SABA: A security-aware and budget-aware workflow scheduling strategy in clouds

Journal of Parallel and Distributed Computing

Volumn 75, Issue , 2015, Pages 141-151

  Zeng, Lingfang ^a   Veeravalli, Bharadwaj ^a   Li, Xiaorong ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b INSTITUTE OF HIGH PERFORMANCE COMPUTING   (Singapore)

Author keywords

Budget; Cloud computing; Scheduling; Security; Workflow

Indexed keywords

BUDGET CONTROL; CLOUD COMPUTING; SCHEDULING;

BUDGET; CLOUD SERVICE PROVIDERS; SCHEDULING PERFORMANCE; SCHEDULING STRATEGIES; SECURITY; SECURITY REQUIREMENTS; WORKFLOW; WORKFLOW APPLICATIONS;

MULTITASKING;

EID: 84918795447     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2014.09.002     Document Type: Article

References (51)

1. V. Aravinthan, and W. Jewell Optimized maintenance scheduling for budget-constrained distribution utility IEEE Trans. Smart Grid 4 4 2013 2328 2338
2. F. Azzedin, M. Muthucumaru, A trust brokering system and its application to resource management in public-resource grids, in: Proc. of 18th International Parallel and Distributed Processing Symposium, IPDPS, 2004.
3. D. Bell, and L. LaPadula Secure computer systems: mathematical foundations, Tech. Rep. 2547, Mitre Corp., Bedford, MA, March 1973
4. S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M. Su, K. Vahi, Characterization of scientific workflows, in: Proc. of the 3rd Workshop on Workflows in Support of Large-Scale Science, 2008.
5. L.F. Bittencourt, and E.R.M. Madeira HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds J. Internet Serv. Appl. 2 3 2011 207 227
6. B. Blanton, H. Lander, R. Luettich, M. Reed, K. Gamiel, K. Galluppi, Computational aspects of storm surge simulation, in: Ocean Sciences Meeting, 2008.
7. D. Brown, A.D.P. Brady, J. Cao, B. Johnson, and J. McNabb Workflows for e-Science A case study on the use of workflow technologies for scientific analysis: gravitational wave data analysis 2006 Springer (Chapter)
8. E.-K. Byuna, Y.-S. Keeb, J.-S. Kimc, and S. Maeng Cost optimized provisioning of elastic resources for application workflows Future Gener. Comput. Syst. 27 8 2011 1011 1026
9. U.V. Catalyurek, K. Kaya, B. Ucar, Integrated data placement and task assignment for scientific workflows in clouds, in: Proc. of the Fourth International Workshop on Data-Intensive Distributed Computing, DIDC, 2011.
10. A. Chervenak, E. Deelman, I. Foster, L. Guy, W. Hoschek, A. Iamnitchi, C. Kesselman, P. Kunszt, M. Ripeanu, B. Schwartzkopf, H. Stockinger, K. Stockinger, B. Tierney, Giggle: a framework for constructing scalable replica location services, in: Proc. of the ACM/IEEE Conference on Supercomputing, SC, 2002, pp. 1-17.
11. A. Chervenak, E. Deelman, M. Livny, M.-H. Su, R. Schuler, S. Bharathi, G. Mehta, K. Vahi, Data placement for scientific applications in distributed environments, in: Proc. of the 8th Grid Computing Conference, 2007, pp. 267-274.
12. J. Ding, Y. Wang, J. Le, Y. Jin, Dynamic scheduling for workflow applications over virtualized optical networks, in: IEEE Conference on Computer Communications Workshops, 2011, pp. 127-132.
13. P.-F. Dutot, T. N'Takpe, F. Suter, and H. Casanova Scheduling parallel task graphs on (almost) homogeneous multicluster platforms IEEE Trans. Parallel Distrib. Syst. 20 7 2009 940 952
14. F. Gens, IT Cloud services user survey, pt.2: top benefits & challenges, October 2008. URL: http://blogs.idc.com/ie/?p=210.
