Skip to main content
SpringerLink
Log in

Design and analysis of distributed load management: Mobile agent based probabilistic model and fuzzy integrated model

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In large-scale distributed systems, performing load monitoring and load balancing is a challenging task in terms of load management. In order to enhance the overall system performance, we have developed and implemented two different models for large-scale distributed load management. The mobile agent-based system is based on a probabilistic normed estimation model. This model uses mobile agents for collecting the instantaneous status of currently available node resources autonomously. The mobile agent is goal oriented and consumes less network and system resources, which is ideal for load monitoring for large-scale distributed systems. Moreover, we have proposed an integrated load balancing and monitoring model for distributed computing systems employing type-1 fuzzy logic. Furthermore, we have proposed a smooth and composite fuzzy membership function in order to model fine-grained load information in a system. In this paper, a detailed software architectural design for mobile agent based load monitoring system as well as the fuzzy-based load balancing approach are presented. The experimental evaluation is presented to compare the behavior and performance of the mobile agent-based probabilistic model and fuzzy integrated model under different load conditions. A detail comparative analysis is presented for the mobile agent-based probabilistic model and fuzzy integrated model to show the performance and efficiency of each model. In this paper, we have computed cross-correlation to find the relation between our proposed models (FIM and MABMS).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41
Fig. 42
Fig. 43
Fig. 44
Fig. 45
Fig. 46
Fig. 47
Fig. 48
Fig. 49
Fig. 50
Fig. 51
Fig. 52
Fig. 53
Fig. 54
Fig. 55
Fig. 56
Fig. 57
Fig. 58
Fig. 59
Fig. 60

Similar content being viewed by others

References

  1. Xu F, Liu F, Liu L, Jin H, Li B (2014) iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans Comput 63:3012–3025

    Article  MathSciNet  MATH  Google Scholar 

  2. Rajani S., and Garg N. A clustered approach for load balancing in distributed systems, international journal of Mobile Computing & Application, volume: 2, SSRG-IJMCA, 2015, ISSN: 2393-9141

  3. Wörn H, Längle T, Albert M, Kazi A, Brighenti A, Seijo SR, Senior C, Bobi MAS, Collado JV (2004) Diamond: distributed multi-agent architecture for monitoring and diagnosis. Prod Plan Control 15(2):189–200

    Article  Google Scholar 

  4. Tomarchio, O., Vita, L. and Puliafito, A., Active monitoring in grid environments using mobile agent technology, In Active Middleware Services, Springer, Boston, MA, 2000, pp. 57–66

  5. Haverkamp DS, Gauch S (1998) Intelligent information agents: review and challenges for distributed information sources. J Assoc Inf Sci Technol 49(4):304–311

    Google Scholar 

  6. Alakeel AM (2016) Application of fuzzy logic in load balancing of homogenous distributed systems. Int J Comput Sci Secur IJCSS 10(3):95–101

    Google Scholar 

  7. Alipour MM, Derakhshi MRF (2016) Two level fuzzy approach for dynamic load balancing in the cloud computing. J Electron Syst 6(1):17–31

    Google Scholar 

  8. Ahn, H.C., Youn, H.Y., Jeon, K.Y. and Lee, K.S. Dynamic load balancing for large-scale distributed system with intelligent fuzzy controller, In Information Reuse and Integration, IEEE International Conference on, IEEE, 2007, pp. 576–581

  9. Das S (2013) Mobile agents in distributed computing: network exploration. Bulletin of EATCS 1:109

    MATH  Google Scholar 

  10. Papavassiliou S, Puliafito A, Tomarchio O, Ye J (2002) Mobile agent-based approach for efficient network management and resource allocation: framework and applications. IEEE J Sel Areas Commun 20(4):858–872

    Article  Google Scholar 

  11. Manvi SS, Venkataram P A method of network monitoring by mobile agents. Computing 2(3):4–5

  12. Adacal M, Bener AB (2006) Mobile web services: a new agent-based framework. IEEE Internet Comput 10(3):58–65

    Article  Google Scholar 

  13. Du TC, Li EY, Chang AP (2003) Mobile agents in distributed network management. Commun ACM 46(7):127–132

    Article  Google Scholar 

  14. Aridor, Yariv and Danny B. L. Agent design patterns: elements of agent application design, In Proceedings of the second international conference on Autonomous agents, ACM, 1998, pp. 108–115

  15. Mostafa, Salama A., Mohd S. A., Muthukkaruppan A., Azhana A., and Saraswathy S. G. A dynamically adjustable autonomic agent framework, In Advances in Information Systems and Technologies, Springer, 2013, pp 631–642

  16. Ku H, Luderer GW, Subbiah B (1997) An intelligent mobile agent framework for distributed network management. In Global telecommunications conference, GLOBECOM'97. IEEE 1:160–164

    Google Scholar 

  17. Corradi A, Cremonini M, Montanari R, Stefanelli C (1999) Mobile agents integrity for electronic commerce applications. Inf Syst 24(6, Elsevier):519–533

    Article  Google Scholar 

  18. Ahn J (2010) Fault-tolerant Mobile agent-based monitoring mechanism for highly dynamic distributed networks. IJCSI Int J Comput Sci Issues 7(3):1–7

