Cloud computing is one of the promising domains that has gained popularity in the recent years. It offers utility-oriented IT services to the users worldwide over the internet. In cloud, service providers manage and provide resources to users as per they use. Software or hardware can be used as per requirement; there is no need to buy them. Key role in cloud computing systems is managing different tasks. WFaaS provides a way to compose multiple software services/packages based on certain logic within a workflow service. In workflows completion of whole task applications require various sub-tasks which are executed in a particular manner. It facilitates a service and management environment for flexible application integration via workflows. In this paper some Workflow scheduling algorithms are also included, which are the most important part of cloud computing for workflow. This review paper describes cloud computing, basics of workflows and scheduling, some scheduling algorithms used in workflow management, factors considered by these algorithms, type of algorithm and tool used.

Cloud computing, Public, Private, Hybrid, IaaS, PaaS, SaaS, WFaaS, Scheduling algorithm

[1] S.M. Hashemi, A.Kh. Bardsiri, “Cloud computing vs. grid computing,” ARPN journal of systems and software, vol. 2, No 5, pp. 188-194, May 2012.
[2] H. Alhakami, H. Aldabbas, T. Alwada, "Comparison between cloud and grid computing : review paper," International journal on cloud computing: services and architecture (IJCCSA), vol. 2, No. 4, pp. 1-21, August 2012.
[3] http://aws.amazon.com/ec2.
[4] Agarwal N. “Role of Cloud Computing in Development of Smart City” in International Journal of Science, Technology and Engineering (IJSTE), ISSN (online): 2349-784X, 228-232, 2017.
[5] Agarwal N. “Database Management on Clouds through NoSQL” in Aishwarya Research Review, ISSN 2249–2097, 55-63, 2015.
[6] Agarwal N. “Advantages and Uses of Cloud Computing in Business” published in Aishwarya Research Communication, ISSN 0975-3613, Vol.5, 37-41,2013.
[7] M. Shiraz, A. Gani, R. H. Khokhar, R. Buyya, "A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing," IEEE communications surveys & tutorials, vol. 15, no. 3, pp. 1294-1313, 2013.
[8] R. Sakellariou, H. Zhao, “A hybrid heuristic for DAG scheduling on heterogeneous systems”.
[9] Niu S, Zhai J, Ma X, Tang X, Chen W. Cost-effective cloud HPC resource provisioning by building semi-elastic virtual clusters. Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis; ACM; 2013. Article No. 56.
[10] Carrington L, Snavely A, Wolter N. A performance prediction framework for scientific applications. Future Generation Computer Systems. 2006;22(3):336–346.
[11] Zhao Y, Fei X, Raicu I, Lu S. Opportunities and Challenges in Running Scientific Workflows on the Cloud. Proceedings of IEEE International Conference on Cyber-enabled distributed computing and knowledge discovery (CyberC); 2011. pp. 455–462.
[12] 18th international parallel and distributed processing symposium, 2004.
[13] Radulescu, A. Gemund, “Fast and effective task scheduling in heterogeneous systems,” Proceedings of the 9th heterogeneous computing workshop (HCW 2000), pp. 229-238, 2000.
[14] Y. K. Kwok, I. Ahmad, “Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors,” IEEE transactions on parallel and distributed systems, vol. 7, no. 5, pp. 506-521, May 1996.
[15] G.C. Sih, E.A. Lee, “A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures,” IEEE transactions on parallel and distributed systems, vol. 4, no. 2, pp. 175-187, February 1993.
[16] H. Zhao, R. Sakellarious, “Scheduling multiple DAGs onto heterogeneous systems,” IEEE 20th international parallel and distributed processing symposium,2006.
[17] Z. Yu, W. Shi, “A planner-guided scheduling strategy for multiple workflow applications,” international conference on parallel processing - IEEE workshop, pp. 1-8, 2008.
[18] S. Pandey, L. Wu, S. Mayura Guru, R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments,” 24th IEEE international conference on advanced information networking and applications, PP 400-407, 2010.
[19] T. A. L. Genez, L. F. Bittencourt, E. R. M. Madeira, “Workflow scheduling for saas / paas cloud providers considering two SLA levels,” IEEE network operations and management symposium (NOMS): mini-conference, pp. 906-912, 2012.
[20] C. Lin, S. Lu, “Scheduling scientific workflows elastically for cloud computing,” IEEE 4th international conference on cloud computing, pp. 246-247, 2011.
[21] H. Zhong, K. Tao, X. Zhang, “An approach to optimized resource scheduling algorithm for open-source cloud systems,” Fifth annual china grid conference
[22] (IEEE), pp. 124-129, 2010.
[23] M. Xu, L. Cui, H. Wang, Y. Bi, “A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing,” IEEE international symposium on parallel and distributed processing with applications, pp. 629-634, 2009.
[24] Verma, S. Kaushal, “Deadline and budget distribution based cost- time optimization workflow scheduling algorithm for cloud,” International conference on recent advances and future trends in information technology (iRAFIT 2012).