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Erschienen in: Wireless Personal Communications 2/2022

07.03.2022

A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds

verfasst von: Rambabu Medara, Ravi Shankar Singh

Erschienen in: Wireless Personal Communications | Ausgabe 2/2022

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Abstract

Cloud computing has emerged as the preeminent computing platform for multiple enterprises. All scales of organizations adopt cloud services to leverage cloud technology to drive their businesses ahead. It is prevalent to use the workflow paradigm in modeling a wide variety of problems to compute in distributed environments. Cloud computing is mostly adapting technology to deal with workflow applications, particularly applications with unpredictable workloads. Due to the increased demand for cloud services, excessive power utilization in cloud data centers is a serious issue that needs to be addressed. Scientific workflow applications, in particular, consume high amounts of electrical energy. Many studies have been conducted on the consumption of energy in the cloud environment, and this area of research attracts people from all fields, including both research and business. For this paper, a survey was conducted on existing energy-efficient techniques for scheduling various workflows in a cloud environment. We targeted the methods that minimize energy consumption with assured quality of service constraints. This study on energy-aware and proper workflow scheduling provide extensive knowledge about various energy-aware scheduling paradigms currently going on. The review will help in listing the future directions in this field along with other factors included.

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Literatur
1.
Zurück zum Zitat Rambabu, M., Gupta, S., & Singh, R. S. (2021). Data mining in cloud computing: survey. In: Innovations in Computational Intelligence and Computer Vision (pp. 48–56). Springer. Rambabu, M., Gupta, S., & Singh, R. S. (2021). Data mining in cloud computing: survey. In: Innovations in Computational Intelligence and Computer Vision (pp. 48–56). Springer.
2.
Zurück zum Zitat Medara, R., Singh, R. S., Kumar, U. S., & Barfa, S. (2020). Energy efficient virtual machine consolidation using water wave optimization. In: 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–7). IEEE. Medara, R., Singh, R. S., Kumar, U. S., & Barfa, S. (2020). Energy efficient virtual machine consolidation using water wave optimization. In: 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–7). IEEE.
4.
Zurück zum Zitat Arroba, P, Moya, J. M., Ayala, J. L., & Buyya, R. (2017). Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency and Computation: Practice and Experience 29(10), e4067. Arroba, P, Moya, J. M., Ayala, J. L., & Buyya, R. (2017). Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency and Computation: Practice and Experience 29(10), e4067.
6.
Zurück zum Zitat Engbers, N., & Taen, E. (2014). Green data net. Report to it room infra. European Commision. FP7 ICT 2013.6. 2. Engbers, N., & Taen, E. (2014). Green data net. Report to it room infra. European Commision. FP7 ICT 2013.6. 2.
11.
Zurück zum Zitat Belkhir, L., & Elmeligi, A. (2018). Assessing ict global emissions footprint: Trends to 2040 and recommendations. Journal of Cleaner Production, 177, 448–463.CrossRef Belkhir, L., & Elmeligi, A. (2018). Assessing ict global emissions footprint: Trends to 2040 and recommendations. Journal of Cleaner Production, 177, 448–463.CrossRef
12.
Zurück zum Zitat You, X., Li, Y., Zheng, M., Zhu, C., & Lifeng, Y. (2017). A survey and taxonomy of energy efficiency relevant surveys in cloud-related environments. IEEE Access, 5, 14066–14078.CrossRef You, X., Li, Y., Zheng, M., Zhu, C., & Lifeng, Y. (2017). A survey and taxonomy of energy efficiency relevant surveys in cloud-related environments. IEEE Access, 5, 14066–14078.CrossRef
13.
Zurück zum Zitat Adhikary, T., Das, A. K., Razzaque, M. A., & Sarkar, A. M. J. (2013). Energy-efficient scheduling algorithms for data center resources in cloud computing. In 2013 IEEE 10th International Conference on High Performance Computing and Communications and 2013 IEEE International Conference on Embedded and Ubiquitous Computing) (pp. 1715–1720). IEEE. Adhikary, T., Das, A. K., Razzaque, M. A., & Sarkar, A. M. J. (2013). Energy-efficient scheduling algorithms for data center resources in cloud computing. In 2013 IEEE 10th International Conference on High Performance Computing and Communications and 2013 IEEE International Conference on Embedded and Ubiquitous Computing) (pp. 1715–1720). IEEE.
