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Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

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Qing-Hua Zhu, Huan Tang, Jia-Jie Huang, and Yan Hou, "Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 848-865, Apr. 2021. doi: 10.1109/JAS.2021.1003934
Citation: Qing-Hua Zhu, Huan Tang, Jia-Jie Huang, and Yan Hou, "Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 848-865, Apr. 2021. doi: 10.1109/JAS.2021.1003934

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

doi: 10.1109/JAS.2021.1003934
Funds:  This work was supported in part by the National Natural Science Foundation of China (61673123, 61603100), and in part by the Natural Science Foundation of Guangdong Province, China (2020A151501482)
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  • The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

     

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  • [1]
    P. Zhang, and M. Zhou, “Dynamic cloud task scheduling based on a two-stage strategy,” IEEE Trans. Automation Science and Engineering, vol. 15, no. 2, pp. 772–783, Apr. 2018. doi: 10.1109/TASE.2017.2693688
    [2]
    J. Bi, H. Yuan, M. Zhou, and Q. Liu, “Time-dependent cloud workload forecasting via multi-task learning,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2401–2406, Jul. 2019. doi: 10.1109/LRA.2019.2899224
    [3]
    P. Zhang, S. Shu, and M. Zhou, “An online fault detection model and strategies based on SVM-grid in clouds,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 2, pp. 445–456, Mar. 2018. doi: 10.1109/JAS.2017.7510817
    [4]
    A. B. Patel, M. Birla, and U. Nair, “Addressing big data problem using hadoop and map reduce,” in Proc. Nirma University Int. Conf. on Engineering (NUiCONE), Ahmedabad, India, pp. 1–5, Dec. 2012.
    [5]
    M. H. Ghahramani, M. Zhou, and C. T. Hon, “Toward cloud computing QoS architecture: Analysis of cloud systems and cloud services,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 1, pp. 6–18, Jan. 2017. doi: 10.1109/JAS.2017.7510313
    [6]
    H. Yuan, J. Bi, B. H. Li, and W. Tan, “Cost-aware request routing in multi-geography cloud data centres using software-defined networking,” Enterprise Information Systems, vol. 11, no. 3, pp. 359–388, Mar. 2017. doi: 10.1080/17517575.2015.1048833
    [7]
    B. Lin, W. Guo, N. Xiong, G. Chen, A. V. Vasilakos, and H. Zhang, “A pretreatment workflow scheduling approach for big data applications in multicloud environments,” IEEE Trans. Network and Service Management, vol. 13, no. 3, pp. 581–594, Sept. 2016. doi: 10.1109/TNSM.2016.2554143
    [8]
    H. Yuan, J. Bi, and M. Zhou, “Spatial task scheduling for cost minimization in distributed green cloud data centers,” IEEE Trans. Automation Science and Engineering, vol. 16, no. 2, pp. 729–740, Apr. 2019. doi: 10.1109/TASE.2018.2857206
    [9]
    B. Hu, Z. Cao, and M. Zhou, “Scheduling real-time parallel applications in cloud to minimize energy consumption,” IEEE Trans. Cloud Computing, to be published, DOI: 10.1109/tcc.2019.2956498.
    [10]
    R. Kaur and J. Kaur, “Cloud computing security issues and its solution: A review,” in Proc. the 2nd Int. Conf. on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 1198–1200, May. 2015.
    [11]
    A. V. Dastjerdi and R. Buyya, “An autonomous reliability-aware negotiation strategy for cloud computing environments,” in Proc. the 12th IEEE/ACM Int. Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), Ottawa, ON, Canada, pp. 284–291, Jun. 2012.
    [12]
    S. K. Panda and P. K. Jana, “Efficient task scheduling algorithms for heterogeneous multi-cloud environment,” in Proc. the Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India, vol. 71, pp. 1204–1209, Sep. 2014.
    [13]
    Y. Liu, W. Jing, H. Shao, and Z. Qiu, “Multi-DAGs scheduling integrating with security and availability in cloud environment,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 709–716, Oct. 2015. doi: 10.1049/cje.2015.10.008
    [14]
    S. Ashish and C. Kakali, “Cloud security issues and challenges: A survey,” Journal of Network and Computer Applications, vol. 79, pp. 88–115, Feb. 2017. doi: 10.1016/j.jnca.2016.11.027
    [15]
    P. Zhang, M. Zhou, and Y. Kong, “A double-blind anonymous evaluation-based trust model in cloud computing environments,” IEEE Trans. Systems, Man, and Cybernetics: Systems, to be published, DOI: 10.1109/TSMC.2019.2906310.
    [16]
