Abstract
Distributed computing workflow is an effective paradigm to express a range of applications with cloud computing platforms for scientific research explorations. One of the most difficult application areas of cloud computing technology is task scheduling. In a cloud, heterogeneous context, job scheduling with minimal execution cost and time, as well as workflow reliability, are critical. While working in the heterogeneous cloud environment, tasks that are successfully executed are widely identified by considering the failure of the processor or any communication technologies link. It will also have an impact on the workflow's reliability as well as the user's service quality expectations. This research paper proposes a Critical Parent Reliability-based Scheduling (CPRS) method that uses the reliability parameter to plan the task while taking into account the user-defined cost and deadline metrics. The effectiveness of the algorithm is compared to current algorithms utilizing scientific workflows as a benchmark, such as Cybershake, Sipht, and Montage. The simulation results supported the assertions by efficiently allocating resources to the cloudlets and stabilizing all of the aforementioned parameters using sufficient performance metrics growth.
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References
Bawa, R. K., & Sharma, G. (2012). Reliable resource selection in grid environment. ar**v preprint ar**v:1204.1516
Badotra, S., & Panda, S. N. (2022). Software defined networking: a crucial approach for cloud computing adoption. International Journal of Cloud Computing, 11(2), 123–137. https://doi.org/10.1504/IJCC.2022.122028
Faragardi, H. R., Shojaee, R., & Yazdani, N. (2012, June). Reliability-aware task allocation in distributed computing systems using hybrid simulated annealing and tabu search. In 2012 IEEE 14th international conference on high performance computing and communication & 2012 IEEE 9th international conference on embedded software and systems (pp. 1088–1095). IEEE. https://doi.org/10.1109/HPCC.2012.159
Shojaee, R., Faragardi, H. R., Alaee, S., & Yazdani, N. (2012, November). A new cat swarm optimization based algorithm for reliability-oriented task allocation in distributed systems. In 6th international symposium on telecommunications (IST) (pp. 861–866). IEEE. https://doi.org/10.1109/ISTEL.2012.6483106
Sahoo, S., Sahoo, B., Turuk, A. K., & Mishra, S. K. (2017). Real time task execution in cloud using mapreduce framework. In Resource management and efficiency in cloud computing environments (pp. 190–209). IGI Global. https://doi.org/10.4018/978-1-5225-1721-4.ch008
Olakanmi, O. O., & Dada, A. (2019). An efficient privacy-preserving approach for secure verifiable outsourced computing on untrusted platforms. International Journal of Cloud Applications and Computing, 9(2), 79–98. https://doi.org/10.4018/IJCAC.2019040105
Rani, M., Guleria, K., & Panda, S. N. (2021, September). Cloud Computing An Empowering Technology: Architecture, Applications and Challenges. 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1–6). IEEE. https://ieeexplore.ieee.org/abstract/document/9596259
Daoud, M. I., & Kharma, N. (2008). A high performance algorithm for static task scheduling in heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 68(4), 399–409. https://doi.org/10.1016/j.jpdc.2007.05.015
Zhou, A., Wang, S., Cheng, B., Zheng, Z., Yang, F., Chang, R. N., Lyu, M. R., & Buyya, R. (2016). Cloud service reliability enhancement via virtual machine placement optimization. IEEE Transactions on Services Computing, 10(6), 902–913. https://doi.org/10.1109/TSC.2016.2519898
Zhao, L., Ren, Y., & Sakurai, K. (2013). Reliable workflow scheduling with less resource redundancy. Parallel Computing, 39(10), 567–585. https://doi.org/10.1016/j.parco.2013.06.003
Qiu, W., Zheng, Z., Wang, X., Yang, X., & Lyu, M. R. (2013). Reliability-based design optimization for cloud migration. IEEE Transactions on Services Computing, 7(2), 223–236. https://doi.org/10.1109/TSC.2013.38
Silic, M., Delac, G., & Srbljic, S. (2014). Prediction of atomic web services reliability for QoS-aware recommendation. IEEE Transactions on services Computing, 8(3), 425–438. https://doi.org/10.1109/TSC.2014.2346492
Dongarra, J. J., Jeannot, E., Saule, E., & Shi, Z. (2007, June). Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems. In Proceedings of the nineteenth annual ACM symposium on parallel algorithms and architectures (pp. 280–288). https://doi.org/10.1145/1248377.1248423
Zheng, Q., & Veeravalli, B. (2009). On the design of communication-aware fault-tolerant scheduling algorithms for precedence constrained tasks in grid computing systems with dedicated communication devices. Journal of Parallel and Distributed Computing, 69(3), 282–294. https://doi.org/10.1016/j.jpdc.2008.11.007
Yu, J., Buyya, R., & Ramamohanarao, K. (2008). Workflow scheduling algorithms for grid computing. In Metaheuristics for scheduling in distributed computing environments (pp. 173–214). Springer. https://doi.org/10.1007/978-3-540-69277-5_7
Yu, J., & Buyya, R. (2005). A taxonomy of workflow management systems for grid computing. Journal of Grid Computing, 3(3), 171–200. https://doi.org/10.1007/s10723-005-9010-8
