Abstract
The security of workflow scheduling is a significant concern and even is one of the most important metrics of QoS (Quality of Service). This paper presents two approaches to provide a secure connection between users and servers and handle large and medium task size problems. Firstly, a multi-objective scheduling (MO-Ring-IC-NCCLA) algorithm for scientific workflow in the cloud environment is proposed. It tries to minimize workflow makespan and cost as well as increase the cost of attack from an invader. The proposed multi-objective is based on the New Caledonian Crow Learning Algorithm (NCCLA). However, this algorithm has a few drawbacks, including poor exploration activity and inability to balance exploration and exploitation. The social and asocial learning part of standard NCCLA has been modified to tackle these limitations, then a concept of ring topology is used to better Pareto optimal can be found. Secondly, the structure of virtual machines is modified so that the cost of attack from invaders increases. Experimental results based on various real-world workflows indicate the performance improvement of MO-Ring-IC-NCCLA over SBDE, NSGA-II, and MOHFHB algorithms in terms of FS-metric. According to the delta metric (i.e., diversity measures), the proposed algorithm is superior to 85% of the compared metaheuristics. In terms of Inverted Generational Distance (IGD) metric, it outperforms NSGAII and Multi-Objective Artificial Hummingbird Algorithm (MOAHA) for 95% and 80% of the cases, respectively. Based on experiments, makespan and cost improved by 23.12% and 18.43% over existing workflow algorithms. Compared to Multi-Objective Hybrid Fuzzy Hitchcock Bird (MOHFHB), Simulated-annealing Based Differential Evolution (SBDE), and non-dominated sorting genetic algorithm (NSGAII), it improves the FS-metric by 23.35% on average.
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Data availability
The datasets generated and/or analysed during the current study are not publicly available due but are available from the corresponding author on reasonable request.
References
Mohammad Hasani Zade B, Mansouri N, Javidi MM (2022) A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment. J Netw Comput Appl 202:103385. https://doi.org/10.1016/j.jnca.2022.103385
Khaledian N, Khamforoosh K, Azizi S, Maihami V (2022) IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment. Sustainable Computing: Informatics and Systems 37:20223. https://doi.org/10.1016/j.suscom.2022.100834
Atya AOF, Qian Z, Krishnamurthy SV, Porta TL McDaniel P, Marvel L (2017) Malicious co-residency on the cloud: Attacks and defense. In IEEE INFOCOM 2017-IEEE Conference on Computer Communications. USA 1–9. https://doi.org/10.1109/INFOCOM.2017.8056951
Zhang Y, Juels A, Reiter MK, Ristenpart T (2014) Cross-tenant side-channel attacks in PaaS clouds. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, ACM 990–1003. https://doi.org/10.1145/2660267.2660356
Wang Z, Wu J, Guo Z, Cheng G, Hu H (2016) Secure virtual network embedding to mitigate the risk of covert channel attacks. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), San Francisco, CA pp. 144–145. https://doi.org/10.1109/INFCOMW.2016.7562061
Wu J, Lei Z, Chen S, Shen W (2017) An access control model for preventing virtual machine escape attack. Future Internet 9(2):1–19. https://doi.org/10.3390/fi9020020
Zeng L, Veeravallia B, Li X (2015) SABA: A security-aware and budget-aware workflow scheduling strategy in clouds. Journal of Parallel and Distributed Computing 75:141–151. https://doi.org/10.1016/j.jpdc.2014.09.002
Wang ZJ, Zhan ZH, Yu WJ, Lin Y, Zhang J, Gu TL, Zhang J (2019) Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling. IEEE transactions on cybernetics 50(6):2715–2729. https://doi.org/10.1109/TCYB.2019.2933499
Kishor A, Niyogi R, Veeravalli B (2020) A game-theoretic approach for cost-aware load balancing in distributed systems. Futur Gener Comput Syst 109(2):29–44. https://doi.org/10.1016/j.future.2020.03.027
Singh P, Dutta M, Aggarwal N (2017) A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl Inf Syst 52:1–51. https://doi.org/10.1007/s10115-017-1044-2
Kumar M, Kishor A, Abawajy J, Agarwal P, Singh A, Zomaya A (2021) ARPS: An Autonomic Resource Provisioning and Scheduling framework for cloud platforms. IEEE Transactions on Sustainable Computing 7(2):386–399. https://doi.org/10.1109/TSUSC.2021.3110245
