A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Future Generation Computer Systems

Volumn 65, Issue , 2016, Pages 140-152

  Li, Zhongjin ^a   Ge, Jidong ^a,b   Yang, Hongji ^c   Huang, Liguo ^d   Hu, Haiyang ^e   Hu, Hao ^a   Luo, Bin ^a  

^a NANJING UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c BATH SPA UNIVERSITY   (United Kingdom)

^d Southern Methodist University   (United States)

^e HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

Big data application; Cloud computing; Deadline constraint; Particle swarm optimization (PSO); Scientific workflow scheduling; Security awareness

Indexed keywords

ALGORITHMS; BIG DATA; CLOUD COMPUTING; COSTS; DIGITAL STORAGE; PARTICLE SWARM OPTIMIZATION (PSO); SCHEDULING; SCHEDULING ALGORITHMS;

BIG DATA APPLICATIONS; CLOUD COMPUTING PLATFORMS; COST-AWARE SCHEDULING; DEADLINE CONSTRAINT; DISTRIBUTED INFRASTRUCTURE; META-HEURISTIC OPTIMIZATION TECHNIQUES; SCIENTIFIC WORKFLOWS; SECURITY AWARENESS;

OPTIMIZATION;

EID: 84955279987     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2015.12.014     Document Type: Article

References (54)