14.
Zurück zum Zitat Rodriguez, M. A., & Buyya, R. (2017). A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8), e4041.CrossRef Rodriguez, M. A., & Buyya, R. (2017). A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8), e4041.CrossRef
15.
Zurück zum Zitat Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, Stephen. (2009). Systematic literature reviews in software engineering-a systematic literature review. Information and Software Technology, 51(1), 7–15.CrossRef Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, Stephen. (2009). Systematic literature reviews in software engineering-a systematic literature review. Information and Software Technology, 51(1), 7–15.CrossRef
16.
Zurück zum Zitat Choenni, S., Bakker, R., & Baets, W. (2003). On the evaluation of workflow systems in business processes. Electronic Journal of Information Systems Evaluation, 6(2), 33–44. Choenni, S., Bakker, R., & Baets, W. (2003). On the evaluation of workflow systems in business processes. Electronic Journal of Information Systems Evaluation, 6(2), 33–44.
17.
Zurück zum Zitat Barker, A, & Van Hemert, J. (2007). Scientific workflow: a survey and research directions. In: International Conference on Parallel Processing and Applied Mathematics, (pp. 746–753). Springer. Barker, A, & Van Hemert, J. (2007). Scientific workflow: a survey and research directions. In: International Conference on Parallel Processing and Applied Mathematics, (pp. 746–753). Springer.
18.
Zurück zum Zitat Deelman, E., Gannon, D., Shields, M., & Taylor, I. (2009). Workflows and e-science: An overview of workflow system features and capabilities. Future Generation Computer Systems, 25(5), 528–540.CrossRef Deelman, E., Gannon, D., Shields, M., & Taylor, I. (2009). Workflows and e-science: An overview of workflow system features and capabilities. Future Generation Computer Systems, 25(5), 528–540.CrossRef
19.
Zurück zum Zitat Liew, C. S., Atkinson, M. P., Galea, Michelle, A., Tan F., Martin, P. & Van HHemert. I., J. (2016). Scientific workflows: Moving across paradigms. ACM Computing Surveys (CSUR), 49(4), 1–39. Liew, C. S., Atkinson, M. P., Galea, Michelle, A., Tan F., Martin, P. & Van HHemert. I., J. (2016). Scientific workflows: Moving across paradigms. ACM Computing Surveys (CSUR), 49(4), 1–39.
20.
Zurück zum Zitat Gupta, S, Singh, R. S, Vasant, U. D., & Saxena, V. User defined weight based budget and deadline constrained workflow scheduling in cloud. Concurrency and Computation: Practice and Experience, p. e6454. Gupta, S, Singh, R. S, Vasant, U. D., & Saxena, V. User defined weight based budget and deadline constrained workflow scheduling in cloud. Concurrency and Computation: Practice and Experience, p. e6454.
21.
Zurück zum Zitat Berriman, G. B., Deelman, E., Good, J. C., Jacob, J. C., Katz, D. S., Kesselman, C., Laity, A. C., Prince, T. A., Singh, G., & Su, M.-H. (2004). Montage: A grid-enabled engine for delivering custom science-grade mosaics on demand. In: Optimizing Scientific Return for Astronomy through Information Technologies, Vol. 5493, pp. 221–232. International Society for Optics and Photonics. Berriman, G. B., Deelman, E., Good, J. C., Jacob, J. C., Katz, D. S., Kesselman, C., Laity, A. C., Prince, T. A., Singh, G., & Su, M.-H. (2004). Montage: A grid-enabled engine for delivering custom science-grade mosaics on demand. In: Optimizing Scientific Return for Astronomy through Information Technologies, Vol. 5493, pp. 221–232. International Society for Optics and Photonics.
22.
Zurück zum Zitat Graves, R., Jordan, T. H., Callaghan, S., Deelman, E., Field, E., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., et al. (2011). Cybershake: A physics-based seismic hazard model for Southern California. Pure and Applied Geophysics, 168(3–4), 367–381.CrossRef Graves, R., Jordan, T. H., Callaghan, S., Deelman, E., Field, E., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., et al. (2011). Cybershake: A physics-based seismic hazard model for Southern California. Pure and Applied Geophysics, 168(3–4), 367–381.CrossRef
24.
