Skip to main content
SpringerLink
Log in

Prevalence of Multi-Agent System Consensus in Cloud Computing

  • Chapter
  • First Online:
Multi Agent Systems

Part of the book series: Springer Tracts in Human-Centered Computing ((STHCC))

Abstract

Cloud computing that follows service-oriented architecture is useful for intelligent agent or multi-agent system (MAS) communication. Their use in representation and construction, parallel, and published applications is identified here and shows similarities, contrasts, and potential combinations between cloud computing and multi-agent structures. Long execution complex structure with clever applications works with MAS to showcase cloud computing. The assembling of interfaces within MAS that requires reliable scattering systems and cloud computing systems that require programs with clever, enthusiastic, versatile, and independent behavior can be current systems and applications. The engineering of a system consisting of MAS that primarily focuses on the materials of cost transactions between cloud users and providers is planned to mitigate the disadvantages of both cloud clients and cloud providers and exploit the full potential of cloud computing. As it turns out, as innovation develops and solves increasingly complex applications, the need for an integrated framework of multiple operators communicating in peer-to-peer mode is becoming clear. Central to the design and operation of such MAS is the focus of a problem and research question that has long been tested by all communities. Arrange it like a cloud environment.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nourah J, Iyad K, Aiiad A, Rashid M (2020) Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments, sensors (MDPI), vol 20, p 5796. https://doi.org/10.3390/s20205796

  2. Merizig A, Kazar O, López-Sánchez M (2019) A multi-agent system approach for service deployment in the cloud. Int J Commun Netw Distrib Syst 23:69. https://doi.org/10.1504/IJCNDS.2019.100642

    Article  Google Scholar 

  3. Koley S, Ghosh S (2014) Cloud computing with CDroid OS based on Fujitsu server for mobile technology. Skit Res J 4(2): 1–6. ISSN 2278-2508

    Google Scholar 

  4. Abbas HA, Shaheen SI, Amin MH (2015) Organization of multi-agent systems: an overview. Int J Intell Inform Syst 4(3):46–57. https://doi.org/10.11648/j.ijiis.20150403.11

    Article  Google Scholar 

  5. Pintea CM, Tripon AC, Avram A et al (2018) Multi-agents features on Android platforms. Complex Adapt Syst Model 6:10. https://doi.org/10.1186/s40294-018-0061-7

    Article  Google Scholar 

  6. Grzonka D (2015) The analysis of openstack cloud computing platform: features and performance. J Telecommun Inform Technol 3:52–57

    Google Scholar 

  7. Kumar R, Gupta N, Charu S, Jain K, Jangir SK (2014) Open source solution for cloud computing platform using openstack. Int J Comput Sci Mob Comput 3(5):89–98

    Google Scholar 

  8. Jak´obik A (2016) Big data security. Springer International Publishing, Cham, pp 241–261. https://doi.org/10.1007/978-3-319-44881-7_12. 28

  9. Cloud Controls Matrix ver. 3.0.1, Cloud security alliance. https://cloudsecurityalliance.org/group/cloud-controls-matrix/. Last Accessed April, 2021

  10. U. S. Department of Commerce (2013) NIST Cloud Computing Standards Roadmap, NIST Cloud Computing Standards-Roadmap Working Group, SP 500-291, ver. 2, Tech. rep

    Google Scholar 

  11. D. Petcu (2014), A taxonomy for sla-based monitoring of cloud security. In: Computer software and applications conference (COMPSAC), IEEE 38th annual, IEEE, pp 640–641

    Google Scholar 

  12. Yongdnog H, Jing W, Zhuofeng Z, Yanbo H (2013) A scalable and integrated cloud monitoring framework based on distributed storage. In: Web information system and application conference (WISA), 10th IEEE, pp 318–323

    Google Scholar 

  13. Trihinas D, Pallis G, Dikaiakos M (2018) Monitoring elastically adaptive multi-cloud services. In: IEEE transactions on cloud computing PP (99), vol 6, Issue 3, pp 800–814. https://doi.org/10.1109/TCC.2015.2511760