1. [1] Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K., Characterizing and profiling scientific workflows. Future Gener. Comput. Syst. 29:3 (2013), 682–692.
2. [2] A. Kashlev, S.Y. Lu, A system architecture for running big data workflows in the cloud, in: 2014 IEEE International Conference on Services Computing, SCC 2014, pp. 51–58.
3. [3] Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I., Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25:6 (2009), 599–616.
4. [4] Chang, V., Wills, G., A model to compare cloud and non-cloud storage of Big Data. Future Gener. Comput. Syst., 2015.
5. [5] Calheiros, R.N., Buyya, R., Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans. Parallel Distrib. Syst. 25:7 (2014), 1787–1796.
6. [6] Rodriguez, M.A., Buyya, R., Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2:2 (2014), 222–235.
7. [7] H.M. Fard, T. Fahringer, R. Prodan, Budget-constrained resource provisioning for scientific applications in clouds, in: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, CloudCom 2013, pp. 315–322.
8. [8] G. Thomas, A.B. Williams, Sequential auctions for heterogeneous task allocation in multiagent routing domains, in: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 1995–200.
9. [9] Iturriaga, S., Nesmachnow, S., Luna, F., Alba, E., A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems. J. Supercomput. 71:2 (2015), 648–672.
10. [10] Amazon EC2, 2015. http://aws.amazon.com/ec2/.
11. [11] A. Amin, R. Ammar, A. El Dessouly, Scheduling real time parallel structures on cluster computing with possible processor failures, in: Ninth International Symposium on Computers and Communications, ISCC 2004, pp. 62–67.
12. [12] Apvrille, A., Pourzandi, M., XML distributed security policy for clusters. Comput. Secur. 23:8 (2004), 649–658.
13. [13] Xie, T., Qin, X., Scheduling security-critical real-time applications on clusters. IEEE Trans. Comput. 55:7 (2006), 864–879.
14. [14] Song, S., Hwang, K., Kwok, Y.K., Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling. IEEE Trans. Comput. 55:6 (2006), 703–719.
15. [15] Xie, T., Qin, X., Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity. J. Parallel Distrib. Comput. 67:10 (2007), 1067–1081.
16. [16] Tang, X., Li, K., Zeng, Z., Veeravalli, B., A novel security-driven scheduling algorithm for precedence constrained tasks in heterogeneous distributed systems. IEEE Trans. Comput. 60:7 (2011), 1017–1029.
17. [17] Zeng, L.F., Veeravalli, B., Li, X.R., SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J. Parallel Distrib. Comput. 75 (2015), 141–151.
18. [18] Chang, V., The business intelligence as a service in the cloud. Future Gener. Comput. Syst. 37 (2014), 512–534.
19. [19] Chang, V., Towards a big data system disaster recovery in a private cloud. Ad Hoc Networks 35 (2015), 65–82.
20. [20] Chang, V., Walters, R.J., Wills, G.B., Organisational sustainability modelling-an emerging service and analytics model for evaluating cloud computing adoption with two case studies. Int. J. Inf. Manage. 36:1 (2016), 167–179.
21. [21] Chang, V., Kuo, Y.H., Ramachandran, M., Cloud computing adoption framework: A security framework for business clouds. Future Gener. Comput. Syst. 57 (2016), 24–41.
22. [22] F. Gens, IT cloud services user survey, pt.2: Top benefits & challenges, October 2008. URL: http://blogs.idc.com/ie/?p=210.
23. [23] W. Yurcik, X. Meng, G. Koenig, J. Greenseid, Cluster security as a unique problem with emergent properties, in: Fifth LCI International Conference on Linux Clusters: The HPC Revolution 2004, May 2004.
24. [24] A. Behl, K. Behl, An analysis of cloud computing security issues, in: 2012 World Congress on Information and Communication Technologies, WICT 2012, pp. 109–114.
25. [25] Ali, M., Khan, S.U., Vasilakos, A.V., Security in cloud computing: opportunities and challenges. Inform. Sci. 305 (2015), 357–383.
26. [26] J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.
27. [27] H. Yoshida, K. Kawata, Y. Fukuyama, Y. Nakanishi, A particle swarm optimization for reactive power and voltage control considering voltage stability, in: International Conference on Intelligent System Application to Power System, ISAP 1999, pp. 117–121.
28. [28] Sousa, T., Silva, A., Neves, A., Particle swarm based data mining algorithms for classification tasks. Parallel Comput. 30:5–6 (2004), 767–783.
29. [29] Louchet, J., Guyon, M., Lesot, M.J., Boumaza, A., Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern Recognit. Lett. 23:1–3 (2002), 335–345.
30. [30] Y.P. Wen, Z.G. Chen, T.M. Chen, J.X. Liu, G.S. Kang, A particle swarm optimization algorithm for batch processing workflow scheduling, in: 2012 Second International Conference on Cloud and Green Computing, CGC 2012, pp. 645–649.
31. [31] S. Pandey, L.L. Wu, S.M. Guru, R. Buyya, A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments, in: 24th IEEE International Conference on Advanced Information Networking and Applications, AINA 2010, pp. 400–407.
32. [32] K. Grolinger, M.A. Hayes, W.A. Higashino, A. L'Heureux, D.S. Allison, M.A.M. Capretz, Challenges for MapReduce in big data, in: 2014 IEEE 10th World Congress on Services, SERVICES 2014, pp. 182–189.
33. [33] Tan, W., Blake, M.B., Saleh, I., Dustdar, S., Social-network-sourced big data analytics. IEEE Internet Comput. 17:5 (2013), 62–69.
34. [34] C.K-S. Leung, R.K. MacKinnon, F. Jiang, Reducing the search space for big data mining for interesting patterns from uncertain data, in: 2014 IEEE International Congress on Big Data, BigData 2014, pp. 315–322.
35. [35] M. Mao, M. Humphrey, Auto-scaling to minimize cost and meet application deadlines in cloud workflows, in: 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2011, pp. 1–12.
36. [36] M. Malawski, G. Juve, E. Deelman, J. Nabrzyski, Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds, in: 2012 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 1–11.
37. [37] Abrishami, S., Naghibzadeh, M., Epema, D.H.J., Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans. Parallel Distrib. Syst. 23:8 (2012), 1400–1414.
38. [38] Abrishami, S., Naghibzadeh, M., Epema, D.H.J., Deadline-constrained workflow scheduling algorithms for Infrastructure as a service clouds. Future Gener. Comput. Syst. 29:1 (2013), 158–169.
39. [39] A. Oprescu, T. Kielmann, Bag-of-tasks scheduling under budget constraints, in: IEEE Second International Cloud Computing Technology and Science Conference, CloudCom 2010, pp. 351–359.
40. [40] Byuna, E.K., Keeb, Y.S., Kimc, J.S., Maeng, S., Cost optimized provisioning of elastic resources for application workflows. Future Gener. Comput. Syst. 27:8 (2011), 1011–1026.
41. [41] J. Li, S. Su, X. Cheng, Q. Huang, Z. Zhang, Cost-conscious scheduling for large graph processing in the cloud, in: 13th International Conference on High Performance Computing and Communications, HPCC 2011, pp. 808–813.
42. [42] Bittencourt, L.F., Madeira, E.R.M., HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. J. Internet Serv. Appl. 2:3 (2011), 207–227.
43. [43] L. Zeng, B. Veeravalli, X. Li, Scalestar: budget conscious scheduling precedence-constrained many-task workflow applications in cloud, in: 26th International Conference on Advanced Information Networking and Applications, AINA 2012, pp. 534–541.
44. [44] Zhu, Q., Agrawal, G., Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Trans. Serv. Comput. 5:4 (2012), 497–511.
45. [45] X.Y. Lin, C.Q. Wu, On scientific workflow scheduling in clouds under budget constraint, in: 2013 42nd International Conference on Parallel Processing, ICPP 2013, pp. 90–99.
46. [46] Malawski, M., Figiel, K., Nabrzyski, J., Cost minimization for computational applications on hybrid cloud infrastructures. Future Gener. Comput. Syst. 29:7 (2013), 1786–1794.
47. [47] F. Azzedin, M. Muthucumaru, A trust brokering system and its application to resource management in public-resource grids, in: 18th International Parallel and Distributed Processing Symposium, IPDPS 2004, pp. 22–31.
48. [48] Duan, R., Prodan, R., Li, X.R., Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid clouds. IEEE Trans. Cloud Comput. 2:1 (2014), 29–42.
49. [49] K.F. Deng, J.Q. Song, K.J. Ren, D. Yuan, J.J. Chen, Graph-cut based coscheduling strategy towards efficient execution of scientific workflows in collaborative cloud environments, in: 12th IEEE/ACM International Conference on Grid Computing, GRID 2011, pp. 34–41.
50. [50] J. Yu, R. Buyya, C.K. Tham, Cost-based scheduling of scientific workflow applications on utility grids, in: 2005 First International Conference on e-Science and Grid Computing, e-Science 2005, pp. 140–147.
51. [51] Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G., Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12:2 (2008), 171–195.
52. [52] Deb, K., An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Engrg. 186:2–4 (2000), 311–338.
53. [53] Coello Coello, C.A., Pulido, G.T., Lechuga, M.S., Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8:3 (2004), 256–279.
54. [54] Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. - Pract. Exp. 41:1 (2011), 23–50.