    Google Scholar 

  19. Park HJ, Jyung KJ, Kim SS (2004) Mobile agent-based load monitoring system for the safety web server environment. In: In international conference on computational science. Springer, pp 274–280

  20. Wang X, Wang H, Wang Y (2010) A unified monitoring framework for distributed environment. Intell Inf Manag 2(07):398–405

    Google Scholar 

  21. Massie ML, Chun BN, Culler DE (2004) The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput 30(7):817–840

    Article  Google Scholar 

  22. Vidhate SL, Kharat MU (2014) Resource aware monitoring in distributed system using Tabu search algorithm. Int J Comput Appl 96(23):22–25

    Google Scholar 

  23. Tomarchio, O. and Vita, L. On the use of mobile code technology for monitoring grid system, In Cluster Computing and the Grid, Proceedings First IEEE/ACM International Symposium on, IEEE, 2001, pp. 450–455

  24. Iosup, A., Ţãpuş, N. and Vialle, S. A monitoring architecture for control grids, In European Grid Conference, Springer, 2005, pp. 922–931

  25. Mace, J., Roelke, R. and Fonseca, R. Pivot tracing: dynamic causal monitoring for distributed systems, In Proceedings of the 25th symposium on operating systems principles, ACM, 2015, pp. 378–393

  26. Gunter, D., Tierney, B., Jackson, K., Lee, J. and Stoufer, M. Dynamic monitoring of high-performance distributed applications, In High performance distributed computing, 11th IEEE international symposium, IEEE, 2002, pp. 163–170

  27. Hoke E, Sun J, Faloutsos CI (2006) Intelligent system monitoring on large clusters. In: Proceedings of the 32nd international conference on very large data bases, VLDB endowment, ACM, pp 1239–1242

    Google Scholar 

  28. Tie Z (2013) A Mobile agent-based system for server resource monitoring. Cybernetics and Information Technologies 13(4):104–117

    Article  MathSciNet  Google Scholar 

  29. Dobre, C., Voicu, R., Muraru, A., and Legrand, I.C. A distributed agent based system to control and coordinate large scale data transfers, 2011, arXiv preprint arXiv:1106.5171, 2011

  30. Seenuvasan P, Kannan A, Varalakshmi P (2017) Agent-based resource management in a cloud environment. Appl Math 11(3):777–788

    Google Scholar 

  31. Helmy T, Al-Jamimi H, Ahmed B, Loqman H (2012) Fuzzy logic-based scheme for load balancing in grid services. J Softw Eng Appl 5:149–157

    Article  Google Scholar 

  32. Floyd MW, Esfandiari B (2018) Supplemental observation acquisition for learning by observation agents. Appl Intell, Springer 48:1–17. https://doi.org/10.1007/s10489-018-1191-5

    Article  Google Scholar 

  33. Zhong W, Zhuang Y, Sun J, Gu J (2018) A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine. Appl Intell, Springer 48:1–12. https://doi.org/10.1007/s10489-018-1194-2

    Article  Google Scholar 

  34. Bagchi S (2016) Probabilistic and fuzzy process classifiers for operating systems scheduler. Fundamenta Informaticae 145(4):405–427

    Article  MathSciNet  Google Scholar 

  35. Rahmat RS, Lafuerza Guillén B (2009) Probabilistic norms and statistical convergence of random variables, surveys in mathematics and its applications, vol 4, pp 65–76

    MATH  Google Scholar 

  36. Nine MSZ, Azad MAK, Abdullah S, Rahman RM (2013) Fuzzy logic based dynamic load balancing in virtualized data centers. In: Fuzzy systems (FUZZ), 2013 IEEE international conference. IEEE, pp 1–7

  37. Velde, V. and Rama, B. An advanced algorithm for load balancing in cloud computing using fuzzy technique, In Intelligent Computing and Control Systems (ICICCS), International Conference, IEEE, 2017, pp. 1042–1047

  38. Kwon, S. and Choi, J. An agent-based adaptive monitoring system, In Pacific rim international workshop on multi-agents, Springer, Berlin, Heidelberg, 2006, pp. 672–677

  39. Brooks, C., Tierney, B. and Johnston, W. JAVA agents for distributed system management, LBNL Report, 1997

  40. Kim ST, Park HJ, Kim YC (2001) The load monitoring of web server using mobile agent, in Info-tech and Info-net, 2001, Proceedings. ICII 2001-Beijing. International Conferences on, IEEE 5:89–94

    Google Scholar 

  41. Legrand I, Newman H, Voicu R, Cirstoiu C, Grigoras C, Dobre C, Muraru A, Costan A, Dediu M, Stratan C (2009) MonALISA: an agent based, dynamic service system to monitor, control and optimize distributed systems. Comput Phys Commun 180(12):2472–2498

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Aerospace and Software Engineering (Informatics), Gyeongsang National University, Jinju, 660701, South Korea

    Moazam Ali & Susmit Bagchi

Authors
  1. Moazam Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susmit Bagchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susmit Bagchi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, M., Bagchi, S. Design and analysis of distributed load management: Mobile agent based probabilistic model and fuzzy integrated model. Appl Intell 49, 3464–3489 (2019). https://doi.org/10.1007/s10489-019-01454-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01454-z

Keywords

Search

Navigation