  14. Nguyen TAB, Siebenhaar M, Hans R, Steinmetz R (2014) Role-based templates for cloud monitoring. In: IEEE/ACM 7th international conference on utility and cloud computing (UCC), pp 242–250. https://doi.org/10.1109/UCC.2014.33

  15. de Carvalho MB, Esteves RP, da Cunha Rodrigues G, Granville LZ, Tarouco LMR (2013) A cloud monitoring framework for self configured monitoring slices based on multiple tools. In: Proceedings of the 9th international conference on network and service management, pp 180–184. https://doi.org/10.1109/CNSM.2013.6727833

  16. Wettinger J, Andrikopoulos V, Leymann F, Strauch S (2015) Middleware-oriented deployment automation for cloud applications. IEEE transactions on cloud computing, pp (99). https://doi.org/10.1109/TCC.2016.2535325

  17. Meng S, Iyengar AK, Rouvellou IM, Liu L, Lee K, Palanisamy B, Tang Y (2012) In: IEEE 5th international conference on reliable state monitoring in cloud data centres, cloud computing (CLOUD), pp 951–958. https://doi.org/10.1109/CLOUD.2012.10

  18. Ferry N, Rossini A, Chauvel F, Morin B, Solberg A (2013) Towards model-driven provisioning, deployment, monitoring, and adaptation of multi-cloud systems. IEEE Sixth Int Conf Cloud Comput 2013:887–894. https://doi.org/10.1109/CLOUD.2013.133.29

    Article  Google Scholar 

  19. Lopez-Rodriguez I, Hernandez-Tejera M (2011) Software agents as cloud computing services. In: Demazeau Y, Pěchoucěk M, Corchado JM, Pérez JB (eds) Advances on practical applications of agents and multiagent systems, advances in intelligent and soft computing, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19875-5_35

  20. Pireva K, Kefalas P, Dranidis D, Hatziapostolou T, Cowling A (2014) Cloud e-Learning: a new challenge for multi-agent systems. In: Jezic G, Kusek M, Lovrek I, Howlett R, Jain L (eds) Agent and multi-agent systems: technologies and applications, advances in intelligent systems and computing, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-319-07650-8_28

  21. Núñez A, Andrés C, Merayo MG (2012) MAScloud: a framework based on multi-agent systems for optimizing cost in cloud computing. In: Nguyen NT, Hoang K, Jȩdrzejowicz P (eds) Computational collective intelligence, technologies and applications, ICCCI 2012. Lecture notes in computer science, vol 7653, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_45

  22. Kumar D, Ashwin R (2012) Multi-agent based cloud services, international conference on Egovernance & cloud computing services. Int J Comput Appl EGOV(1):7–10

    Google Scholar 

  23. Chen J, Han X, Jiang G (2014) A negotiation model based on multi-agent system under cloud computing. In: The ninth international multi-conference on computing in the global information technology, ICCGI 2014

    Google Scholar 

  24. https://access.redhat.com/documentation/en-us/red_hat_openstack_platform/16.1/pdf/service_telemetry_framework_1.1/Red_Hat_OpenStack_Platform-16.1-Service_Telemetry_Framework_1.1-en-US.pdf. Last Accessed April, 2021

  25. Lee K, Murray D, Hughes D, Joosen W (2010) Extending sensor networks into the cloud using amazon web services. In: IEEE international conference on Networked embedded systems for enterprise applications (NESEA), pp 1–7

    Google Scholar 

  26. The Ultralight project: the network as an integrated and managed resource for data-intensive science. Comput Sci Eng 7(6):38–47, Nov-Dec 2005. https://doi.org/10.1109/MCSE.2005.127

  27. Pllana S, Benkner S, Mehofer E, Natvig L, Xhafa F (2009) Towards an intelligent environment for programming multi-core computing systems. Springer, Berlin, pp 141–151. https://doi.org/10.1007/978-3-642-00955-6_19

  28. Talia D (2011) Cloud computing and software agents: towards cloud intelligent services. In: Proceedings of the 12th workshop on objects and agents, Rende (CS), Italy