    A. B. M. B. Alam, M. Zulkernine, and A. Haque, “A reliability-based resource allocation approach for cloud computing,” in Proc. the 7th IEEE Int. Symposium on Cloud and Service Computing (SC2), Kanazawa, Japan, pp. 249–252, Nov. 2017.
    [17]
    L. Zhang, K. Li, Y. Xu, J. Mei, F. Zhang, and K. Li, “Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster,” Information Sciences, vol. 319, pp. 113–131, Oct. 2015. doi: 10.1016/j.ins.2015.02.023
    [18]
    S. Devipriya and C. Ramesh, “Improved Max-min heuristic model for task scheduling in cloud,” in Proc. Int. Conf. on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, India, pp. 883–888, Dec. 2013.
    [19]
    M. Derakhshan and Z. Bateni, “Optimization of tasks in cloud computing based on Max-Min, Min-Min and priority,” in Proc. the 4th Int. Conf. on Web Research (ICWR), pp. 45–50, Tehran, Iran, Apr. 2018.
    [20]
    H. Yu, S. Ruepp and M. S. Berger, “Enhanced first-in-first-out-based round-robin multicast scheduling algorithm for input-queued switches,” IET Communications, vol. 5, no. 8, pp. 1163–1171, May 2011. doi: 10.1049/iet-com.2010.0378
    [21]
    S. S. Rajput and V. S. Kushwah, “A genetic based improved load balanced min-min task scheduling algorithm for load balancing in cloud computing,” in Proc. the 8th Int. Conf. on Computational Intelligence and Communication Networks, Tehri, India, pp. 677–681, Dec. 2016.
    [22]
    P. Han, C. Du, and J. Chen, “A DEA based hybrid algorithm for bi-objective task scheduling in cloud computing,” in Proc. the 5th IEEE Int. Conf. on Cloud Computing and Intelligence Systems (CCIS), pp. 63–67, Nanjing, China, Nov. 2018.
    [23]
    Y. Fang, X. Xiao, and J. Ge, “Cloud computing task scheduling algorithm based on improved genetic algorithm,” in Proc. the 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conf. (ITNEC), Chengdu, China, pp. 852–856, Jun. 2019.
    [24]
    Y. Li, S. Wang, X. Hong, and Y. Li, “Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm,” in Proc. the 37th Chinese Control Conf. (CCC), pp. 4489–4494, Wuhan, China, Jul. 2018.
    [25]
    L. Liu, S. Gu, and Z. Qiu, “A survey on Cloudware and scheduling algorithm for Multi-Cloud,” in Proc. the 2nd IEEE Int. Conf. on Computer and Communications (ICCC), pp. 2722–2726, Chengdu, China, Oct. 2017.
    [26]
    J. Bi, H. Yuan, and M. Zhou, “Temporal prediction of multiapplication consolidated workloads in distributed clouds,” IEEE Trans. Automation Science and Engineering, vol. 16, no. 4, pp. 1763–1773, Oct. 2019. doi: 10.1109/TASE.2019.2895801
    [27]
    A. Taha, S. Manzoor, and N. Suri, “SLA-Based Service Selection for Multi-Cloud Environments,” in Proc. IEEE Int. Conf. on Edge Computing (EDGE), Honolulu, HI, USA, pp. 25–30, Jun. 2017.
    [28]
    B. Lin, W. Guo, G. Chen, N. Xiong, and R. Li, “Cost-driven scheduling for deadline-constrained workflow on multi-clouds,” in Proc. IEEE Int. Parallel and Distributed Processing Symposium Workshop, Hyderabad, India, pp. 1191–1198, May. 2015.
    [29]
    N. Sooezi, S. Abrishami, and M. Lotfian, “Scheduling data-driven workflows in multi-cloud environment,” in Proc. IEEE Int. Conf. on Cloud Computing Technology & Science, Vancouver, BC, Canada, pp. 163–167, Dec. 2015.
    [30]
    M. A. Rodriguez and R. Buyya, “Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds,” IEEE Trans. Cloud Computing, vol. 2, no. 2, pp. 222–235, Apr. 2014. doi: 10.1109/TCC.2014.2314655
    [31]
    H. Hu, Z. Li, H. Hu, J. Chen, J. Ge, C. Li, and V. Chang, “Multi-objective scheduling for scientific workflow in multicloud environment,” Journal of Network and Computer Applications, vol. 114, pp. 108–122, Apr. 2018. doi: 10.1016/j.jnca.2018.03.028
    [32]
    S. Kianpisheh, N. M. Charkari, and M. Kargahi, “Reliability-driven scheduling of time/cost-constrained grid workflows,” Future Generation Computer Systems, vol. 55, pp. 1–16, Feb. 2016. doi: 10.1016/j.future.2015.07.014
    [33]
    L. Zeng, B. Veeravalli, and X. Li, “SABA: A security-aware and budget-aware workflow scheduling strategy in clouds,” Journal of Parallel and Distributed Computing, vol. 75, pp. 141–151, Jan. 2015. doi: 10.1016/j.jpdc.2014.09.002
    [34]
    Z. Wen, J. Cała, P. Watson, and A. Romanovsky, “Cost effective- reliable and secure workflow deployment over federated clouds,” IEEE Trans. Services Computing, vol. 10, no. 6, pp. 929–941, Dec. 2017. doi: 10.1109/TSC.2016.2543719
    [35]
