Serverless Computing and Scheduling Tasks on Cloud: A Review
Keywords:
FaaS Clouds, Serverless Computing, Scheduling, Workflows, Cloud Computing, Scheduling Algorithms.Abstract
Recently, the emergence of Function-as-a-Service (FaaS) has gained increasing attention by researchers. FaaS, also known as serverless computing, is a new concept in cloud computing that allows the services computation that triggers the code execution as a response for certain events. In this paper, we discuss various proposals related to scheduling tasks in clouds. These proposals are categorized according to their objective functions, namely minimizing execution time, minimizing execution cost, or multi objectives (time and cost). The dependency relationships between the tasks plays a vital role in determining the efficiency of the scheduling approach. This dependency may result in resources underutilization. FaaS is expected to have a significant impact on the process of scheduling tasks. This problem can be reduced by adopting a hybrid approach that combines both the benefit of FaaS and Infrastructure-as-a-Service (IaaS). Using FaaS, we can run the small tasks remotely and focus only on scheduling the large tasks. This helps in increasing the utilization of the resources because the small tasks will not be considered during the process of scheduling. An extension of the restricted time limit by cloud vendors will allow running the complete workflow using the serverless architecture, avoiding the scheduling problem.
References
[1] S. Abrishami, M. Naghibzadeh, and D. H. J. Epema, “Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds,” Futur. Gener. Comput. Syst., vol. 29, no. 1, pp. 158–169, 2013.
[2] L. K. Arya and A. Verma, “Workflow scheduling algorithms in cloud environment - A survey,” 2014 Recent Adv. Eng. Comput. Sci. RAECS 2014, no. April, 2014.
[3] A. Bardsiri and S. Hashemi, “A Review of Workflow Scheduling in Cloud Computing Environment,” Int. J. Comput. Sci. Manag. Res., vol. 1, no. 3, pp. 348–351, 2012.
[4] M. A. Rodriguez and R. Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds,” IEEE Trans. Cloud Comput., vol. 2, no. 2, pp. 222–235, 2014.
[5] C. Q. Wu, X. Lin, D. Yu, W. Xu, and L. Li, “End-to-end delay minimization for scientific workflows in clouds under budget constraint,” IEEE Trans. Cloud Comput., vol. 3, no. 2, pp. 169–181, 2015.
[6] K. Almi’Ani and Y. C. Lee, “Partitioning-based workflow scheduling in clouds,” Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, vol. 2016–May, pp. 645–652, 2016.
[7] T. Ryan and Y. C. Lee, “Effective Resource Multiplexing for Scientific Workflows,” 2015.
[8] M. Mao and M. Humphrey, “Auto-scaling to minimize cost and meet application deadlines in cloud workflows,” Proc. 2011 Int. Conf. High Perform. Comput. Networking, Storage Anal. - SC ’11, p. 1, 2011.
[9] Y. C. Lee and B. Lian, “Cloud Bursting Scheduler for Cost Efficiency,” 2017 IEEE 10th Int. Conf. Cloud Comput., pp. 774–777, 2017.
[10] Y. C. Lee, Y. Kim, H. Han, and S. Kang, “Fine-Grained, Adaptive Resource Sharing for Real Pay-Per-Use Pricing in Clouds,” Proc. - 2015 Int. Conf. Cloud Auton. Comput. ICCAC 2015, pp. 236–243, 2015.
[11] M. R. Hoseiny farahabady, Y. C. Lee, A. Y. Zomaya, Z. Tari, and A. Song, “A model predictive controller for contention-aware resource allocation in virtualized data centers,” Proc. - 2016 IEEE 24th Int. Symp. Model. Anal. Simul. Comput. Telecommun. Syst. MASCOTS 2016, no. 2, pp. 277–282, 2016.
[12] H. Arabnejad and J. G. Barbosa, “A Budget Constrained Scheduling Algorithm for Workflow Applications,” J. Grid Comput., vol. 12, no. 4, pp. 665–679, 2014.