    Google Scholar 

  29. Bhargava R, Srivastva AK, Srivastava V (2015) A framework of multi agent system in cloud computing. Int J Sci Eng Technol Res (IJSETR) 4(6)

    Google Scholar 

  30. Bousmah M, Labouidya O, El Kamoun N (2015) Design of a cloud learning system based on multi-agents approach. Int J Adv Comput Sci Appl 6(3)

    Google Scholar 

  31. Hu J, Wang Z, Chen D, Alsaadi FE (2016) Estimation, filtering and fusion for networked systems with network induced phenomena: new progress and prospects. Inform Fusion 31:65–75

    Article  Google Scholar 

  32. Xia YQ, Gao YL, Yan LP, Fu MY (2015) Recent progress in networked control systems-a survey. Int J Autom Comput 12(4):343–367

    Article  Google Scholar 

  33. Hu J, Wang Z, Liu S, Gao H (2016) A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements. Automatica 64:155–162

    Google Scholar 

  34. Savino HJ, Souza FO, Pimenta LCA (2018) Consensus on intervals of communication delay. Int J Autom Comput 15(1):13–24

    Google Scholar 

  35. Sun YG, Wang L (2009) Consensus of multi-agent systems in directed networks with non uniform time-varying delay. IEEE Trans Autom Control 54(7):1607–1613

    Article  Google Scholar 

  36. Killworth PD, Russell Bernard H (1979) “The reversal small-world experiment” Social networks, 1(1978/79), @Elsevier Sequoia S.A. Lausanne - Printed in the Netherlands 1:159–192

    Google Scholar 

  37. Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O (1995) Novel type of phase transition in a system of self-driven particles. Phys Rev Lett 75(6):1226–1229. https://doi.org/10.1103/PhysRevLett.75.1226,PMID10060237

    Article  MathSciNet  Google Scholar 

  38. Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Control 51(3):401–420

    Article  MathSciNet  Google Scholar 

  39. Zhao X, Lei Z, Zhang G, Zhang Y, Xing C (2020) Blockchain and distributed system. In: Wang G, Lin X, endler J, Song W, Xu Z, Liu G (eds) Web information systems and applications, WISA 2020. Lecture notes in computer science, vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_56

  40. Kar S, Moura JMF (2009) Distributed consensus algorithms in sensor networks: quantized data and random link failures. arXiv:0712.1609v3, pp 1–54

  41. Batres R, Braunschweig B (2002) Chapter 6.1—software agents, computer aided chemical engineering, vol 11. Elsevier, pp 455–483

    Google Scholar 

  42. De S, Sahoo SR, Wahi P (2018) Trajectory tracking control with heterogeneous input delay in multi-agent system. J Intell Robot Syst 92:521–544. https://doi.org/10.1007/s10846-017-0715-2

    Article  Google Scholar 

  43. Xie D, Shi L, Jiang F (2018) Group tracking control of second-order multi-agent systems with fixed and Markovian switching topologies. Neuro Comput 281:37–46. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2017.11.040

  44. Ajwad SA, Moulay E, Defoort M, Ménard T, Coirault P (2021) Leader-following consensus of second-order multi-agent systems with switching topology and partial aperiodic sampled data. IEEE Control Syst Lett 5(5):1567–1572. https://doi.org/10.1109/LCSYS.2020.3041566

    Article  MathSciNet  MATH  Google Scholar 

  45. Matušů RS, Cao C, Chengyu (2018) Nonlinear uncertainties cancelling in multi-agent systems enabled by cooperative adaptation. J Control Sci Eng 2018:1687–5249.https://doi.org/10.1155/2018/3927108

  46. Cai H, Huang J (2017) Leader-following attitude consensus of multiple rigid body systems by an adaptive distributed observer approach. IFAC-PapersOnLine, 50(1):15446–15451. ISSN 2405-8963.https://doi.org/10.1016/j.ifacol.2017.08.1878

  47. Rehák, Lynnyk B, Volodymyr (2021) Leader-following synchronization of a multi-agent system with heterogeneous delays. Front Inform Technol Electron Eng 22:97–106.https://doi.org/10.1631/FITEE.2000207