25.
Zurück zum Zitat Livny, J., Teonadi, H., Livny, M., & Waldor, M. K. (2008). High-throughput, kingdom-wide prediction and annotation of bacterial non-coding rnas. PloS One, 3(9), e3197.CrossRef Livny, J., Teonadi, H., Livny, M., & Waldor, M. K. (2008). High-throughput, kingdom-wide prediction and annotation of bacterial non-coding rnas. PloS One, 3(9), e3197.CrossRef
26.
Zurück zum Zitat Abbott, B. P., Abbott, R., Adhikari, R., Ajith, P., Allen, B., Allen, G., Amin, R. S., Anderson, S. B., Anderson, W. G., Arain, M. A., et al. (2009). Ligo: The laser interferometer gravitational-wave observatory. Reports on Progress in Physics, 72(7), 076901.CrossRef Abbott, B. P., Abbott, R., Adhikari, R., Ajith, P., Allen, B., Allen, G., Amin, R. S., Anderson, S. B., Anderson, W. G., Arain, M. A., et al. (2009). Ligo: The laser interferometer gravitational-wave observatory. Reports on Progress in Physics, 72(7), 076901.CrossRef
27.
Zurück zum Zitat Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., & Vahi, K. (2013). Characterizing and profiling scientific workflows. Future Generation Computer Systems, 29(3), 682–692.CrossRef Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., & Vahi, K. (2013). Characterizing and profiling scientific workflows. Future Generation Computer Systems, 29(3), 682–692.CrossRef
28.
Zurück zum Zitat Souri, A., Rahmani, A. M., Navimipour, N. J., & Rezaei, R. (2020). A hybrid formal verification approach for qos-aware multi-cloud service composition. Cluster Computing, 23(4), 2453–2470.CrossRef Souri, A., Rahmani, A. M., Navimipour, N. J., & Rezaei, R. (2020). A hybrid formal verification approach for qos-aware multi-cloud service composition. Cluster Computing, 23(4), 2453–2470.CrossRef
29.
Zurück zum Zitat Konjaang, J. K., & Xu, L. (2021). Multi-objective workflow optimization strategy (mowos) for cloud computing. Journal of Cloud Computing, 10(1), 1–19. Konjaang, J. K., & Xu, L. (2021). Multi-objective workflow optimization strategy (mowos) for cloud computing. Journal of Cloud Computing, 10(1), 1–19.
30.
Zurück zum Zitat Fakhfakh, F., Kacem, H. H., & Kacem, A. H. (2014). Workflow scheduling in cloud computing: a survey. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (pp. 372–378). IEEE. Fakhfakh, F., Kacem, H. H., & Kacem, A. H. (2014). Workflow scheduling in cloud computing: a survey. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (pp. 372–378). IEEE.
31.
Zurück zum Zitat Arya, L. K., & Verma, A. (2014). Workflow scheduling algorithms in cloud environment-a survey. 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–4. IEEE. Arya, L. K., & Verma, A. (2014). Workflow scheduling algorithms in cloud environment-a survey. 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–4. IEEE.
32.
Zurück zum Zitat Cao, F., & Zhu, M. M. (2013). Energy-aware workflow job scheduling for green clouds. 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 232–239. IEEE. Cao, F., & Zhu, M. M. (2013). Energy-aware workflow job scheduling for green clouds. 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 232–239. IEEE.
33.
Zurück zum Zitat Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308. Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:​1006.​0308.
34.
Zurück zum Zitat Li, Z., Ge, J., Hu, H., Song, W., Hu, H., & Luo, B. (2018). Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Transactions on Services Computing, 11(4), 713–726.CrossRef Li, Z., Ge, J., Hu, H., Song, W., Hu, H., & Luo, B. (2018). Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Transactions on Services Computing, 11(4), 713–726.CrossRef
35.
Zurück zum Zitat Yu, J., & Buyya, R. (2004). A novel architecture for realizing grid workflow using tuple spaces. In: Fifth IEEE/ACM International Workshop on Grid Computing, (pp. 119–128). IEEE. Yu, J., & Buyya, R. (2004). A novel architecture for realizing grid workflow using tuple spaces. In: Fifth IEEE/ACM International Workshop on Grid Computing, (pp. 119–128). IEEE.