Yuan H, Bi J, Zhou M, Liu Q, Ammari AC (2021) Biobjective task scheduling for distributed green data centers. IEEE Trans Autom Sci Eng 18(2):731–742. https://doi.org/10.1109/TASE.2019.2958979
Xue S, Shi W, Xu X (2016) A heuristic scheduling algorithm based on PSO in the cloud computing environment. International Journal of u- and e- Service, Science and Technology 9(1):349–362. https://doi.org/10.14257/IJUNESST.2016.9.1.36
Topcuoglu H, Hariri S, Wu M (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274. https://doi.org/10.1109/71.993206
Saeedi S, Khorsand R, Bidgoli SG, Ramezanpour M (2020) Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing.Comput Indust Eng 147:106649. https://doi.org/10.1016/j.cie.2020.106649
Khan SA, Mahmood A (2019) Fuzzy goal programming-based ant colony optimization algorithm for multi-objective topology design of distributed local area networks. Neural Comput Appl 31(7):2329–2347. https://doi.org/10.1007/s00521-017-3191-5
Mohammad Hasani Zade B, Mansouri N, Javidi MM (2021) Multi-objective scheduling technique based on hybrid hitchcock bird algorithm and fuzzy signature in cloud computing.Eng Appl Art Intell 104:104372. https://doi.org/10.1016/j.engappai.2021.104372
Durillo JJ, Prodan R (2014) Multi-objective workflow scheduling in Amazon EC2. Clust Comput 17(2):169–189. https://doi.org/10.1007/s10586-013-0325-0
Wang B, Wang C, Huang W, Song Y, Qin X (2021) Security-aware task scheduling with deadline constraints on heterogeneous hybrid clouds. Journal of Parallel and Distributed Computing 153:15–28. https://doi.org/10.1016/j.jpdc.2021.03.003
Wu X, Wang S, Pan Y, Shao H (2021) A knee point-driven multi-objective artificial flora optimization algorithm. Wireless Netw 27(5):3573–3583. https://doi.org/10.1007/s11276-019-02228-8
Wang Y, Guo Y, Guo Z, Baker T, Liu W (2020) CLOSURE: A cloud scientific workflow scheduling algorithm based on attack–defense game model. Futur Gener Comput Syst 111:460–474. https://doi.org/10.1016/j.future.2019.11.003
Wu Q, Zhou MC, Zhu Q, Xia Y, Wen J (2019) MOELS: Multiobjective evolutionary list scheduling for cloud workflows. IEEE Trans Autom Sci Eng 17(1):166–176. https://doi.org/10.1109/TASE.2019.2918691
Visheratin A, Melnik M, Butakov N, Nasonov D (2015) Hard-deadline constrained workflows scheduling using metaheuristic algorithms. In 4th International Young Scientists Conference on Computational Science. 66:506–514. https://doi.org/10.1016/j.procs.2016.05.529
Meng S, Huang W, Yin X, Khosravi MR, Li Q, Wan S, Qi L (2020) Security-aware dynamic scheduling for real-time optimization in cloud-based industrial applications. IEEE Trans Industr Inf 17(6):4219–4228. https://doi.org/10.1109/TII.2020.2995348
Domanal S, Guddeti RM, Buyya R (2017) A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment. IEEE Trans Serv Comput 13(1):3–15. https://doi.org/10.1109/TSC.2017.2679738
Versluis L, Iosup A (2021) A survey of domains in workflow scheduling in computing infrastructures: Community and keyword analysis, emerging trends, and taxonomies. Futur Gener Comput Syst 123:156–177. https://doi.org/10.1016/j.future.2021.04.009
Lei J, Wu Q, Xu J (2022) Privacy and security-aware workflow scheduling in a hybrid cloud. Futur Gener Comput Syst 131:269–278. https://doi.org/10.1016/j.future.2022.01.018
Kakkottakath Valappil Thekkepurayil J, Peter Suseelan D, Mathew Keerikkattil P (2022) Multi-objective scheduling policy for workflow applications in cloud using hybrid particle search and rescue algorithm. Service Oriented Computing and Applications 16:45–65. https://doi.org/10.1007/s11761-021-00330
Shishido HY, Cezar Estrella CF, Motta Toledo J (2018) Multi-objective optimization for workflow scheduling under task selection policies in clouds. IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2018.8477799
Al-Sorori W, Mohsen AM (2020) New Caledonian crow learning algorithm: A new metaheuristic algorithm for solving continuous optimization problems.Appl Soft Comput 92:106325. https://doi.org/10.1016/j.asoc.2020.106325
Mirjalili S, Saremi S, Mirjalili SM, Coelho LS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/j.eswa.2015.10.039
Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows. In IEEE Workshop on Workflows in Support of Large-Scale Science, Austin, USA 1–10. https://doi.org/10.1109/WORKS.2008.4723958
