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Cluster Computing
Published in: Cluster Computing 3/2014

01-09-2014

Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies

Authors: Wenjuan Li, Jiyi Wu, Qifei Zhang, Keyong Hu, Jing Li

Published in: Cluster Computing | Issue 3/2014

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Abstract

Due to the restrictions that most traditional scheduling strategies only cared about users’ quality of service (QoS) time or cost requirements, lacked the effective analysis of users’ real service demand and could not guarantee scheduling security, this paper added trust into workflow’s QoS target and proposed a novel customizable cloud workflow scheduling model. In order to better analyze different user’s service requirements and provide customizable services, the new model divided workflow scheduling into two stages: the macro multi-workflow scheduling as the unit of cloud user and the micro single workflow scheduling. It introduced trust mechanism into multi-workflow scheduling level. And in single workflow scheduling level, it classified workflows into time-sensitive, cost-sensitive and balance three types according to different workflow’s QoS demand parameters using fuzzy clustering method. Based on it, it customized different service strategies for different type. The simulation experiments show that the new schema has some advantages in shortening workflow’s final completion time, achieving relatively high execution success rate and user satisfaction compared to other kindred solutions.
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Literature
1.
Liu, P.: Cloud Computing, 2nd edn. Publishing House of Electronics Industry, Beijing (2011)
2.
Foster, I., Kesselman, C.: “The Grid: Blueprint for a New Computing Infrastructure”. Morgan Kaufmann, San Francisco (1999)
3.
4.
Vouk, M.A.: Cloud computing-issues research and implementations, Proceedings ITI 2008 30th International Conference on Information Technology Interfaces, 2008. IEEE, (2008)
5.
Zhu, F., He, Y.: Parallel Scheduling Algorithm in Distributed Computing Theory and Design. Wuhang University Press, Wuhang (2003)
6.
Ullman, J.D.: Complexity of sequencing problems. In: Coffman, E.G. (ed.) Computer and Job Scheuling Theory. Wiley, New York (1976)
7.
Li, X., Gui, X.: Cognitive model of dynamic trust forecasting. J. Softw. 21(1), 163–176 (2010)CrossRef
8.
Li, X., Gui, X.: Research on dynamic trust model for large scale distributed environment. J. Softw. 18(6), 1510–1521 (2007)CrossRefMathSciNet
9.
Pan, J., Xu, F., Lv, J.: Reputation-based recommender discovery approach for service selection. J. Softw. 21(2), 388–400 (2010)CrossRef
10.
Hu, J., Wu, Q., Zhou, B., et al.: Robust feedback credibility-based distributed P2P trust model. J. Softw. 20(10), 2885–2898 (2009)CrossRef
11.
Wang, S., Zhang, L., Li, H.: Evaluation approach of subjective trust based on cloud model. J. Softw. 21(6), 1341–1352 (2010)CrossRef
12.
Hu, C., Chang, B., Xu, H., Zhao, Q.: Approach of service combination based on deepness trust reasoning in complex cross-organizational collaboration. J. Central South University (Science and Technology) 43(2), 567–575 (2012)
13.
Wang, Y., Xiao, X., Jia, L.: Dynamic and comprehensive evaluation method for interoperability trust based on fuzzy variable weighting. J. Comput. Res. Dev. 49(6), 1235–1242 (2012)
14.
Xie, X., Liu, L., Zhao, P.: Trust model based on double incentive and deception detection for cloud computing. J. Electron. Inform. Technol. 34(4), 812–817 (2012)
15.
Wei, Z., Zhou, W., Ren, X., Wei, Q., et al.: A strategy-proof trust based decision mechanism for pervasive computing environments. Chin. J. Comput. 35(5), 871–882 (2012)CrossRef
16.
Liu, W., Yin, L., Fang, B., Zhang, H.: A hierarchical trust model for the internet of things. Chin. J. Comput. 35(5), 846–855 (2012)CrossRef
17.
Long, J., Liu, X., Yuan, X., Zhang, Z., et al.: A web services composition strategy based on trust reasoning and evolution. Chin. J. Comput. 35(2), 298–314 (2012)CrossRef
18.
Qi, x, Jiyi, W., Guilin, W., Wenhao, L., et al.: Provably secure authentication protocol based on convertible proxy signcryption in cloud computing. Sci. China 42(3), 303–313 (2012)
19.
Jiyi, W., Ping, L., Pan, X., Zhuo, L.: Cloud computing: concept and platform. Telecommun. Sci. 33(3), 59–66 (2012)
20.
Li, Wenjuan, Ping, L., Li, J., Qiu, Q.: Cloud service discovery algorithm based on trust fuzzy comprehensive evaluation. ICIC Express Lett. Part B 3(2), 1–6 (2012)
21.