[13] Y. C. Lee and A. Y. Zomaya, “Stretch Out and Compact: Workflow Scheduling with Resource Abundance,” Proc. - 13th IEEE/ACM Int. Symp. Clust. Cloud, Grid Comput. CCGrid 2013, pp. 219–226, 2013.
[14] Y. C. Lee, R. Subrata, and A. Y. Zomaya, “On the performance of a dual-objective optimization model for workflow applications on grid platforms,” IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 9, pp. 1273–1284, 2009.
[15] R. Sakellariou, H. Zhao, E. Tsiakkouri, and M. Dikaiakos, “Scheduling workflows with budget constraints,” Integr. Res. GRID Comput., pp. 189–202, 2007.
[16] H. Topcuoglu, S. Hariri, and M. Wu, “Performance-effective and low-complexity task scheduling for heterogeneous computing,” Parallel Distrib. Syst. …, vol. 13, no. 3, pp. 260–274, 2002.
[17] Q. Jiang, Y. C. Lee, and A. Y. Zomaya, “Serverless Execution of Scientific Workflows,” 2017, pp. 706–721.
[18] Amazon, “AWS Lambda - Serverless Compute,” Amazon Web Services, Inc, 2014. [Online]. Available: https://aws.amazon.com/lambda/. [Accessed: 11-Nov-2017].
[19] Google, “Cloud Functions - Serverless Environment to Build and Connect Cloud Services | Google Cloud Platform,” Google Cloud Platform, 2016. [Online]. Available: https://cloud.google.com/functions/. [Accessed: 11-Nov-2017].
[20] Microsoft, “Microsoft Azure Cloud Computing Platform & Services,” Azure.microsoft.com, 2016. [Online]. Available: https://azure.microsoft.com/en-us/. [Accessed: 11-Nov-2017].
[21] A. AWS, “Optimizing Enterprise Economics with Serverless Architectures,” 2017.
[22] A. AWS, “Netflix & AWS Lambda Case Study,” 2014. [Online]. Available: https://aws.amazon.com/solutions/case-studies/netflix-and-aws-lambda/. [Accessed: 29-Nov-2017].
[23] D. Wu, D. W. Rosen, L. Wang, and D. Schaefer, “Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation,” Comput. Aided Des., vol. 59, pp. 1–14, 2015.
[24] M. Malawski, K. Figiela, M. Bubak, E. Deelman, and J. Nabrzyski, “Cost optimization of execution of multi-level deadline-constrained scientific workflows on clouds,” Sci. Program., vol. 2015, 2015.
[25] W. Zheng and R. Sakellariou, “Budget-Deadline Constrained Workflow Planning for Admission Control,” J. Grid Comput., vol. 11, no. 4, pp. 633–651, 2013.
[26] J. J. Durillo, H. M. Fard, and R. Prodan, “MOHEFT: A multi-objective list-based method for workflow scheduling,” CloudCom 2012 - Proc. 2012 4th IEEE Int. Conf. Cloud Comput. Technol. Sci., pp. 185–192, 2012.
[27] R. Prodan and M. Wieczorek, “Bi-Criteria Scheduling of Scientific Grid Workflows,” J. Grid Comput., vol. 8, no. 4, pp. 493–510, 2010.
[28] L. M. Leslie, Y. C. Lee, P. Lu, and A. Y. Zomaya, “Exploiting performance and cost diversity in the cloud,” IEEE Int. Conf. Cloud Comput. CLOUD, pp. 107–114, 2013.
[29] P. Lu, Y. C. Lee, C. Wang, B. B. Zhou, J. Chen, and A. Y. Zomaya, “Workload characteristic oriented scheduler for MapReduce,” Proc. Int. Conf. Parallel Distrib. Syst. - ICPADS, pp. 156–163, 2012.
[30] Y. C. Lee and A. Y. Zomaya, “An artificial immune system for heterogeneous multiprocessor scheduling with task duplication,” Proc. - 21st Int. Parallel Distrib. Process. Symp. IPDPS 2007; Abstr. CD-ROM, 2007.
