A Proposal on Novel Cloud Scheduling Using Soft Computing Techniques
In recent years, facing information explosion, industry and academia have adopted distributed file system and to address new challenges the big data has brought. Based on these technologies, this research presents an OLAP system for workflow scheduling in cloud environment. The advanced development in virtualization technologies and cloud computing servers the best way for distributing computing resources for existing resource pools based on demand and scientific computing. With the development of information technology, a large volume of data is growing and getting stored electronically in cloud platform . As workload characteristics and requirements evolve, database engines need to efficiently handle both transactional (OLTP) and analytical (OLAP) workloads with strong guarantees for throughput, latency and data freshness. Cloud computing is a new paradigm for distributed computing that delivers infrastructure, platform and software (application) as services and made available as subscription-based services in a pay-as-you-go model to consumers. Cloud computing offers a wide range of computation and resource facilities for execution of workflow applications. Many resources are involved in execution of single workflow.
Abdi, S., Motamedi, S.A. and Sharifian, S., 2014, January. Task scheduling using Modified PSO Algorithm in cloud computing environment. In International conference on machine learning, electrical and mechanical engineering (pp. 8-9).
Bala, Anju, and Inderveer Chana. "A survey of various workflow scheduling algorithms in cloud environment." In 2nd National Conference on Information and Communication Technology (NCICT), pp. 26-30. 2011.
Bardsiri, A.K. and Hashemi, S.M., 2012. A review of workflow scheduling in cloud computing environment. International Journal of Computer Science and Management Research, 1(3), pp.348-351
Bessai, Kahina, Samir Youcef, Ammar Oulamara, Claude Godart, and Selmin Nurcan. "Bi-criteria workflow tasks allocation and scheduling in Cloud computing environments." In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pp. 638-645. IEEE, 2012.
Chen, Chiu-Hung, Tung-Kuan Liu, and Jyh-Horng Chou. "A Novel Crowding Genetic Algorithm and Its Applications to Manufacturing Robots." Industrial Informatics, IEEE Transactions on 10, no. 3 (2014): 1705-1716.
Durillo, J.J. and Prodan, R., 2014. Multi-objective workflow scheduling in Amazon EC2. Cluster computing, 17(2), pp.169-189.
Fard, Hamid Mohammadi, Radu Prodan, Juan Jose Durillo Barrionuevo, and Thomas Fahringer. "A multi-objective approach for workflow scheduling in heterogeneous environments." In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp. 300-309. IEEE Computer Society, 2012.
Komaki GM, Kayvanfar V. Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. Journal of Computational Science. 2015 May 31;8:109-20.
Li, Z., Wang, C., Lv, H. and Song, X., 2014. Scheduling Tasks on Heterogeneous Multi-Core Processors Based on Modified Ant Colony Optimization. International Journal of Control and Automation, 7(9), pp.345-356.
Lin, Cui, and Shiyong Lu. "Scheduling scientific workflows elastically for cloud computing." In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pp. 746-747. IEEE, 2011.
Mirjalili, S. and Lewis, A., 2016. The whale optimization algorithm. Advances in Engineering Software, 95, pp.51-67.
Oliva Diego, Mohamed Abd El Aziz, Aboul Ella Hassanien, Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm, In Applied Energy, Volume 200, 2017, Pages 141-154.
Nzanywayingoma, F. and Yang, Y., 2017. Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment. Analysis, 8(1).
Qing, Ling, Wu Gang, Yang Zaiyue, and Wang Qiuping. "Crowding clustering genetic algorithm for multimodal function optimization." Applied Soft Computing 8, no. 1 (2008): 88-95.
Qu, Bo-Yang, Jing J. Liang, and Ponnuthurai N. Suganthan. "Niching particle swarm optimization with local search for multi-modal optimization."Information Sciences 197 (2012): 131-143.
Rahman, M., Hassan, R., Ranjan, R. and Buyya, R., 2013. Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience, 25(13), pp.1816-1842.
Singh, R. and Singh, S., 2013. Score based deadline constrained workflow scheduling algorithm for Cloud systems. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 3(6).
Wang, Xiaofeng, Chee Shin Yeo, Rajkumar Buyya, and Jinshu Su. "Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm." Future Generation Computer Systems 27, no. 8 (2011): 1124-1134.
Xue, Sheng-Jun, and Wu Wu. "Scheduling workflow in cloud computing based on hybrid particle swarm algorithm." TELKOMNIKA Indonesian Journal of Electrical Engineering 10, no. 7 (2012): 1560-1566.
Ye, Feng, Weimin Qi, and Jie Xiao. "Research of Niching Genetic Algorithms for Optimization in Electromagnetics." Procedia engineering 16 (2011): 383-389.
Zhan, S. and Huo, H., 2012. Improved PSO-based task scheduling algorithm in cloud computing. Journal of Information & Computational Science, 9(13), pp.3821-3829.
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