nach oben

Wireless Personal Communications

Erschienen in:

05.07.2016

A Hybrid Strategy for Resource Allocation and Load Balancing in Virtualized Data Centers Using BSO Algorithms

verfasst von: V. Jeyakrishnan, P. Sengottuvelan

Erschienen in: Wireless Personal Communications | Ausgabe 4/2017

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In data centers are provided solution to the consumer and to the organization by means of store and process their data. When scheduling operation carrying more requirements for resources than it can hold, in this situation load balancing strategy distributes workloads across multiple servers to optimize the performances. However, resource allocation and load balancing is an inspiring problem for the cloud service providers to consumers in terms of Quality of Services. The proposed hybrid bacterial swarm optimization algorithm, achieve global seek over the entire search space through PSO while local search is achieved by BFO algorithm. This paper proposed a novel idea, how to tackle the scheduling problem by using hybrid load balancing techniques. The experimental results demonstrate that the projected algorithms overtake the existing SA, PSO, Dynamic ADS algorithms considerably by minimizing the operational cost, make-span and maximize the utilization of the resource.

Vorheriger Artikel Circumvention of Nascent and Potential Wi-Fi Phishing Threat Using Association Rule Mining

Nächster Artikel Dynamic Estimators for Islanding Detection in IEEE 9-Bus Wireless System

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

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

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

Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.CrossRef

Meng, X., Pappas, V., & Zhang, L. (2010). Improving the scalability of data center networks with traffic-aware virtual machine placement. In Proceedings of IEEE INFOCOM (pp. 1–9).

Singh, S., & Chana, I. (2015). QRSF: QoS-aware resource scheduling framework in cloud computing. Journal of Supercomputing, 71, 241–292.CrossRef

Sahu, R. K., Panda, S., & Padhan, S. (2015). A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system. Applied Soft Computing, 29, 310–327.CrossRef

Braun, T. D., Siegel, H. J., & Beck, N. (2001). A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 61, 810–837.CrossRef

Cao, J., Spooner, D. P., Jarvis, S. A., & Nudd, G. R. (2005). Grid load balancing using intelligent agents. Future Generation Computer Systems, 21, 135–149.CrossRef

Zhu, X., He, C., Ge, R., & Lu, P. (2011). Boosting adaptivity of fault-tolerant scheduling for real-time tasks with service requirements on clusters. Journal of System and Software, 84(10), 1708–1716.CrossRef

Menasce, D. A., & Tripathi, S. K. (1995). Static and dynamic processor scheduling disciplines in heterogeneous parallel architectures. Journal of Parallel and Distributed Computing, 28, 1–18.CrossRefMATH

Sugavanam, P., Siegel, H. J., & Maciejewski, A. A. (2007). Robust static allocation of resources for independent tasks under makespan and dollar cost constraints. Journal of Parallel and Distributed Computing, 67, 400–416.CrossRefMATH

10.

Chang, R. S., Chang, J. S., & Lin, P. S. (2009). An ant algorithm for balanced job scheduling in grids. Future Generation Computer Systems, 25, 20–27.CrossRef

11.

Xu, G., Pang, J., & Fu, X. (2013). A load balancing model based on cloud partitioning for the public cloud. Tsinghua Science and Technology, 18(1), 34–39.CrossRefMATH

12.

Dhinesh Babu, L. D., & Krishna, P. V. (2013). Honey bee behaviour inspired load balancing of tasks in cloud computing environments. Applied Soft Computing, 13, 2292–2303.CrossRef

13.

Mondal, B., Dasgupta, K., & Dutta, P. (2012). Load balancing in cloud computing using stochastic hill climbing-a soft computing approach. Procedia Technology, 4, 783–789.CrossRef

14.

Cao, J., Li, K., & Stojmenovic, I. (2014). Optimal power allocation and load distribution for multiple heterogeneous multicore server processor across clouds and data centers. IEEE Transactions on Computers, 63(1), 45–58.MathSciNetCrossRef

15.

