13.

Chitra DD, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci World J 2016:1–14. https://doi.org/10.1155/2016/3896065CrossRef

14.

Tao D, Lin Z, Wang B (2017) Load feedback-based resource scheduling and dynamic migration-based data locality for virtual Hadoop clusters in OpenStack-based clouds. Tsinghua Sci Technol 22(2):149–159.https://doi.org/10.23919/TST.2017.7889637

15.

Xie R, Wen Y, Jia X, Xie H (2015) Supporting seamless virtual machine migration via named data networking in cloud data center. IEEE Trans Parallel Distrib Syst 26(12):3485–3497. https://doi.org/10.1109/TPDS.2014.2377119CrossRef

16.

Mosleh Mohammed AS, Radhamani G, Hazber Mohamed AG, Hasan SH (2016) Adaptive cost-based task scheduling in cloud environment. Sci Program 2016:1–9. https://doi.org/10.1155/2016/8239239CrossRef

17.

Liu Y, Li C, Li L (2016) Distributed two-level cloud-based multimedia task scheduling. Automat Contr Comput Sci 50(3):41–150. https://doi.org/10.3103/S0146411616030044CrossRef

18.

Shi L, Zhang Z, Robertazzi T (2017) Energy-aware scheduling of embarrassingly parallel jobs and resource allocation in cloud. IEEE Trans Parallel Distrib Syst 28(6):1607–1620. https://doi.org/10.1109/TPDS.2016.2625254CrossRef

19.

Li Y, Chen M, Dai W, Qiu M (2017) Energy optimization with dynamic task scheduling mobile cloud computing. IEEE Syst J 11(1):96–105. https://doi.org/10.1109/JSYST.2015.2442994CrossRef

20.

Keng-M C, Pang-W T, Chun-W T, Chu-S Y (2015) A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput Appl 26(6):297–1309. https://doi.org/10.1007/s00521-014-1804-9CrossRef

21.

Eswaran S, Rajakannu M (2017) Multiservice load balancing with hybrid particle swarm optimization in cloud-based multimedia storage system with QoS provision. Mobile Netw Appl 22(4):760–770. https://doi.org/10.1007/s11036-017-0840-yCrossRef

22.

Dhinesh Babu LD, Venkata Krishna P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13:2292–2303. https://doi.org/10.1016/j.asoc.2013.01.025CrossRef

23.

Negi S, Panwar N, Vaisla K S, Rauthan MMS (2020) Artificial Neural Network Based Load Balancing in Cloud Environment. Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, 94. /https://doi.org/10.1007/978-981-15-0694-9_20.

24.

Jeyakrishnan V, Sengottuvelan P (2017) A Hybrid Strategy for Resource Allocation and Load Balancing in Virtualized Data Centers Using BSO Algorithms. Wireless Pers Commun 94(4):2363–2375. https://doi.org/10.1007/s11277-016-3481-8CrossRef

25.

Polepally V K, Chatrapati K S (2017) Dragonfly optimization and constraint measure-based load balancing in cloud computing. Cluster Computing. Springer.https://doi.org/10.1007/s10586-017-1056-4

26.

Zhao J, Yang K, Wei X, Ding Y, Hu L, Xu G (2016) A Heuristic Clustering-based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment. IEEE Trans Parallel Distrib Syst 27(2):305–316. https://doi.org/10.1109/TPDS.2015.2402655CrossRef

27.

Tsakalozos K, Verroios V, Roussopoulos M, Delis A (2017) Live VM Migration under Time-Constraints in Share-Nothing IaaS-Clouds. IEEE Trans Parallel Distrib Syst 28(8):2285–2298. https://doi.org/10.1109/TPDS.2017.2658572CrossRef

28.

Kansal Nidhi J, Chana I (2016) Energy-aware Virtual Machine Migration for cloud computing—a firefly optimization approach. J Grid Comput 14(2):327–345. https://doi.org/10.1007/s10723-016-9364-0CrossRef

29.

Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, TrungHieu N, Tenhunen H (2016) Energy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model. IEEE Transactions on Cloud Computing (99).https://doi.org/10.1109/TCC.2016.2617374

30.

Patel G, Mehta R, Bhoi U (2015) Enhanced Load Balanced Min-Min algorithm for Static Meta-task Scheduling in Cloud Computing. Procedia Computer Science (Elsevier). https://doi.org/10.1016/j.procs.2015.07.385CrossRef

31.

Lakraa AV, Yadav DK (2015) Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization. Procedia Computer Science, Elsevier 48:107–113. https://doi.org/10.1016/j.procs.2015.04.158CrossRef

32.