38.
Zurück zum Zitat Alaei, M., Khorsand, R., & Ramezanpour, M. (2021). An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Applied Soft Computing, 99, 106895.CrossRef Alaei, M., Khorsand, R., & Ramezanpour, M. (2021). An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Applied Soft Computing, 99, 106895.CrossRef
39.
Zurück zum Zitat Medara, R., Singh, R. S., & Amit. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323.CrossRef Medara, R., Singh, R. S., & Amit. (2021). Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization. Simulation Modelling Practice and Theory, 110, 102323.CrossRef
40.
Zurück zum Zitat Medara, R., & Singh, R. S. (2021). Energy efficient and reliability aware workflow task scheduling in cloud environment. Wireless Personal Communications, pp. 1–20. Medara, R., & Singh, R. S. (2021). Energy efficient and reliability aware workflow task scheduling in cloud environment. Wireless Personal Communications, pp. 1–20.
41.
Zurück zum Zitat Ranjan, R., Thakur, I. S., Aujla, G. S., Kumar, N., & Zomaya, A. Y. (2020). Energy-efficient workflow scheduling using container-based virtualization in software-defined data centers. IEEE Transactions on Industrial Informatics, 16(12), 7646–7657.CrossRef Ranjan, R., Thakur, I. S., Aujla, G. S., Kumar, N., & Zomaya, A. Y. (2020). Energy-efficient workflow scheduling using container-based virtualization in software-defined data centers. IEEE Transactions on Industrial Informatics, 16(12), 7646–7657.CrossRef
42.
Zurück zum Zitat Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents. Computer Networks, pp. 107340. Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents. Computer Networks, pp. 107340.
43.
Zurück zum Zitat Li, C., Zhang, Y., Hao, Z., & Luo, Y. (2020). An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters. Computer Networks, 170, 107096.CrossRef Li, C., Zhang, Y., Hao, Z., & Luo, Y. (2020). An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters. Computer Networks, 170, 107096.CrossRef
44.
Zurück zum Zitat Li, C., Tang, J., Ma, T., Yang, X., & Luo, Y. (2020). Load balance based workflow job scheduling algorithm in distributed cloud. Journal of Network and Computer Applications, 152, 102518.CrossRef Li, C., Tang, J., Ma, T., Yang, X., & Luo, Y. (2020). Load balance based workflow job scheduling algorithm in distributed cloud. Journal of Network and Computer Applications, 152, 102518.CrossRef
45.
Zurück zum Zitat Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). Online scheduling of dependent tasks of cloud‘s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 24(21), 16177–16199.CrossRef Asghari, A., Sohrabi, M. K., & Yaghmaee, F. (2020). Online scheduling of dependent tasks of cloud‘s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 24(21), 16177–16199.CrossRef
46.
Zurück zum Zitat Stavrinides, G. L., & Karatza, H. D. (2019). An energy-efficient, qos-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing dvfs and approximate computations. Future Generation Computer Systems, 96, 216–226.CrossRef Stavrinides, G. L., & Karatza, H. D. (2019). An energy-efficient, qos-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing dvfs and approximate computations. Future Generation Computer Systems, 96, 216–226.CrossRef
47.
Zurück zum Zitat Garg, R., Mittal, M., et al. (2019). Reliability and energy efficient workflow scheduling in cloud environment. Cluster Computing, 22(4), 1283–1297.CrossRef Garg, R., Mittal, M., et al. (2019). Reliability and energy efficient workflow scheduling in cloud environment. Cluster Computing, 22(4), 1283–1297.CrossRef
48.
Zurück zum Zitat Qureshi, B. (2019). Profile-based power-aware workflow scheduling framework for energy-efficient data centers. Future Generation Computer Systems, 94, 453–467.CrossRef Qureshi, B. (2019). Profile-based power-aware workflow scheduling framework for energy-efficient data centers. Future Generation Computer Systems, 94, 453–467.CrossRef
49.
Zurück zum Zitat Safari, Monire, & Khorsand, Reihaneh. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in dvfs-enabled cloud environment. Simulation Modelling Practice and Theory, 87, 311–326.CrossRef Safari, Monire, & Khorsand, Reihaneh. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in dvfs-enabled cloud environment. Simulation Modelling Practice and Theory, 87, 311–326.CrossRef
50.