  48. Shahnazi R (2020) Cooperative neuro adaptive control of leader following uncertain multi-agent systems with unknown hysteresis and dead-zone. J Syst Sci Complex 33:312–332. https://doi.org/10.1007/s11424-020-8198-9

    Article  MathSciNet  MATH  Google Scholar 

  49. Rahimi N, Binazadeh T (2019) Distributed robust consensus control for nonlinear leader–follower multi- agent systems based on adaptive observer-based sliding mode. J Vib Control 25(1):109–121. https://doi.org/10.1177/1077546318772239

    Article  MathSciNet  Google Scholar 

  50. Ma T, Li K, Zhang Z, Cui B (2021) Impulsive consensus of one-sided Lipschitz nonlinear multi-agent systems with Semi-Markov switching topologies. Nonlinear Anal Hybrid Syst 40:101020. ISSN: 1751-570X.https://doi.org/10.1016/j.nahs.2021.101020

  51. Song J, Li K, Hua C (2019) Output feedback consensus control of high-order nonlinear multi-agent systems with full state constraints, Chinese control conference (CCC), Guangzhou, China, pp 6212–6217. https://doi.org/10.23919/ChiCC.2019.8865422

  52. Almeida R, Girejko E, Hristova S, Malinowska A (2019) Leader-following consensus for fractional multi-agent systems. Adv Difference Equ. https://doi.org/10.1186/s13662-019-2235-9

    Article  MathSciNet  MATH  Google Scholar 

  53. Anuradha M, Ganesan V, Oliver S et al (2020) Hybrid firefly with differential evolution algorithm for multi agent system using clustering based personalization. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02120-w

    Article  Google Scholar 

  54. Abdulghafor R, Turaev S (2017) Consensus of fractional nonlinear dynamics stochastic operators for multi-agent systems. Inform Fusion 44:1. https://doi.org/10.1016/j.inffus.2017.11.003

    Article  Google Scholar 

  55. Wang X, Su H, Wang H, Chen G (2016) An overview of coordinated control for multi-agent systems subject to input saturation. Perspect Sci 7:133–139. ISSN: 2213-0209. https://doi.org/10.1016/j.pisc.2015.11.022

  56. Wang Q, Wang J (2018) Fully distributed fault-tolerant consensus protocols for Lipschitz nonlinear multi-agent systems, vol 6, issue 2018, pp 17313–17325

    Google Scholar 

  57. Cai Y, Zhang H, Zhang J, Wang W (2021) Fixed-time leader-following/containment consensus for a class of nonlinear multi-agent systems. Inform Sci 555:58–84. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2020.12.064

  58. Jia Q, Tang WKs (2018) Consensus of multi-agents with event-based nonlinear coupling over time-varying digraphs. In: IEEE transactions on circuits and systems II: express briefs, pp 1–1. https://doi.org/10.1109/TCSII.2018.2790582

  59. Liu, Jie JH (2018) Leader-following consensus of linear discrete-time multi-agent systems subject to jointly connected switching networks. Sci China Inform Sci 61:1869–1919.https://doi.org/10.1007/s11432-018-9453-x

  60. Chen TD, Fatemeh Bevrani H (2009) Multi-agent systems in control engineering: a survey. J Control Sci Eng 2009:1687–5249.https://doi.org/10.1155/2009/531080

  61. Chen Y, Lu J (2013) Consensus of discrete-time multi-agent systems with transmission nonlinearity. Automatica 49:1768–1775. https://doi.org/10.1016/j.automatica.2013.02.021

    Article  MathSciNet  MATH  Google Scholar 

  62. Miao G, Ma Q (2015) Group consensus of the first-order multi-agent systems with nonlinear input constraints. Neurocomputing 161:113. https://doi.org/10.1016/j.neucom.2015.02.058

    Article  Google Scholar 

  63. Shi L, Xie D (2020) Leader-following consensus of second-order multi-agent systems with time-varying delays and arbitrary weights. Trans Inst Meas Control 42(16):3156–3167. https://doi.org/10.1177/0142331220942715