Zhang P, Zhou Meng C (2017) Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy. IEEE Trans Autom Sci Eng 99:1–12. https://doi.org/10.1109/TASE.2017.2693688CrossRef

33.

Zuo X, Zhang G, Tan W (2014) Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud. IEEE Trans Autom Sci Eng 11(2):564–573. https://doi.org/10.1109/TASE.2013.2272758CrossRef

34.

Awada AI, El-Hefnawyb NA, Abdelkader HM (2015) Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments. Procedia Comput Sci Elsevier 65:920–929. https://doi.org/10.1016/j.procs.2015.09.064CrossRef

35.

Panwar N, Negi S, Rauthan MMS, Vaisla KS (2019) TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment. Clust Comput. https://doi.org/10.1007/s10586-019-02915-3CrossRef

36.

Pooranian Z, Shojafar M, Abawajy Jemal H, Abraham A (2013) An efficient meta-heuristic algorithm for grid computing. J Comb Optim, Springer 30(3):413–434. https://doi.org/10.1007/s10878-013-9644-6MathSciNetCrossRefMATH

37.

Brochu E, Cora Vlad M, Freitas Nando D (2013) A Tutorial on Bayesian Optimization of Expensive Cost Functions with Application to Active User Modeling and Hierarchical Reinforcement Learning. https://arxiv.org/abs/1012.2599

38.

Nyikosa F M, Osborne M A, Roberts S J (2018) Bayesian Optimization for Dynamic Problems. https://arxiv.org/abs/1803.03432

39.

Wagner C (2013) Juzzy – A Java based Toolkit for Type-2 Fuzzy Logic. IEEE. Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ). https://doi.org/10.1109/T2FZZ.2013.6613298

40.

Mendel JM, John RI, Liu F (2006) Interval Type-2 Fuzzy Logic Systems Made Simple. IEEE Trans Fuzzy Syst 14(6):808–821CrossRef

41.

Kasper Fredenslund (2018) March 25. https://kasperfred.com/series/introduction-to-neural-networks/computational-complexity-of-neural-networks

42.

Baptista R, Poloczek M (2018) Bayesian optimization of combinatorial structures. In: Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR. 80. https://arxiv.org/abs/1806.08838

43.

Ren Q, Balazinski M, Baron L (2011) Type-2 TSK fuzzy logic system and its type-1 counterpart. Int J Comput Appl 20(6):0975–8887. https://doi.org/10.5120/2440-3292CrossRef

44.

Buyya R, Ranjan R, Calheiros R N (2019) Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities. High Performance Computing & Simulation HPCS’09. 1–11. https://doi.org/10.1109/HPCSIM.2009.5192685

45.

Shojafar M, Javanmardi S, Saeid A, Nicola C (2015) FUGE: a joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Clust Comp 18(2):829–844CrossRef

46.

Chen Y (2019) Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms. Compl Hindwai 2019:1–12. https://doi.org/10.1155/2019/7325053CrossRefMATH

47.

Mendel JM (2013) On KM algorithms for solving type-2 fuzzy set problems. IEEE Trans Fuzzy Syst 21(3):426–446CrossRef

48.

Liang Q, Mendel J (2000) Interval Type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8:535–550CrossRef

49.

Singh H, Tyagi S, Kumar P (2020) Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing. Int J Commun Syst 33(5):e4467. https://doi.org/10.1002/dac.4467CrossRef

50.

Prassanna J, Venkataraman N (2019) Threshold based multi-objective memetic optimized round robin scheduling for resource efficient load balancing in cloud. Mobile Netw Appl 24:1214–1225. https://doi.org/10.1007/s11036-019-01259-xCrossRef

51.

Neelima P, Rama Mohan Reddy A (2020) An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Cluster Comput 23:2891–2899. https://doi.org/10.1007/s10586-020-03054-wCrossRef

Titel: CMODLB: an efficient load balancing approach in cloud computing environment
verfasst von: Sarita Negi
Man Mohan Singh Rauthan
Kunwar Singh Vaisla
Neelam Panwar
Publikationsdatum: 28.01.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 8/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03601-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 8/2021

HybriDroid: an empirical analysis on effective malware detection model developed using ensemble methods

Multistep temperature prediction for proactive thermal management on chip multiprocessors

A method of activity-based software maintenance cost estimation for package software

A secure blockchain-based group key agreement protocol for IoT

Gait analysis in patients with neurological disorders using ankle-worn accelerometers

Parallel source separation system for heart and lung sounds