[31] M. R. Hoseiny Farahabady, Y. C. Lee, and A. Y. Zomaya, “Pareto-optimal cloud bursting,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 10, pp. 2670–2682, 2014.
[32] K. Almi’Ani, Y. C. Lee, and B. Mans, “Resource Demand Aware Scheduling for Workflows in Clouds,” in The 16th IEEE International Symposium on Network Computing and Applications (NCA 2017), 2017, p. to appear.
[2] L. K. Arya and A. Verma, “Workflow scheduling algorithms in cloud environment - A survey,” 2014 Recent Adv. Eng. Comput. Sci. RAECS 2014, no. April, 2014.
[3] A. Bardsiri and S. Hashemi, “A Review of Workflow Scheduling in Cloud Computing Environment,” Int. J. Comput. Sci. Manag. Res., vol. 1, no. 3, pp. 348–351, 2012.
[4] M. A. Rodriguez and R. Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds,” IEEE Trans. Cloud Comput., vol. 2, no. 2, pp. 222–235, 2014.
[5] C. Q. Wu, X. Lin, D. Yu, W. Xu, and L. Li, “End-to-end delay minimization for scientific workflows in clouds under budget constraint,” IEEE Trans. Cloud Comput., vol. 3, no. 2, pp. 169–181, 2015.
[6] K. Almi’Ani and Y. C. Lee, “Partitioning-based workflow scheduling in clouds,” Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, vol. 2016–May, pp. 645–652, 2016.
[7] T. Ryan and Y. C. Lee, “Effective Resource Multiplexing for Scientific Workflows,” 2015.
[8] M. Mao and M. Humphrey, “Auto-scaling to minimize cost and meet application deadlines in cloud workflows,” Proc. 2011 Int. Conf. High Perform. Comput. Networking, Storage Anal. - SC ’11, p. 1, 2011.
[9] Y. C. Lee and B. Lian, “Cloud Bursting Scheduler for Cost Efficiency,” 2017 IEEE 10th Int. Conf. Cloud Comput., pp. 774–777, 2017.
[10] Y. C. Lee, Y. Kim, H. Han, and S. Kang, “Fine-Grained, Adaptive Resource Sharing for Real Pay-Per-Use Pricing in Clouds,” Proc. - 2015 Int. Conf. Cloud Auton. Comput. ICCAC 2015, pp. 236–243, 2015.
[11] M. R. Hoseiny farahabady, Y. C. Lee, A. Y. Zomaya, Z. Tari, and A. Song, “A model predictive controller for contention-aware resource allocation in virtualized data centers,” Proc. - 2016 IEEE 24th Int. Symp. Model. Anal. Simul. Comput. Telecommun. Syst. MASCOTS 2016, no. 2, pp. 277–282, 2016.
[12] H. Arabnejad and J. G. Barbosa, “A Budget Constrained Scheduling Algorithm for Workflow Applications,” J. Grid Comput., vol. 12, no. 4, pp. 665–679, 2014.
[13] Y. C. Lee and A. Y. Zomaya, “Stretch Out and Compact: Workflow Scheduling with Resource Abundance,” Proc. - 13th IEEE/ACM Int. Symp. Clust. Cloud, Grid Comput. CCGrid 2013, pp. 219–226, 2013.
[14] Y. C. Lee, R. Subrata, and A. Y. Zomaya, “On the performance of a dual-objective optimization model for workflow applications on grid platforms,” IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 9, pp. 1273–1284, 2009.
[15] R. Sakellariou, H. Zhao, E. Tsiakkouri, and M. Dikaiakos, “Scheduling workflows with budget constraints,” Integr. Res. GRID Comput., pp. 189–202, 2007.
[16] H. Topcuoglu, S. Hariri, and M. Wu, “Performance-effective and low-complexity task scheduling for heterogeneous computing,” Parallel Distrib. Syst. …, vol. 13, no. 3, pp. 260–274, 2002.