Zurück zum Zitat Stavrinides, G. L., & Karatza, H. D. (2018). Energy-aware scheduling of real-time workflow applications in clouds utilizing dvfs and approximate computations. In: 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), (pp. 33–40). IEEE. Stavrinides, G. L., & Karatza, H. D. (2018). Energy-aware scheduling of real-time workflow applications in clouds utilizing dvfs and approximate computations. In: 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), (pp. 33–40). IEEE.
51.
Zurück zum Zitat Wang, Z., Wen, Y., Chen, J., Cao, B., & Wang, F. (2018). Towards energy-efficient scheduling with batch processing for instance-intensive cloud workflows. In: 2018 IEEE Intl Conf on Parallel and Distributed Processing with Applications, Ubiquitous Computing and Communications, Big Data and Cloud Computing, Social Computing and Networking, Sustainable Computing and Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), (pp. 590–596). IEEE. Wang, Z., Wen, Y., Chen, J., Cao, B., & Wang, F. (2018). Towards energy-efficient scheduling with batch processing for instance-intensive cloud workflows. In: 2018 IEEE Intl Conf on Parallel and Distributed Processing with Applications, Ubiquitous Computing and Communications, Big Data and Cloud Computing, Social Computing and Networking, Sustainable Computing and Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), (pp. 590–596). IEEE.
52.
Zurück zum Zitat Juarez, F., Ejarque, J., & Badia, R. M. (2018). Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems, 78, 257–271.CrossRef Juarez, F., Ejarque, J., & Badia, R. M. (2018). Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems, 78, 257–271.CrossRef
53.
Zurück zum Zitat Yao, G., Ding, Y., & Hao, K. (2017). Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm. Journal of Central South University, 24(5), 1050–1062.CrossRef Yao, G., Ding, Y., & Hao, K. (2017). Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm. Journal of Central South University, 24(5), 1050–1062.CrossRef
54.
Zurück zum Zitat Xu, X., Dou, W., Zhang, X., & Chen, J. (2016). Enreal: An energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Transactions on Cloud Computing, 4(2), 166–179.CrossRef Xu, X., Dou, W., Zhang, X., & Chen, J. (2016). Enreal: An energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Transactions on Cloud Computing, 4(2), 166–179.CrossRef
55.
Zurück zum Zitat Khaleel, M., & Zhu, M. M. (2016). Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud. International Journal of Computational Science and Engineering, 13(3), 268–284.CrossRef Khaleel, M., & Zhu, M. M. (2016). Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud. International Journal of Computational Science and Engineering, 13(3), 268–284.CrossRef
56.
Zurück zum Zitat Li, H., Zhu, H., Ren, G., Wang, H., Zhang, H., & Chen, L. (2016). Energy-aware scheduling of workflow in cloud center with deadline constraint. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), (pp. 415–418). IEEE. Li, H., Zhu, H., Ren, G., Wang, H., Zhang, H., & Chen, L. (2016). Energy-aware scheduling of workflow in cloud center with deadline constraint. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), (pp. 415–418). IEEE.
57.
Zurück zum Zitat Tang, Z., Cheng, Z., Li, K., & Li, K. (2014). An efficient energy scheduling algorithm for workflow tasks in hybrids and dvfs-enabled cloud environment. In: 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming, (pp. 255–261). IEEE. Tang, Z., Cheng, Z., Li, K., & Li, K. (2014). An efficient energy scheduling algorithm for workflow tasks in hybrids and dvfs-enabled cloud environment. In: 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming, (pp. 255–261). IEEE.
58.
Zurück zum Zitat Pietri, I., & Sakellariou, R. (2014). Energy-aware workflow scheduling using frequency scaling. In: 2014 43rd International Conference on Parallel Processing Workshops, (pp. 104–113). IEEE. Pietri, I., & Sakellariou, R. (2014). Energy-aware workflow scheduling using frequency scaling. In: 2014 43rd International Conference on Parallel Processing Workshops, (pp. 104–113). IEEE.
59.