    Article  MathSciNet  Google Scholar 

  64. Park JH, Wang F, Yang Y (2018) On leaderless and leader-following consensus for heterogeneous nonlinear multiagent systems via discontinuous distributed control protocol. Mathematical problems in engineering, Hindawi, vol 2018.https://doi.org/10.1155/2018/2917954

  65. Xu X, Chen S, Gao L (2014) Distributed leader-following finite-time consensus control for linear multiagent systems under switching topology. Sci World J 248041.https://doi.org/10.1155/2014/248041

  66. Cai N, Diao C, Khan M (2017) A novel clustering approach based on group quasi-consensus of unstable dynamic linear high-order multi-agent systems complexityhttps://doi.org/10.1155/2017/4978613

  67. Qin J, Gao H, Zheng WX (2011) Second-order consensus for multi-agent systems with switching topology and communication delay. Syst Control Lett 60:390–397. https://doi.org/10.1016/j.sysconle.2011.03.004

    Article  MathSciNet  MATH  Google Scholar 

  68. Zhao Y, Wen G, Duan Z, Xu X (2011) A new observer-type consensus protocol for linear multi- agent dynamical systems. In: Proceedings of the 30th Chinese control conference, Yantai, China, pp 5975–5980

    Google Scholar 

  69. Li X, Li C, Yang Y, Mo L (2020) Consensus for heterogeneous multi-agent systems with nonconvex input constraints and nonuniform time delays. J Franklin Inst 357(6):3622–3635. ISSN: 0016-0032.https://doi.org/10.1016/j.jfranklin.2019.12.035

  70. Ni W, Cheng D (2010) Leader-following consensus of multi-agent systems under fixed and switching topologies. Syst Control Lett 59(3–4):209–217. ISSN: 0167-6911.https://doi.org/10.1016/j.sysconle.2010.01.006

  71. Miao G, Ma Q, Liu Q (2016) Consensus problems for multi-agent systems with nonlinear algorithms. Neural Comput Applic 27:1327–1336. https://doi.org/10.1007/s00521-015-1936-6

    Article  Google Scholar 

  72. Hu H, Yu W, Wen G, Xuan Q, Cao J (2016) Reverse group consensus of multi-agent systems in the cooperation-competition network. IEEE Trans Circuits Syst I Regul Pap 63(11):2036–2047. https://doi.org/10.1109/TCSI.2016.2591264

    Article  Google Scholar 

  73. Shen Y et al (2018) Distributed cluster control for multi-microgrids using pinning-based group consensus of multi-agent system. In: 5th IEEE international conference on cloud computing and intelligence systems (CCIS), Nanjing, China, pp 1077–1080. https://doi.org/10.1109/CCIS.2018.8691332

  74. Zhang X, Zhu Q, Liu X (2016) Consensus of second order multi-agent systems with exogenous disturbance generated by unknown exosystems. Entropy 18(12):423. https://doi.org/10.3390/e18120423

    Article  Google Scholar 

  75. Mondal S, Rong S, Xie L (2017) Heterogeneous consensus of higher-order multi-agent systems with mismatched uncertainties using sliding mode control. Int J Robust Nonlinear Control 27(13):2303–2320

    Article  MathSciNet  Google Scholar 

  76. Nada D, Bousbia-Salah M, Bettayeb M (2018) Multi-sensor Data Fusion for wheelchair position estimation with unscented Kalman Filter. Int J Autom Comput 15:207–217. https://doi.org/10.1007/s11633-017-1065-z

    Article  Google Scholar 

  77. Xu X, Liu L, Feng G (2016) Consensus of single integrator multi-agent systems with directed topology and communication delays. Control Theory Technol 14:21–27. https://doi.org/10.1007/s11768-016-5122-x

    Article  MathSciNet  MATH  Google Scholar 

  78. Song J (2018) Low-pass filter design and sampling theorem verification. AIP conference proceedings, vol 1971, Issue 1. https://aip.scitation.org/doi/abs/10.1063/1.5041159

