Research article of American Journal of Computer Sciences and Applications
Modeling a Fault Tolerant Control Mechanism for Cloud e-marketplaces using Raft Consensus Protocol (RCP)
Ige A.O.a, Akingbesote A.O.a, Oriola O.a, and Aranuwa F.O.a
aComputer Science Department, Adekunle Ajasin University, Akungba – Akoko, Ondo State, Nigeria
The evolution of marketplaces started from the traditional marketplace, the internet marketplace, the web service marketplace, the grid marketplace, before moving to cloud e-marketplace. The need to have rapid access to various service by different customers brought about cloud e-marketplaces. The goal of the cloud e-marketplace is to attract the biggest possible number of buyers while ensuring a reduced waiting time for customers and maximized profit for cloud service providers. Challenges like security, performance and fault tolerance are of great concern in the cloud market. While discussion on the issue of security and performance are ongoing, that of fault tolerance is yet to be fully addressed. Although some researchers have proposed the use of multiple servers in achieving the main idea of the cloud e-marketplace, different kinds of faults still affects the performance of cloud e-market. Balancing of providers’ cost and customers’ waiting time is still a major concern. Various techniques have been proposed in solving these problems. However, these techniques only work in a static environment where these servers can be faulty which may lead to long waiting time. We propose the use of Raft consensus protocol as our fault tolerant approach. We use the dynamic environment as against the already static approached already discussed in the literature. In the dynamic environment, two fault tolerant centers that are capable of surviving failure caused by server overload or congestion are used. These are primary and the reservoir centers. The Raft Consensus Protocol is used in both centers to coordinate the servers and make sure that each of the servers exist either as the leader, a candidate, or a as a follower. A waiting time counter algorithm is developed that directs customers request to the primary center when waiting time t<N, and to the reservoir center when waiting time t≥N. We set up our consumer arrival time, the service time is recorded and our N is set to 5 sec. Furthermore, the result of this research when compared to existing system using various performance metrics showed that the developed mechanism allowed optimal performance in the servers used for cloud e-marketplaces’ service delivery, thereby causing reduced waiting time and increased profit.
Keywords: marketplaces, cloud e-market, raft consensus protocol, waiting time, profit.
How to cite this article:
Ige A.O., Akingbesote A.O., Oriola O., and Aranuwa F.O., Modeling a Fault Tolerant Control Mechanism for Cloud e-marketplaces using Raft Consensus Protocol (RCP). American Journal of Computer Sciences and Applications, 2019; 3:14
1. D. Felipe, S. Al Zahr, M. Gagnaire, J. Laisn, and I. J. Marshall, “CompatibleOne : Bringing Cloud as a Commodity,” 2014.
2. A. O. Akingbesote, M. O. Adigun, S. S. Xulu, and E. Jembere, “Performance evaluation of cloud e-marketplaces using non preemptive queuing model,” 2014 World Congr. Sustain. Technol. WCST 2014, pp. 66–71, 2015.
3. K. Rowley, Chris Hee-Dong, Yang Sora, “Electronic-marketplaces and their evolving benefits over time,” Part 1 Mark. Types Res. Quest., no. July, 2009.
4. A. Kambil and V. Heck, “Reengineering the Dutch flower auctions: A framework for analyzing exchange organisations.,” Inf. Syst. Res., vol. Vol. 9, no. 1, pp. 1–19, 1998.
5. R. Klueber, F. Leser, and N. Kaltenmorgen, “Concept and procedure for evaluating e-markets,” Proc. Seventh Am. Conf. Inf. Syst. Boston., 2001.
6. H. M. Mutlu and A. Sürer, “Effects of market, e-marketing, and technology orientations on innovativeness and performance in Turkish health organizations,” Health Mark. Q., vol. 33, no. 2, pp. 95–111, 2016.
7. Q. Duan, “Author ’ s Accepted Manuscript Cloud Service Performance Evaluation : Status , Challenges , and Opportunities – A Survey from the System Modeling Perspective,” Digit. Commun. Networks, 2016.
8. P. Bonacquisto, G. Di Modica, G. Petralia, and O. Tomarchio, “A strategy to optimize resource allocation in auction-based cloud markets,” Proc. – 2014 IEEE Int. Conf. Serv. Comput. SCC 2014, pp. 339–346, 2014.
9. R. Buyya, C. S. Yeo, and S. Venugopal, “Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities,” in Proceedings – 10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008, 2008, pp. 5–13.