15. G. Gharooni-fard, F. Moein-darbari, H. Deldari, A. Morvaridi, Scheduling of scientific workflows using a chaos-genetic algorithm, in: International Conference on Computational Science, ICCS, Vol. 1 (1), 2010, pp. 1445-1454.
16. W. Jansen, T. Grance, Guidelines on security and privacy in public cloud computing, January 2011. URL: http://csrc.nist.gov/publications/drafts/800-144/Draft-SP-800-144cloudcomputing.pdf.
17. D. Jung, J. Lim, H. Yu, J. Gil, and E. Lee A workflow scheduling technique for task distribution in spot instance-based cloud Ubiquitous Information Technologies and Applications, Vol. 280 2014 Springer Berlin, Heidelberg 409 416 (Chapter)
18. Y. Kwong Kwok, and I. Ahmad Static scheduling algorithms for allocating directed task graphs to multiprocessors ACM Comput. Surv. 31 4 1999 406 471
19. Y.C. Lee, R. Subrata, and A.Y. Zomaya On the performance of a dual-objective optimization model for workflow applications on grid platforms IEEE Trans. Parallel Distrib. Syst. 20 9 2009 1273 1284
20. Y.C. Lee, and A.Y. Zomaya On effective slack reclamation in task scheduling for energy reduction J. Inf. Process. Syst. 5 4 2009 175 186
21. M. Levin, Storage management disciplines are declining, June 2006. URL: http://www.computereconomics.com/article.cfm?id=1129.
22. J. Li, S. Su, X. Cheng, Q. Huang, Z. Zhang, Cost-conscious scheduling for large graph processing in the cloud, in: Proc. of the 13th International Conference on High Performance Computing and Communications, HPCC, 2011, pp. 808-813.
23. X. Lin, C. Wu, On scientific workflow scheduling in clouds under budget constraint, in: Proc. of the 42nd International Conference on Parallel Processing, ICPP, 2013, pp. 90-99.
24. J. Livny, H. Teonadi, M. Livny, and M. Waldor High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs PLoS ONE 3 9 2008 e3197
25. V. Lynch, J. Cobb, E. Farhi, S. Miller, M. Taylor, Virtual experiments on the neutron science teragrid gateway, in: TeraGrid, 2008.
26. J.C. Maces, A. van Moorsel, P. Watson, The case for dynamic security solutions in public cloud workflow deployments, in: Proc. of IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops, DSN-W, 2011, pp. 111-116.
27. M. Malawski, G. Juve, E. Deelman, J. Nabrzyski, Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds, in: Proc. of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC, 2012, pp. 1-11.
28. M. Mao, M. Humphrey, Scaling and scheduling to maximize application performance within budget constraints in cloud workflows, in: Proc. of IEEE 27th International Symposium on Parallel & Distributed Processing, IPDPS, 2013, pp. 67-78.
29. P. Mell, T. Grance, The NIST definition of cloud computing, version 15, October 2009. URL: http://csrc.nist.gov/groups/SNS/cloud-computing.
30. A. Oprescu, T. Kielmann, Bag-of-tasks scheduling under budget constraints, in: Proc. IEEE Second Int. Cloud Computing Technology and Science Conf., 2010, pp. 351-359.
31. R. Prodan, and M. Wieczorek Bi-criteria scheduling of scientific grid workflows IEEE Trans. Autom. Sci. Eng. 7 2 2010 364 376
32. A. Quiroz, H. Kim, M. Parashar, N. Gnanasambandam, N. Sharma, Towards autonomic workload provisioning for enterprise grids and clouds, in: Proc. 10th IEEE/ACM Int. Grid Computing Conf., 2009, pp. 50-57.
33. L. Ramakrishnan, B. Plale, A multi-dimensional classification model for workflow characteristics, in: Workflow Approaches to New Data-Centric Science, with ACM SIGMOD, 2010, pp. 1-12.