[17] Q. Jiang, Y. C. Lee, and A. Y. Zomaya, “Serverless Execution of Scientific Workflows,” 2017, pp. 706–721.
[18] Amazon, “AWS Lambda - Serverless Compute,” Amazon Web Services, Inc, 2014. [Online]. Available: https://aws.amazon.com/lambda/. [Accessed: 11-Nov-2017].
[19] Google, “Cloud Functions - Serverless Environment to Build and Connect Cloud Services | Google Cloud Platform,” Google Cloud Platform, 2016. [Online]. Available: https://cloud.google.com/functions/. [Accessed: 11-Nov-2017].
[20] Microsoft, “Microsoft Azure Cloud Computing Platform & Services,” Azure.microsoft.com, 2016. [Online]. Available: https://azure.microsoft.com/en-us/. [Accessed: 11-Nov-2017].
[21] A. AWS, “Optimizing Enterprise Economics with Serverless Architectures,” 2017.
[22] A. AWS, “Netflix & AWS Lambda Case Study,” 2014. [Online]. Available: https://aws.amazon.com/solutions/case-studies/netflix-and-aws-lambda/. [Accessed: 29-Nov-2017].
[23] D. Wu, D. W. Rosen, L. Wang, and D. Schaefer, “Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation,” Comput. Aided Des., vol. 59, pp. 1–14, 2015.
[24] M. Malawski, K. Figiela, M. Bubak, E. Deelman, and J. Nabrzyski, “Cost optimization of execution of multi-level deadline-constrained scientific workflows on clouds,” Sci. Program., vol. 2015, 2015.
[25] W. Zheng and R. Sakellariou, “Budget-Deadline Constrained Workflow Planning for Admission Control,” J. Grid Comput., vol. 11, no. 4, pp. 633–651, 2013.
[26] J. J. Durillo, H. M. Fard, and R. Prodan, “MOHEFT: A multi-objective list-based method for workflow scheduling,” CloudCom 2012 - Proc. 2012 4th IEEE Int. Conf. Cloud Comput. Technol. Sci., pp. 185–192, 2012.
[27] R. Prodan and M. Wieczorek, “Bi-Criteria Scheduling of Scientific Grid Workflows,” J. Grid Comput., vol. 8, no. 4, pp. 493–510, 2010.
[28] L. M. Leslie, Y. C. Lee, P. Lu, and A. Y. Zomaya, “Exploiting performance and cost diversity in the cloud,” IEEE Int. Conf. Cloud Comput. CLOUD, pp. 107–114, 2013.
[29] P. Lu, Y. C. Lee, C. Wang, B. B. Zhou, J. Chen, and A. Y. Zomaya, “Workload characteristic oriented scheduler for MapReduce,” Proc. Int. Conf. Parallel Distrib. Syst. - ICPADS, pp. 156–163, 2012.
[30] Y. C. Lee and A. Y. Zomaya, “An artificial immune system for heterogeneous multiprocessor scheduling with task duplication,” Proc. - 21st Int. Parallel Distrib. Process. Symp. IPDPS 2007; Abstr. CD-ROM, 2007.
[31] M. R. Hoseiny Farahabady, Y. C. Lee, and A. Y. Zomaya, “Pareto-optimal cloud bursting,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 10, pp. 2670–2682, 2014.
[32] K. Almi’Ani, Y. C. Lee, and B. Mans, “Resource Demand Aware Scheduling for Workflows in Clouds,” in The 16th IEEE International Symposium on Network Computing and Applications (NCA 2017), 2017, p. to appear.
Downloads
Published
2018-03-03
How to Cite
Alqaryouti, O., & Siyam, N. (2018). Serverless Computing and Scheduling Tasks on Cloud: A Review. American Scientific Research Journal for Engineering, Technology, and Sciences, 40(1), 235–247. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/3913
Issue
Section
Articles
License
Authors who submit papers with this journal agree to the following terms.