10. M. Armbrust, A. Fox, R. Griffith, A. Joseph, and H. Ranfy, “Above the clouds: A Berkeley view of cloud computing,” Univ. California, Berkeley, Tech. Rep. UCB, pp. 07–013, 2009.
11. P. Mell and T. Grance, “The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology,” Nist Spec. Publ., vol. 145, p. 7, 2011.
12. L. Du, “Pricing and resource allocation in a cloud computing market,” Proc. – 12th IEEE/ACM Int. Symp. Clust. Cloud Grid Comput. CCGrid 2012, pp. 817–822, 2012.
13. H. Khazaei, J. Misic, and V. B. Misic, “Performance analysis of cloud computing centers using M/G/m/m+r queuing systems,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 5, pp. 936–943, 2012.
14. E. Pakbaznia and M. Pedram, “Minimizing data center cooling and server power costs,” Proc. 14th ACM/IEEE Int. Symp. Low power Electron. Des. – ISLPED ’09, p. 145, 2009.
15. L. Wu, S. K. Garg, and R. Buyya, “SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments,” in 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2011, pp. 195–204.
16. A. . Akingbesote and M. O. Adigun, “Perfomance modeling of Cloud E-marketplace using Dynamic Control Model,” December 2014 Sci. Res. Ann., vol. Vol. 6, no. No 1, pp. 69–79, 2014.
17. D. Ongaro, “Consensus: Bridging Theory and Practice,” 2014.
18. C. Giovanoli, P. Pulikal, and S. G. Grivas, “E-Marketplace for Cloud Services,” Cloud Comput. 2014 Fifth Int. Conf. Cloud Comput. Grids, Virtualization E-Marketplace, no. c, pp. 76–83, 2014.
19. A. Ailijiang, A. Charapko, and M. Demirbas, “Consensus in the cloud: Paxos systems demystified,” 2016 25th Int. Conf. Comput. Commun. Networks, ICCCN 2016, 2016.
20. D. R. Ferreira and J. J. Pinto Ferreira, “Building an e-marketplace on a peer-to-peer infrastructure,” Int. J. Comput. Integr. Manuf., vol. 17, no. 3, pp. 254–264, 2004.
21. A. . Akingbesote, “Modelling the performance of web services in cloud e- marketplaces based on consumer waiting time and provider cost,” 2014.
22. A. Alsarhan, A. Itradat, A. Y. Al-Dubai, A. Y. Zomaya, and G. Min, “Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments,” IEEE Trans. Parallel Distrib. Syst., vol. 9219, no. c, pp. 1–13, 2017.
23. Y. Choi and Y. Lim, “Optimization Approach for Resource Allocation on Cloud Computing for IoT,” Int. J. Distrib. Sens. Networks, vol. 2016, 2016.
24. S. H. Lin, R. Pal, M. Paolieri, and L. Golubchik, “Performance Driven Resource Sharing Markets for the Small Cloud,” Proc. – Int. Conf. Distrib. Comput. Syst., pp. 241–251, 2017.
25. S. Paliwal, “Performance Challenges in Cloud Computing,” 2014.
26. Q. Honghong, “A model for value-added e-market provisioning: case study from,” Proc. 2008 2nd Int. Conf. Futur. Gener. Commun. Networking, FGCN 2008, vol. 1, pp. 47–52, 2008.
27. S. Sundareswaran, A. Squicciarini, and D. Lin, “A brokerage-based approach for cloud service selection,” in Proceedings – 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012, 2012, pp. 558–565.
28. NIBusiness, “E-marketplaces, online auctions and exchanges,” 2016. [Online]. Available: https://www.nibusinessinfo.co.uk/content/types-e-marketplace. [Accessed: 18-Apr-2018].
29. M. A. Lindemann and B. F. Schmid, “Framework for Specifying, Building, and Operating Electronic Markets,” Int. J. Electron. Commer., vol. 3, no. 2, pp. 7–21, 1998.
30. N. Moganarangan, R. G. Babukarthik, S. Bhuvaneswari, M. S. S. Basha, and P. Dhavachelvan, “A novel algorithm for reducing energy-consumption in cloud computing environment : Web service computing approach,” J. King Saud Univ. – Comput. Inf. Sci., vol. 28, no. 1, pp. 55–67, 2016.
31. A. Souri and N. J. Navimipour, “Expert Systems with Applications Behavioral modeling and formal verification of a resource discovery approach in Grid computing,” Expert Syst. Appl., vol. 41, no. 8, pp. 3831–3849, 2014.