Zurück zum Zitat Zheng, W., & Huang, S. (2014). Deadline constrained energy-efficient scheduling for workflows in clouds. In: 2014 Second International Conference on Advanced Cloud and Big Data, (pp. 69–76). IEEE. Zheng, W., & Huang, S. (2014). Deadline constrained energy-efficient scheduling for workflows in clouds. In: 2014 Second International Conference on Advanced Cloud and Big Data, (pp. 69–76). IEEE.
60.
Zurück zum Zitat Yassa, S., Chelouah, R., Kadima, H., & Granado, B. (2013). Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal, 2013. Yassa, S., Chelouah, R., Kadima, H., & Granado, B. (2013). Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal, 2013.
61.
Zurück zum Zitat Thanavanich, T., & Uthayopas, P. (2013). Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment. In: 2013 International Computer Science and Engineering Conference (ICSEC), (pp. 37–42). IEEE. Thanavanich, T., & Uthayopas, P. (2013). Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment. In: 2013 International Computer Science and Engineering Conference (ICSEC), (pp. 37–42). IEEE.
62.
Zurück zum Zitat Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., & Sakellariou, R. (2013). Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, (pp. 34–41). IEEE. Pietri, I., Malawski, M., Juve, G., Deelman, E., Nabrzyski, J., & Sakellariou, R. (2013). Energy-constrained provisioning for scientific workflow ensembles. In: 2013 International Conference on Cloud and Green Computing, (pp. 34–41). IEEE.
63.
Zurück zum Zitat Huang, Q., Su, S., Li, J., Xu, P., Shuang, K., & Huang, X. (2012). Enhanced energy-efficient scheduling for parallel applications in cloud. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), (pp. 781–786). IEEE. Huang, Q., Su, S., Li, J., Xu, P., Shuang, K., & Huang, X. (2012). Enhanced energy-efficient scheduling for parallel applications in cloud. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), (pp. 781–786). IEEE.
64.
Zurück zum Zitat Wang, L., Von Laszewski, G., Dayal, J., & Wang, F. (2010). Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, (pp. 368–377). IEEE. Wang, L., Von Laszewski, G., Dayal, J., & Wang, F. (2010). Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, (pp. 368–377). IEEE.
65.
Zurück zum Zitat Zhu, Q., Zhu, J., & Agrawal, G. (2010). Power-aware consolidation of scientific workflows in virtualized environments. In: SC’10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, (pp. 1–12). IEEE. Zhu, Q., Zhu, J., & Agrawal, G. (2010). Power-aware consolidation of scientific workflows in virtualized environments. In: SC’10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, (pp. 1–12). IEEE.
66.
Zurück zum Zitat Minas, L, & Ellison, B. (2009). Energy efficiency for information technology: How to reduce power consumption in servers and data centers. Intel Press. Minas, L, & Ellison, B. (2009). Energy efficiency for information technology: How to reduce power consumption in servers and data centers. Intel Press.
67.
Zurück zum Zitat Rivoire, Suzanne, Ranganathan, Parthasarathy, & Kozyrakis, Christos. (2008). A comparison of high-level full-system power models. HotPower, 8(2), 32–39. Rivoire, Suzanne, Ranganathan, Parthasarathy, & Kozyrakis, Christos. (2008). A comparison of high-level full-system power models. HotPower, 8(2), 32–39.
68.
Zurück zum Zitat Khalil, K. M., Abdel-Aziz, M., Nazmy, T. T., & Salem, A.-B. M. (2017). Cloud simulators–an evaluation study. International Journal Information Models and Analyses , 6(1). Khalil, K. M., Abdel-Aziz, M., Nazmy, T. T., & Salem, A.-B. M. (2017). Cloud simulators–an evaluation study. International Journal Information Models and Analyses , 6(1).
69.
Zurück zum Zitat Jiang, Q., Lee, Y. C., & Zomaya, A. Y. (2015). Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529. Jiang, Q., Lee, Y. C., & Zomaya, A. Y. (2015). Executing large scale scientific workflow ensembles in public clouds. In: 2015 44th International Conference on Parallel Processing, pp. 520–529.
70.
Zurück zum Zitat Tyagi, R., & G., Santosh K. (2018). A survey on scheduling algorithms for parallel and distributed systems. Silicon Photonics and High Performance Computing, pp. 51–64. Springer. Tyagi, R., & G., Santosh K. (2018). A survey on scheduling algorithms for parallel and distributed systems. Silicon Photonics and High Performance Computing, pp. 51–64. Springer.