Li, W., Ping, L., Pan, X.: Trust model to enhance security and interoperability of cloud environment. In: Proceedings of the 1st International Conference on Cloud Computing (CloudCom’09), pp. 69–79. Springer, Berlin (2009)
22.
Li, W., Ping, L., Qiu, Q., Zhang, Q.: Research on trust management strategies in cloud computing environment. J. Comput. Inform. Syst. 8(4), 1757–1763 (2012)
23.
Zhao, G.: Research on adaptive genetic algorithm based service workflow scheduling problem. Sun yat-sen university, Guangzhou (2010)
24.
Yu, J., Tian, G., Cao, Y., et al.: A resource allocating algorithm in reliability. J. Comput. Res. Dev. 46(2), 1821–1829 (2009)
25.
Yuan, Y., Li, X., Wang, Q., et al.: Time optimization heuristics for scheduling budget\_constrained grid workflows. J. Comput. Res. Dev. 46(2), 194–201 (2009)
26.
Li, J.: QoS Optimal, Grid Workflow Scheduling Algorithm. Guangxi University, Guilin (2009)
27.
Wang, Y., Hu, C., Du, Z.: QoS-awared grid workflow schedule. J. Softw. 17(11), 2341–2351 (2006)CrossRefMATH
28.
Azzedin, F., Maheswaran, M.: Integrating trust into grid resource management systems. Proceedings of the 2002 International Conference on Parallel Processing, pp. 47–54. Vancouver, British Columbia (2002)
29.
He, X., Sun, X., Gregor, V.L.: QoS guided Min-rain heuristic for grid task scheduling. J. Comput. Sci. Technol. 18(4), 442–451 (2003)CrossRefMATH
30.
Weng, C., Lu, X.: Heuristic scheduling for bag -of-tasks applications in combination with QoS in the computational grid. Future Gener. Comput. Syst. 21(2), 271–280 (2005)CrossRef
31.
Buyya, R., Abramson, D., Giddy, J., et al.: Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. 14(13–15), 1507–1542 (2002)
32.
Hum, P.M., Thompson, M.R.: Security implications of typical grid computing usage scenario, pp. 95–103. Proceedings of the IEEE HPDC, San Francisco, CA (2001)
33.
Li K., He Y., Liu X.: Security-driven scheduling algorithms based on eigentrust in grid. In: Proceedings of the 6th International Conference of Parallel and Distributed Computing Applications and Technologies, Denver, USA, pp. 1068–1072 (2005)
34.
Zhang, W., Fang, B., et al.: A trust-QoS enhanced grid service scheduling. chin. J. Comput. 29(7), 1157–1166 (2006)
35.
Yuan, L., Zeng, G., et al.: Dynamic level scheduling based on trust model in grid computing. Chin. J. Comput. 29(7), 1217–1224 (2006)
36.
Hu, Z., Juan, H.: Grid workflow scheduling algorithm under constraints of trust. Appl. Res. Comput. 27(8), 2936–2938 (2010)
37.
Hu, C., Wu, M., Liu, G.: QoS scheduling based on trust relationship in web service workflow. Chin. J. Comput. 32(1), 42–53 (2009)
38.
Hu, C., Wu, M., Liu, G., et al.: An approach to constructing web service workflow based on business spanning graph. J. Softw. 18(8), 1870–1882 (2007)
39.
Han, J.: Research on grid workflow scheduling algorithm under the constraints of trust. Zhongnan University, Changsha (2010)
40.
Wang, X.: On the policy description and quantification model for trust management. National University of Defense Technology, Changsha (2009)
41.
Li, W., Zhang, Q., Ping, L., Pan, X.: Cloud scheduling algorithm based on fuzzy clustering. J. Commun. 33(3), 146–154 (2012)
42.
Li, W., Pan, X., Zhang, Q., Ping, L.: A novel job scheduling model to enhance efficiency and overall user fairness of cloud computing environment. In: Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER2011), pp. 152–157. SCITePress, Noordwijkerhout (2011)
43.
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefMATH
44.
45.
46.
Buyya, R., Ranjan, R., and Calheiros, R.N.: “Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities”. In: Proceedings of the 7th High Performance Computing and Simulation Conference (HPCS09), IEEE Computer Society, June 2009
47.
Chung, J.Y., Shih, W.K., Jane, W.S., et al.: Scheduling periods jobs that allow imprecise results. IEEE Trans. Comput. 39(9), 1156–1174 (1990)CrossRef
48.
Yu, A.C., Lin, K.J.: Scheduling parallelizable imprecise computations on multiprocessor. In: Proceedings of the 5th International Symposium on Parallel Processing. Anaheim, CA, USA, pp. 531–536 April 30–May 2 1991
49.
Zhang, W., Liu, X., Yun, X., Zhang, H., et al.: Trust-driven job scheduling heuristics for computing grid. J. Commun. 27(2), 73–79 (2006)
50.
Zhao, C.: Research and Realization of Job Scheduling Algorithm in Cloud Environment. Beijing Jiaotong University, Beijing (2009)
Metadata
Title
Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies
Authors
Wenjuan Li
Jiyi Wu
Qifei Zhang
Keyong Hu
Jing Li
Publication date
01-09-2014
Publisher
Springer US
Published in
Cluster Computing / Issue 3/2014
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-013-0340-1

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