    Y. Dai, Y. Lou, and X. Lu, “A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing,” in Proc. Int. Conf. on Intelligent Human-machine Systems & Cybernetics, Hangzhou, China, pp. 428–431, Aug. 2015.
    [36]
    M. Zhang, L. Liu, and S. Liu, “Genetic algorithm based QoS-aware service composition in multi-cloud,” in Proc. IEEE Conf. on Collaboration and Internet Computing, pp. 113–118, Hangzhou, China, Oct. 2015.
    [37]
    N. Grozev and R. Buyya, “Inter-cloud architectures and application brokering: taxonomy and survey,” Software:Practice and Experience, vol. 44, no. 3, pp. 369–390, Feb. 2014. doi: 10.1002/spe.2168
    [38]
    L. Heilig, E. Lalla-Ruiz, and S. Voß, “A cloud brokerage approach for solving the resource management problem in multi-cloud environments,” Computers &Industrial Engineering, vol. 95, pp. 16–26, May 2016.
    [39]
    T. Halabi and M. Bellaiche, “Towards security-based formation of cloud federations: A game theoretical approach,” IEEE Trans. Cloud Computing, vol. 8, no. 3, pp. 928–942, Jul. 2020.
    [40]
    W. S N and W. E J, “An extended domain-based model of software reliability,” IEEE Trans. Software Engineering, vol. 14, no. 10, pp. 1512–1524, Oct. 1988. doi: 10.1109/32.6196
    [41]
    A. Chowdhury and P. Tripathi, “Enhancing cloud computing reliability using efficient scheduling by providing reliability as a service,” in Proc. Int. Conf. on Parallel, Distributed and Grid Computing (PDGC), pp. 99–104, Feb. 2014.
    [42]
    K. V. Vishwanath and N. Nagappan, “Characterizing cloud computing hardware reliability,” in Proc. the 1st ACM Symposium on Cloud Computing, pp. 193–204, Jan. 2010.
    [43]
    L. Wu, G. S. Kumar, S. Versteeg, and R. Buyya, “SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments,” IEEE Trans. Services Computing, vol. 7, no. 3, pp. 465–485, Nov. 2014. doi: 10.1109/TSC.2013.49
    [44]
    R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software:Practice and Experience, vol. 41, no. 1, pp. 23–50, Dec. 2011. doi: 10.1002/spe.995
    [45]
    O. H. Ibarra and C. E. Kim, “Heuristic algorithms for scheduling independent tasks on nonidentical processors,” Journal of the ACM (JACM), vol. 24, pp. 280–289, Apr. 1977.
    [46]
    X. He, X. Sun, and G. von Laszewski, “QoS guided min-min heuristic for grid task scheduling,” Journal of Computer Science and Technology, vol. 18, no. 4, pp. 442–451, Jul. 2003. doi: 10.1007/BF02948918
    [47]
    K. Etminani and M. Naghibzadeh, “A min-min max-min selective algorihtm for grid task scheduling,” in Proc. the 3rd IEEE/IFIP Int. Conf. in Central Asia on Internet, Tashkent, Uzbekistan, pp. 1–7, Sep. 2007.
    [48]
    J. Zhao, S. Liu, M. Zhou, X. Guo, and L. Qi, “An improved binary Cuckoo search algorithm for solving unit commitment problems: Methodological description,” IEEE Access, vol. 6, pp. 43535–43545, Aug. 2018. doi: 10.1109/ACCESS.2018.2861319
    [49]
    J. Zhao, S. Liu, M. Zhou, X. Guo, and L. Qi, “Modified cuckoo search algorithm to solve economic power dispatch optimization problems,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 4, pp. 794–806, Jul. 2018. doi: 10.1109/JAS.2018.7511138
    [50]
    H. Yuan, J. Bi, M. Zhou, and A. C. Ammari, “Time-aware multi-application task scheduling with guaranteed delay constraints in green data center,” IEEE Trans. Automation Science and Engineering, vol. 15, no. 3, pp. 1138–1151, July. 2018. doi: 10.1109/TASE.2017.2741965
    [51]
    H. Yuan, J. Bi, W. Tan, M. Zhou, B. H. Li, and J. Li, “TTSA: An effective scheduling approach for delay bounded tasks in hybrid clouds,” IEEE Trans. Cybernetics, vol. 47, no. 11, pp. 3658–3668, Nov. 2017. doi: 10.1109/TCYB.2016.2574766
    [52]
    D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, Aug. 2009. doi: 10.1016/j.amc.2009.03.090
    [53]
    G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, “Characterizing and profiling scientific workflows,” Future Generation Computer Systems, vol. 29, no. 3, pp. 682–692, Mar. 2013. doi: 10.1016/j.future.2012.08.015

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    Highlights

    • To guarantee security and reliability of applications in a multi-cloud environment.
    • Matching and Multi-round Allocation algorithm optimizes the makespan and total cost.
    • MMA algorithm covers: task-resource-matching and multiple rounds of resource reallocation.
    • MMA algorithm outperforms other benchmark algorithms.

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