71.
Zurück zum Zitat Kaur, Gurjit. (2016). A dag based task scheduling algorithms for multiprocessor system-a survey. International Journal of Grid and Distributed Computing, 9(9), 103–114.CrossRef Kaur, Gurjit. (2016). A dag based task scheduling algorithms for multiprocessor system-a survey. International Journal of Grid and Distributed Computing, 9(9), 103–114.CrossRef
72.
Zurück zum Zitat Umarani Srikanth, G., & Geetha, R. (2018). Task scheduling using ant colony optimization in multicore architectures: a survey. Soft Computing, 22(15), 5179–5196.CrossRef Umarani Srikanth, G., & Geetha, R. (2018). Task scheduling using ant colony optimization in multicore architectures: a survey. Soft Computing, 22(15), 5179–5196.CrossRef
73.
Zurück zum Zitat Arunarani, A. R., Manjula, Dhanabalachandran, & Sugumaran, Vijayan. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407–415.CrossRef Arunarani, A. R., Manjula, Dhanabalachandran, & Sugumaran, Vijayan. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407–415.CrossRef
74.
Zurück zum Zitat Singh, P., Dutta, M., & Aggarwal, N. (2017). A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 52(1), 1–51.CrossRef Singh, P., Dutta, M., & Aggarwal, N. (2017). A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 52(1), 1–51.CrossRef
75.
Zurück zum Zitat Motlagh, A. A., Movaghar, A., & Rahmani, A. M. (2020). Task scheduling mechanisms in cloud computing: A systematic review. International Journal of Communication Systems, 33(6), e4302.CrossRef Motlagh, A. A., Movaghar, A., & Rahmani, A. M. (2020). Task scheduling mechanisms in cloud computing: A systematic review. International Journal of Communication Systems, 33(6), e4302.CrossRef
76.
Zurück zum Zitat Liu, S., Ren, K., Deng, K., & Song, J. (2016). A dynamic resource allocation and task scheduling strategy with uncertain task runtime on iaas clouds. In: 2016 Sixth International Conference on Information Science and Technology (ICIST), (pp. 174–180). IEEE. Liu, S., Ren, K., Deng, K., & Song, J. (2016). A dynamic resource allocation and task scheduling strategy with uncertain task runtime on iaas clouds. In: 2016 Sixth International Conference on Information Science and Technology (ICIST), (pp. 174–180). IEEE.
77.
Zurück zum Zitat Pingping, L., Zhang, G., Zhu, Z., Zhou, X., Sun, J., & Zhou, J. (2019). A review of cost and makespan-aware workflow scheduling in clouds. Journal of Circuits, Systems and Computers, 28(06), 1930006.CrossRef Pingping, L., Zhang, G., Zhu, Z., Zhou, X., Sun, J., & Zhou, J. (2019). A review of cost and makespan-aware workflow scheduling in clouds. Journal of Circuits, Systems and Computers, 28(06), 1930006.CrossRef
78.
Zurück zum Zitat Ijaz, S., Munir, E. U., Ahmad, S. G., Mustafa R., M., & Rana, O. F. (2021). Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing. pp. 1–27. Ijaz, S., Munir, E. U., Ahmad, S. G., Mustafa R., M., & Rana, O. F. (2021). Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing. pp. 1–27.
79.
Zurück zum Zitat Lin, K.-J.., Natarajan, S., & Liu, J. W-S. (1987). Imprecise results: Utilizing partial computations in real-time systems. Lin, K.-J.., Natarajan, S., & Liu, J. W-S. (1987). Imprecise results: Utilizing partial computations in real-time systems.
80.
Zurück zum Zitat Kalra, Mala, & Singh, Sarbjeet. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal, 16(3), 275–295.CrossRef Kalra, Mala, & Singh, Sarbjeet. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal, 16(3), 275–295.CrossRef
81.
Zurück zum Zitat Casavant, T. L., & Kuhl, J. G. (1988). A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Transactions on software engineering, 14(2), 141–154.CrossRef Casavant, T. L., & Kuhl, J. G. (1988). A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Transactions on software engineering, 14(2), 141–154.CrossRef
82.