Wang Y, Guo Y, Guo Z, Liu W, Yang C (2019) Securing the intermediate data of scientific workflows in clouds with ACISO. IEEE Access 7(1):126603–126617. https://doi.org/10.1109/ACCESS.2019.2938823
Del Piccolo V, Amamou A, Haddadou K, Pujolle G (2016) A survey of network isolation solutions for multi-tenant data centers. IEEE Communications Surveys & Tutorials 18(4):2787–2821. https://doi.org/10.1109/COMST.2016.2556979
Sayed GI, Darwish A, Hassanien AE (2018) A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. J Exp Theor Artif Intell 30(2):293–317. https://doi.org/10.1080/0952813X.2018.1430858
Choi TJ, Wook Ahn C, An J (2013) An adaptive Cauchy differential evolution algorithm for global numerical optimization. Sci World J. https://doi.org/10.1155/2013/969734
Wang GG, Deb S, Dos Santos Coelho L (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems.International journal of bio-inspired computation. 12(1):1–22. https://doi.org/10.1504/ijbic.2015.10004283
Abdel-Basset M, Chang V, Mohamed R (2020) HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images.Appl Soft Comput 95:106642. https://doi.org/10.1016/j.asoc.2020.106642
Xiujuan L, Zhongke S (2004) Overview of multi-objective optimization methods. J Syst Eng Electron 15(2):142–146. https://doi.org/10.1080/23311916.2018.1502242
Jin Y, Sendhoff B (2008) Pareto-based multiobjective machine learning: An overview and case studies.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 38(3):397–415. https://doi.org/10.1109/TSMCC.2008.919172
Gao Y, Zhang S, Zhou J (2019) A hybrid algorithm for multi-objective scientific workflow scheduling in IaaS Cloud. IEEE Access 7:125783–125795. https://doi.org/10.1109/ACCESS.2019.2939294
Yue C, Boyang Q, Liang J (2017) A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evol Comput 22(5):805–817. https://doi.org/10.1109/TEVC.2017.2754271
Kaur S, Awasthi LK, Sangal AL (2021) HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems. Engineering with Computers 37(4):3167–3203. https://doi.org/10.1007/s00366-020-00989-x
Yusoff Y, Ngadiman MS, Zain AM (2011) Overview of NSGA-II for optimizing machining process parameters. Procedia Engineering 15:3978–3983. https://doi.org/10.1016/j.proeng.2011.08.745
Liu Z, Jiang P, Wang J, Zhang L (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Exp Syst Appl 177:114974. https://doi.org/10.1016/j.eswa.2021.114974
Khodadadi N, Mirjalili SM, Zhao W, Zhang Z, Wang L, Mirjalili S (2022) Multi-Objective Artificial Hummingbird Algorithm. In Advances in Swarm Intelligence: Variations and Adaptations for Optimization Problems pp. 407–419. https://doi.org/10.1007/978-3-031-09835-2_22
Kumar S, Jangir Ghanshyam P, Tejani G, Premkumar M (2022) MOTEO: A novel physics-based multiobjective thermal exchange optimization algorithm to design truss structures. Knowledge-Based Systems 242:108422. https://doi.org/10.1016/j.knosys.2022.108422
Premkumar M, Jangir P, Sowmya R, Haes Alhelou H, Heidari AA, Chen H (2020) MOSMA: Multi-objective slime mould algorithm based on elitist non-dominated sorting,"IEEE Access 9:3229–3248. https://doi.org/10.1109/ACCESS.2020.3047936.
Zouache D, Ould Arby Y, Nouioua F, Ben Abdelaziz F (2019) Multi-objective chicken swarm optimization: a novel algorithm for solving multi-objective optimization problems.Comput Indust Eng 129:377–391.https://doi.org/10.1109/ACCESS.2020.3047936
Ahmed AM, Tarik AR, Soran ABM, Noori KA, Hassan BA, Rahman CM, Ahmed OH, Umar SU, Mundher Yaseen Z (2023) GMOCSO: Multi-objective Cat Swarm Optimization Algorithm based on a Grid System. https://doi.org/10.21203/rs.3.rs-2882076/v1
Hu H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122. https://doi.org/10.1016/j.jnca.2018.03.028
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Behnam Mohammad Hasani Zade: Programming, software development, Ideas Najme Mansouri: Development or design of methodology; creation of models, testing of existing code components, Writing- Original draft preparation. Mohammad Masoud Javidi: Investigation, Verification, Writing- Reviewing and Editing.
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Zade, B.M.H., Javidi, M. & Mansouri, N. An improved Caledonian crow learning algorithm based on ring topology for security-aware workflow scheduling in cloud computing. Peer-to-Peer Netw. Appl. 16, 2929–2984 (2023). https://doi.org/10.1007/s12083-023-01541-6
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DOI: https://doi.org/10.1007/s12083-023-01541-6