  79. Xiao F, Wang L (2008) Asynchronous consensus in continuous-time multi-agent systems with switching topology and time-varying delays. IEEE Trans Autom Control 53(8):1804–1816. https://doi.org/10.1109/TAC.2008.929381

    Article  MathSciNet  MATH  Google Scholar 

  80. Garcia E, Antsaklis PJ (2016) Chapter three—adaptive stabilization of uncertain systems with model-based control and event-triggered feedback updates. Control of complex systems, Butterworth-Heinemann, pp 67–92. ISBN: 9780128052464.https://doi.org/10.1016/B978-0-12-805246-4.00003-3

  81. Mingyu F, Yujie X (2017) Finite-time tracking control for a class of MIMO nonlinear systems with unknown asymmetric saturations. Math Prob Eng 2017, Article ID 9452171, 1–10.https://doi.org/10.1155/2017/9452171

  82. Liu X, Lam J, Yu W, Chen G (2016) Finite-time consensus of multiagent systems with a switching protocol. IEEE Trans Neural Netw Learn Syst 27(4):853–862. https://doi.org/10.1109/TNNLS.2015.2425933

    Article  MathSciNet  Google Scholar 

  83. Donea J, Giuliani S, Halleux JP (1982) An arbitrary Lagrangian-Eulerian finite element method for transient dynamic fluid-structure interactions. Comput Methods Appl Mech Eng 33(1–3):689–723. ISSN: 0045-7825. https://www.sciencedirect.com/science/article/pii/0045782582901281

  84. Ling J, Wang R (2017) Finite-time consensus of heterogeneous multi-agent system with external disturbances. Chinese Automation Congress (CAC), Jinan, China, pp 1478–1483. https://doi.org/10.1109/CAC.2017.8243000

  85. Zheng Y, Chen W, Wang L (2011) Finite-time consensus for stochastic multi-agent systems. Int J Control 84:1644–1652. https://doi.org/10.1080/00207179.2011.622792

    Article  MathSciNet  MATH  Google Scholar 

  86. Abdulghafor RA, Almohamedh S, Turaev H, Almutairi S, Badr (2019) Symmetry, nonlinear consensus protocol modified from doubly stochastic quadratic operators in networks of dynamic agents, vol 11, issue 12, pp 2073–8994. https://doi.org/10.3390/sym11121519, https://www.mdpi.com/2073-8994/11/12/1519

  87. Lokhande SC, Xu H (2017) Optimal self-triggered control and network co-design for networked multi-agent system via adaptive dynamic programming, IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu, HI, USA 2017:1–8. https://doi.org/10.1109/SSCI.2

    Article  Google Scholar 

  88. Huin L, Boulanger D, Disson E (2013) A MAS for access control management in cooperative information systems. In: Proceedings of the fifth international conference on management of emergent digital ecosystems (MEDES ‘13). Association for Computing Machinery, New York, NY, USA, pp 84–91. https://doi.org/10.1145/2536146.2536170

  89. Wang J, Chen K, Zhang Y (2017) Consensus of high-order nonlinear multiagent systems with constrained switching topologies. Complexity 2017, Article ID 5340642, 11:2017. https://doi.org/10.1155/2017/5340642

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science & Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Santanu Koley & Pinaki Pratim Acharjya

Authors
  1. Santanu Koley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pinaki Pratim Acharjya
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University Institute of Technology (UIT), Burdwan University, Bardhaman, West Bengal, India

    Shibakali Gupta

  2. Department of Computer Science and Engineering, University Institute of Technology (UIT), Burdwan University, Bardhaman, West Bengal, India

    Indradip Banerjee

  3. Rajnagar Mahavidyalaya, Birbhum, West Bengal, India

    Siddhartha Bhattacharyya

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Koley, S., Acharjya, P.P. (2022). Prevalence of Multi-Agent System Consensus in Cloud Computing. In: Gupta, S., Banerjee, I., Bhattacharyya, S. (eds) Multi Agent Systems. Springer Tracts in Human-Centered Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-0493-6_4

Download citation

Publish with us

Policies and ethics

Search

Navigation