32. P. Kukreja and D. Sharma, “A Detail Review on Cloud , Fog and Dew Computing,” vol. 5, no. 5, pp. 1412–1420, 2016.
33. J. G. Breslin, D. O. Sullivan, A. Passant, and L. Vasiliu, “Computers in Industry Semantic Web computing in industry,” Comput. Ind., vol. 61, no. 8, pp. 729–741, 2010.
34. K. R. Jackson, S. Canon, S. Cholia, J. Shalf, H. J. Wasserman, and N. J. Wright, “Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud,” 2nd IEEE Int. Conf. cloud Comput. Technol. Sci., pp. 159–168, 2011.
35. Louise Balle, “Difference Between a Marketplace and an eMarketspace | Chron.com,” 2016. [Online]. Available: http://smallbusiness.chron.com/difference-between-marketplace-emarketspace-25645.html. [Accessed: 19-Apr-2018].
36. R. Curran et al., “The traditional marketplace : serious leisure and recommending authentic travel,” Serv. Ind. J., vol. 0, no. 0, pp. 1–17, 2018.
37. S. Gonzalez and P. Waley, “Traditional retail markets: The new gentrification frontier?,” Antipode, vol. 45, no. 4, pp. 965–983, 2013.
38. J. Cave, “Digital Marketing Vs. Traditional Marketing: Which One is Better,” Digital Doughnut, 2016. [Online]. Available: https://www.digitaldoughnut.com/articles/2016/july/digital-marketing-vs-traditional-marketing.
39. Heck, Kambil, and Van, “How Firms Can Design and Profit from Online Auctions and Exchanges,” Harvard Bus. Press., 2002.
40. W. Zhe and H. V Madhyastha, “Rethinking Cloud Service Marketplaces,” in Proceedings of the 15th ACM Workshop on Hot Topics in Networks – HotNets ’16, 2016, pp. 134–140.
41. P. Balco, J. Law, and M. Drahošová, “Cloud market analysis from customer perspective,” in Procedia Computer Science, 2017, vol. 109, pp. 1022–1027.
42. M. Stoshikj, N. Kryvinska, and C. Strauss, “Efficient managing of complex programs with project management services,” Glob. J. Flex. Syst. Manag., vol. 15, no. 1, pp. 25–38, 2014.
43. Gartner, “Gartner says worldwide public cloud services Market to grow 18percent in 2017,” 2017. [Online]. Available: www.gartner.com/newsroom/id/3616417.
44. Gleb, “Choosing the right cloud service: IaaS, PaaS, or SaaS,” http://rubygarage.org/blog/iaas-vs-paas-vs-saas, 2017. [Online]. Available: https://rubygarage.org/blog/iaas-vs-paas-vs-saas.
45. A. Ezenwoke, O. Daramola, and M. Adigun, “Towards a Fuzzy-oriented Framework for Service Selection in Cloud e- Towards a Fuzzy-oriented Framework for Service Selection in Cloud,” no. January, 2017.
46. S. M. Parikh, “A survey on cloud computing resource allocation techniques.,” Pap. Present. 2013 Nirma Univ. Int. Conf. Eng., 2013.
47. G. G. and D. Stirzaker, “Probability and Random Processes,” in third ed. Oxford Univ. Press, 2010.
48. S. Yin, H. Luo, and S. X. Ding, “Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization,” Ind. Electron. IEEE Trans., vol. 61, no. 5, pp. 2402–2411, 2014.
49. A. Bala and I. Chana, “Fault Tolerance- Challenges , Techniques and Implementation in Cloud Computing,” Int. J. Comput. Sci., vol. 9, no. 1, pp. 288–293, 2012.
50. M. Yadin and P. Naor, “Queueing Systems with a Removable Service Station Queueing Systems with a Removable Service Stationt,” Source OR, vol. 14, no. 4, pp. 393–405, 1963.
51. B. Johnson, “Fault-tolerant microprocessor-based systems,” IEEE Micro, vol. 4, no. 6, pp. 6–21, 1984.
52. J. Lim, T. Suh, and J. Gil, “Scalable and leaderless Byzantine consensus in cloud computing environments,” Springer US J. Inf Syst Front, vol. 16, no. 19, 2013.