Zurück zum Zitat Talbi, E. G. (2009). Metaheuristics: From design to implementation, (Vol. 74). John Wiley & Sons.MATHCrossRef Talbi, E. G. (2009). Metaheuristics: From design to implementation, (Vol. 74). John Wiley & Sons.MATHCrossRef
83.
Zurück zum Zitat Shishira, S. R., Kandasamy, A., Chandrasekaran, K. (2016). Survey on meta heuristic optimization techniques in cloud computing. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), (pp. 1434–1440). IEEE. Shishira, S. R., Kandasamy, A., Chandrasekaran, K. (2016). Survey on meta heuristic optimization techniques in cloud computing. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), (pp. 1434–1440). IEEE.
84.
Zurück zum Zitat Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., & Ahmad, I. (2013). Cloud computing pricing models: A survey. International Journal of Grid and Distributed Computing, 6(5), 93–106.CrossRef Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., & Ahmad, I. (2013). Cloud computing pricing models: A survey. International Journal of Grid and Distributed Computing, 6(5), 93–106.CrossRef
85.
Zurück zum Zitat Sharma, R. K., Kamal, P., & Singh, S. P. (2015). A latency reduction mechanism for virtual machine resource allocation in delay sensitive cloud service. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), (pp. 371–375). IEEE. Sharma, R. K., Kamal, P., & Singh, S. P. (2015). A latency reduction mechanism for virtual machine resource allocation in delay sensitive cloud service. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), (pp. 371–375). IEEE.
86.
Zurück zum Zitat Octavio Gutierrez-Garcia, J., & Sim, Kwang Mong. (2013). A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Future Generation Computer Systems, 29(7), 1682–1699.CrossRef Octavio Gutierrez-Garcia, J., & Sim, Kwang Mong. (2013). A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Future Generation Computer Systems, 29(7), 1682–1699.CrossRef
87.
Zurück zum Zitat Villegas, D., Antoniou, A., Sadjadi, S. M., & Iosup, A. (2012). An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), (pp. 612–619). IEEE. Villegas, D., Antoniou, A., Sadjadi, S. M., & Iosup, A. (2012). An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), (pp. 612–619). IEEE.
88.
Zurück zum Zitat Mohanapriya, N., Kousalya, G., Balakrishnan, P., & Pethuru Raj, C. (2018). Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. Journal of Intelligent & Fuzzy Systems, 34(3), 1561–1572. Mohanapriya, N., Kousalya, G., Balakrishnan, P., & Pethuru Raj, C. (2018). Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing. Journal of Intelligent & Fuzzy Systems, 34(3), 1561–1572.
89.
Zurück zum Zitat Hsu, Ching-Hsien., Slagter, Kenn D., Chen, Shih-Chang., & Chung, Yeh-Ching. (2014). Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258, 452–462.CrossRef Hsu, Ching-Hsien., Slagter, Kenn D., Chen, Shih-Chang., & Chung, Yeh-Ching. (2014). Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258, 452–462.CrossRef
90.
Zurück zum Zitat Wen, Y., Zhibin Wang, Y., Zhang, J. L., Cao, B., & Chen, J. (2019). Energy and cost aware scheduling with batch processing for instance-intensive iot workflows in clouds. Future Generation Computer Systems, 101, 39–50.CrossRef Wen, Y., Zhibin Wang, Y., Zhang, J. L., Cao, B., & Chen, J. (2019). Energy and cost aware scheduling with batch processing for instance-intensive iot workflows in clouds. Future Generation Computer Systems, 101, 39–50.CrossRef
91.
Zurück zum Zitat Choi, H., Lim, J., Yu, H., & Lee, E. (2016). Task classification based energy-aware consolidation in clouds. Scientific Programming, 2016. Choi, H., Lim, J., Yu, H., & Lee, E. (2016). Task classification based energy-aware consolidation in clouds. Scientific Programming, 2016.
92.
Zurück zum Zitat Srichandan, S., Kumar, T. A., & Bibhudatta, S. (2018). Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Computing and Informatics Journal, 3(2), 210–230.CrossRef Srichandan, S., Kumar, T. A., & Bibhudatta, S. (2018). Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Computing and Informatics Journal, 3(2), 210–230.CrossRef
Metadaten
Titel
A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds
verfasst von
Rambabu Medara
Ravi Shankar Singh
Publikationsdatum
07.03.2022
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09621-1

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