34. M. Singhal, S. Chandrasekhar, T. Ge, R. Sandhu, R. Krishnan, G.-J. Ahn, and E. Bertino Collaboration in multicloud computing environments: framework and security issues IEEE Comput. 46 2 2013 76 84
35. S. Song, K. Hwang, and Y.-K. Kwok Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling IEEE Trans. Comput. 55 2006 703 719
36. S.X. Sun, Q. Zeng, and H. Wang Process-mining-based workflow model fragmentation for distributed execution IEEE Trans. Syst. Man. Cybern. Part A 41 2 2011 294 310
37. X. Tang, K. Li, Z. Zeng, and B. Veeravalli A novel security-driven scheduling algorithm for precedence constrained tasks in heterogeneous distributed systems IEEE Trans. Comput. 60 7 2011 1017 1029
38. J.L. Tilson, G. Rendon, M.-F. Ger, E. Jakobsson, MotifNetwork: A grid-enabled workflow for high-throughput domain analysis of biological sequences: Implications for annotation and study of phylogeny, protein interactions, and intraspecies variation, in: Proc. 7th IEEE Int. Conf. Bioinformatics and Bioengineering BIBE 2007, 2007, pp. 620-627.
39. H. Topcuoglu, S. Hariri, and M.-Y. Wu Performance-effective and low complexity task scheduling for heterogeneous computing IEEE Trans. Parallel Distrib. Syst. 13 3 2002 260 274
40. Y. Wang, and P. Lu Dataflow detection and applications to workflow scheduling Concurr. Comput.: Pract. Exp. 23 2011 1261 1283
41. Y. Wang, and W. Shi On scheduling algorithms for MapReduce jobs in heterogeneous clouds with budget constraints Principles of Distributed Systems, Vol. 8304 2013 Springer International Publishing 251 265 (Chapter)
42. P. Watson, A multi-level security model for partitioning workflows over federated clouds, in: Proc. of IEEE Third International Conference on Cloud Computing Technology and Science, CloudCom, 2011, pp. 180-188.
43. T. Xie, and X. Qin Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity J. Parallel Distrib. Comput. 67 2007 1067 1081
44. J. Yu, R. Buyya, A budget constrained scheduling of workflow applications on utility grids using genetic algorithms, in: Proc. Workshop Workflows in Support of Large-Scale Science, 2006, pp. 1-10.
45. J. Yu, R. Buyya, C.K. Tham, Cost-based scheduling of scientific workflow applications on utility grids, in: Proc. of First International Conference on e-Science and Grid Computing, 2005. http://dx.doi.org/10.1109/E-SCIENCE.2005.26.
46. D. Yuan, Y. Yang, X. Liu, and J. Chen A data placement strategy in scientific cloud workflows Future Gener. Comput. Syst. 26 8 2010 1200 1214
47. D. Yuan, Y. Yang, X. Liu, and J. Chen On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems J. Parallel Distrib. Comput. 71 2 2011 316 332
48. L. Zeng, B. Veeravalli, X. Li, ScaleStar: budget conscious scheduling precedence-constrained many-task workflow applications in cloud, in: Proc. of the 26th International Conference on Advanced Information Networking and Applications, AINA, Fukuoka, Japan, 2012, pp. 534-541.
49. H. Zhao, R. Sakellariou, Scheduling multiple DAGs onto heterogeneous systems, in: Proc. 20th International Parallel and Distributed Processing Symposium, IPDPS, 2006.
50. W. Zheng, and R. Sakellariou Budget-deadline constrained workflow planning for admission control J. Grid Comput. 11 4 2013 633 651
51. Q. Zhu, G. Agrawal, Resource provisioning with budget constraints for adaptive applications in cloud environments, in: Proc. of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC'10, 2010, pp. 304-307.