Arabnejad, H., & Barbosa, J. G. (2014). A budget constrained scheduling algorithm for workflow applications. Journal of Grid Computing, 12(4), 665–679. https://doi.org/10.1007/s10723-014-9294-7
Sakellariou, R., Zhao, H., Tsiakkouri, E., & Dikaiakos, M. D. (2007). Scheduling workflows with budget constraints. In Integrated research in GRID computing (pp. 189–202). Springer. https://doi.org/10.1007/978-0-387-47658-2_14
Miglani, N., & Sharma, G. (2018). An adaptive load balancing algorithm using categorization of tasks on virtual machine based upon queuing policy in cloud environment. International Journal of Grid and Distributed Computing, 11(11), 1–2. https://doi.org/10.14257/ijgdc.2018.11.11.01
Su, S., Li, J., Huang, Q., Huang, X., Shuang, K., & Wang, J. (2013). Cost-efficient task scheduling for executing large programs in the cloud. Parallel Computing, 39(4–5), 177–188. https://doi.org/10.1016/j.parco.2013.03.002
Mousavi Nik, S. S., Naghibzadeh, M., & Sedaghat, Y. (2020). Cost-driven workflow scheduling on the cloud with deadline and reliability constraints. Computing, 102(2), 477–500. https://doi.org/10.1007/s00607-019-00740-5
Kianpisheh, S., & Moghadam Charkari, N. (2014). A grid workflow Quality-of-Service estimation based on resource availability prediction. The Journal of Supercomputing, 67(2), 496–527. https://doi.org/10.1007/s11227-013-1014-8
Khurana, S., & Singh, R. K. (2018, September). Virtual machine categorization and enhance task scheduling framework in cloud environment. In 2018 international conference on computing, power and communication technologies (GUCON) (pp. 391–394). IEEE. https://doi.org/10.1109/GUCON.2018.8675020
**e, G., Zeng, G., Chen, Y., Bai, Y., Zhou, Z., Li, R., & Li, K. (2017). Minimizing redundancy to satisfy reliability requirement for a parallel application on heterogeneous service-oriented systems. IEEE Transactions on Services Computing, 13(5), 871–886. https://doi.org/10.1109/TSC.2017.2665552
Zhao, L., Ren, Y., & Sakurai, K. (2011, March). A resource minimizing scheduling algorithm with ensuring the deadline and reliability in heterogeneous systems. In 2011 IEEE international conference on advanced information networking and applications (pp. 275–282). IEEE. https://doi.org/10.1109/AINA.2011.87
Qin, X., Jiang, H., & Swanson, D. R. (2002, August). An efficient fault-tolerant scheduling algorithm for real-time tasks with precedence constraints in heterogeneous systems. In Proceedings international conference on parallel processing (pp. 360–368). IEEE. https://doi.org/10.1109/ICPP.2002.1040892
Boeres, C., Sardiña, I. M., & Drummond, L. M. (2011). An efficient weighted bi-objective scheduling algorithm for heterogeneous systems. Parallel Computing, 37(8), 349–364. https://doi.org/10.1016/j.parco.2010.10.003
Narendrababu Reddy, G., & Phani Kumar, S. (2019). Regressive whale optimization for workflow scheduling in cloud computing. International Journal of Computational Intelligence and Applications, 18(04), 1950024. https://doi.org/10.1142/S146902681950024X
Chakravarthi, K. K., Shyamala, L., & Vaidehi, V. (2021). Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm. Applied Intelligence, 51(3), 1629–1644. https://doi.org/10.1007/s10489-020-01875-1
Zhang, L., Zhou, L., & Salah, A. (2020). Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments. Information Sciences, 531, 31–46. https://doi.org/10.1016/j.ins.2020.04.039
Iranmanesh, A., & Naji, H. R. (2021). DCHG-TS: A deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Cluster Computing, 24(2), 667–681. https://doi.org/10.1007/s10586-020-03145-8
Yuan, H., Liu, H., Bi, J., & Zhou, M. (2020). Revenue and energy cost-optimized biobjective task scheduling for green cloud data centers. IEEE Transactions on Automation Science and Engineering, 18(2), 817–830. https://doi.org/10.1109/TASE.2020.2971512
Pham, T. P., Durillo, J. J., & Fahringer, T. (2017). Predicting workflow task execution time in the cloud using a two-stage machine learning approach. IEEE Transactions on Cloud Computing, 8(1), 256–268. https://doi.org/10.1109/TCC.2017.2732344
Arabnejad, H., & Barbosa, J. G. (2013). List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems, 25(3), 682–694. https://doi.org/10.1109/TPDS.2013.57
Khurana, S., & Singh, R. (2020). Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking. EAI Endorsed Transactions on Scalable Information Systems. https://doi.org/10.4108/eai.13-7-2018.161408
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SK conceived the idea, designed the experiments and analysed the data; GS performed the experiments and conducted the analysis; MK and NG analysed the methods, interpreted the results and drew the conclusions; BS proofread the paper. All the authors agree with the above contribution details.
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Khurana, S., Sharma, G., Kumar, M. et al. Reliability Based Workflow Scheduling on Cloud Computing with Deadline Constraint. Wireless Pers Commun 130, 1417–1434 (2023). https://doi.org/10.1007/s11277-023-10337-z
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DOI: https://doi.org/10.1007/s11277-023-10337-z