Zhao, H., Liu, X., & Li, X. (2014). Towards efficient and fair resource trading in a community based cloud computing. Journal of Parallel and Distributed Computing, 74, 3087–3097.CrossRef

16.

Al-Omari, R., Somani, A. K., & Manimaran, G. (2005). An adaptive scheme for fault-tolerant scheduling of soft real-time tasks in multiprocessor systems. Journal of Parallel and Distributed Computing, 65, 595–608.CrossRefMATH

17.

Manimaran, G., & Murthy, C. S. R. (1997). A new scheduling approach supporting different fault-tolerant techniques for real-time multiprocessor systems. Microprocessors and Microsystems, 21, 163–173.CrossRef

18.

Ali, E. S., & Abd-Elazim, S. M. (2011). Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Electrical Power and Energy Systems, 33, 633–638.CrossRef

19.

Mohanty, B., Panda, S., & Hotab, P. K. (2013). Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems. Applied Soft Computing, 13, 4718–4730.CrossRef

20.

Ali, E. S., & Abd-Elazim, S. M. (2013). BFOA based design of PID controller for two area Load Frequency Control with non-linearities. Electrical Power and Energy Systems, 51, 224–231.CrossRef

21.

Zoltan, A. M. (2015). Allocation of virtual machines in cloud data centers: A survey of problem models and optimization algorithms. ACM Computing Surveys, 48(1), 11–34.

22.

Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277, 680–693.CrossRef

23.

Balasangameshwara, J., & Raju, N. (2012). Fault tolerant scheduling and load balancing for computational grids. Journal of Network and Computer Application, 35, 412–422.CrossRef

24.

Feng, Y., Li, D., Wu, H., Zhang, Y. (2000). A dynamic load balancing algorithm based on distributed database system. In Proceedings of the fourth international conference on high performance computing in the Asia-Pacific region (pp. 949–952).

25.

Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29, 1012–1023.CrossRef

26.

Korani, W. (2008). Bacterial foraging oriented by particle swarm optimization strategy for PID tuning. In GECCO (pp. 1823–1826). ACM 978-1-60558-131.

27.

Rajni, I. C. (2013). Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Generation Computer Systems, 29, 751–762.CrossRef

28.

Abd-Elazim, S. M., & Ali, E. S. (2013). A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Electrical Power and Energy Systems, 46, 334–341.CrossRef

29.

Maguluri, S.T., Srikant, R. (2012). Stochastic models of load balancing and scheduling in cloud computing clusters. In INFOCOM-IEEE conference proceeding (pp. 702–710).

30.

Li, J., et al. (2010). Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In Science Direct, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

31.

Anguluri, R., Abraham, A., & Snasel, V. (2011). A hybrid bacterial foraging: PSO algorithm based tuning of optimal FOPI speed controller. Acta Montanistica, 16(1), 55–65.

32.

Zhan, Z. H., Liu, X. F., Gong, Y. J., & Zhang, J. (2015). Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys, 15(63), 1–33.CrossRef

33.

Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann Publishers.

Titel: A Hybrid Strategy for Resource Allocation and Load Balancing in Virtualized Data Centers Using BSO Algorithms
verfasst von: V. Jeyakrishnan
P. Sengottuvelan
Publikationsdatum: 05.07.2016
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 4/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-016-3481-8

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 "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2017

Resource Allocation Scheme for Multihop Cellular Networks Using Directional Transmission

User Path Prediction Based Key Caching and Authentication Mechanism for Broadband Wireless Networks

Inpatient Critical Stage Monitoring in Smart Hospitals by Contextual Fuzzy based QoS Routing for WBMS Network Nurse Call System

Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints

Evolutionary Clustering Algorithm Using Criterion-Knowledge-Ranking for Multi-objective Optimization

Soft Computing Based Fine Tuning and Clock Synchronization in Wireless Sensor Networks