53. S. Hwang and C. Kesselman, “A Flexible Framework for Fault Tolerance in the Grid,” pp. 251–272, 2004.
54. A. Avizienis, “Fault-Tolerant Systems,” IEEE Trans. Comput., vol. C-25, no. 12, pp. 1304–1312, 1976.
55. R. D. Schlichting and F. B. Schneider, “Fail-stop processors: an approach to designing fault-tolerant computing systems,” ACM Trans. Comput. Syst., vol. 1, no. 3, pp. 222–238, 1983.
56. V. Loia, V. Terzija, and A. Vaccaro, “An Affine-Arithmetic-Based Consensus Protocol for Smart-Grid Computing in the Presence of Data Uncertainties,” IEEE Trans. Ind. Electron., vol. 62, no. May 05, 2015.
57. L. Lamport, “The part-time parliament,” ACM Trans. Comput. Syst., vol. 16, no. 2, pp. 133–169, 1998.
58. R. van Renesse, “Paxos Made Moderately Complex,” ACM Comput. Surv., vol. 47, no. 3, pp. 1–36, 2011.
59. G. M. D. Vieira and L. E. Buzato, “The Performance of Paxos and Fast Paxos,” XXVII Simpósio Bras. Redes Comput., pp. 291–304, 2013.
60. B. R. Metzner and T. Psychedelic, “Revisiting the,” vol. xx, no. 1, pp. 32–33, 2015.
61. H. Meling and L. Jehl, “Tutorial summary: Paxos explained from scratch,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8304 LNCS, pp. 1–10, 2013.
62. K. Marzullo, a Mei, and H. Meling, “A Simpler Proof for Paxos and Fast Paxos,” Wwwusers.Di.Uniroma1.It, vol. 2, no. 3, pp. 1–7, 2010.
63. P. J. Marandi, S. Benz, F. Pedone, and K. Birman, “The performance of paxos in the cloud,” Proc. IEEE Symp. Reliab. Distrib. Syst., vol. 2014–Janua, pp. 41–50, 2014.
64. J. Kirsch, “Paxos for System Builders,” Proc. 2nd Work. Large-Scale …, no. March, p. 35, 2008.
65. J. Gray and L. Lamport, “Consensus on transaction commit,” ACM Trans. Database Syst., vol. 31, no. 1, pp. 133–160, 2006.
66. C. Based, L. Manager, and S. Yethadka, “A Distributed Lock Manager Using Paxos,” no. April, 2013.
67. L. Lamport et al., “In Search of an Understandable Consensus Algorithm,” Atc ’14, vol. 22, no. 2, pp. 305–320, 2014.
68. Chandra, R. Griesmer, and J. Redstone, “Paxos made live: an engineering perspective.,” Proc. Pod. ACM Symp. Princ. Distrib. Comput., vol. 7, pp. 398–407, 2007.
69. S. A. M. Toueg, “Unreliable Distributed Failure Detectors Systems for Reliable,” no. 2, pp. 225–267, 1996.
70. S. Jose, “Consensus in the Presence of Partial Synchrony,” vol. 35, no. 2, pp. 288–323, 1988.
71. H. Howard, “ARC : Analysis of Raft Consensus,” 2014.
72. I. Foster and C. Kesselman, “The Globus Toolkit,” in I. Foster and C. Kesselman (eds.), The GRID: Blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers, 1998, pp. 259–278.
73. A. O. Ige, A. O. Akingbesote, and O. L. Ogbeide, “Distributed Approach to Cloud Oriented e-Marketplaces : A Constructive Review,” Int. J. Eng. Comput. Sci., vol. 7, no. 10, pp. 24319–24325, 2018.
74. R. N. Calheiros, R. Ranjan, A. Beloglazov, and A. F. De Rose, “CloudSim : a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” no. August 2010, pp. 23–50, 2011.
75. J. P. Barthelemy and M. F. Janowitz, “Formal theory of consensus*,” Soc. Ind. Appl. Math., vol. 4, no. 3, pp. 305–322, 1991.
76. O. George, “The Solution of some Queueing Problems,” J. Soc. Ind. Appl. Math., vol. 2, no. 3, pp. 133–142, 1954.
77. C. L. Dumitrescu and I. Foster, “GangSim: A simulator for grid scheduling studies.,” in IEEE International Symposium on Cluster Computing and the Grid, 2005, pp. 1151–1158.
78. A. Legrand, L. Marchal, and H. Casanova, “Scheduling distributed applications: The SimGrid simulation framework,” in Third IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003, pp. 138–145.