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Energy-aware scientific workflow scheduling in cloud environment

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Abstract

Cloud computing represents a significant shift in computer capability acquisition from the former ownership model to the current subscription approach. In cloud computing, services are provisioned and released in a distributed environment and encourage researchers to further investigate the benefits of cloud resources for executing scientific applications such as workflows. Workflow is composed by a number of fine-grained and coarse-grained tasks. The runtime of fine-grained tasks may be shorter than the duration of system overheads. These overheads can be reduced by merging the multiple fine-grained tasks into a single job which is called task clustering. Clustering of the task is itself a big challenge because workflow tasks are dependent on each other either by data or control dependency. Further, workflow scheduling is also critical issues which aimed to successfully complete the execution of workflow without compromising the agreed Quality of Service parameters such as deadline, cost, etc. Energy efficiency is another challenging issues and energy-aware scheduling is a promising way to achieve the energy-efficient cloud environment. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to provide complete framework for workflow scheduling. The main contribution of this study is to propose a novel scheduling framework that provide a step by step solution for workflow execution while considering the mentioned issues. In order to minimize energy consumption and total execution cost, power-aware dynamic scheduling algorithms are designed and developed that try to execute scientific applications within the user-defined deadline. We implement the task clustering and partial critical path algorithm which helps to forms the jobs of fine-grained tasks and recursively assign the sub-deadlines to the task which are on the partial critical path. Further, to improve the energy efficiency, we implement Dynamic Voltage and Frequency Scaling (DVFS) technique on computing nodes to dynamically adjust voltage and frequency of the processor. Simulation is performed on Montage, CyberShake, SIPHT, LIGO Inspiral Analysis scientific applications and it is observed that the proposed framework deal with the mentioned issues. From the analysis of results it is observed that using clustering and DVFS technique transmission cost and energy consumption is reduced at considerable level.

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References

  1. Buyya, R., Vecchiola, C., Selvi, S.T.: Mastering Cloud Computing: Foundations and Applications Programming. Morgan Kaufmann, Burlington (2013)

    Google Scholar 

  2. Buyya, R., Beloglazov, A., Abawajy, J.: Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering. The University of Melbourne, Australia, no. Vm, pp. 1–12 (2010)

  3. Brown, R., Masanet, E., Nordman, B., Tschudi, B., Shehabi, A., Stanley, J., Koomey, J., Sartor, D., Chan, P., Loper, J., Capana, S., Hedman, B., Duff, R., Haines, E., Sass, D., Fanara, A.: Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431, Technical Report (2007)

  4. Koomey, J.G.: Growth in Data Center Electricity use 2005 to 2010, Ph.D. dissertation (2011)

  5. Andrae, A., Edler, T.: On global electricity usage of communication technology: trends to 2030. Challenges 6(1), 117–157 (2015)

    Article  Google Scholar 

  6. Merout, T., Monteil, T., Da Costa, G., Calheiros, R. Neves., Buyya, R., Alexandru, M., Guérout, T., Monteil, T., Da Costa, G., Calheiros, R. Neves., Buyya, R., Alexandru, M.: Energy-aware simulation with DVFS. Simul. Model. Pract. Theory 39, 76–91 (2013)

    Article  Google Scholar 

  7. Cao, F., Zhu, Wu, C.Q.: Energy-Efficient Resource Management for Scientific Workflows in Clouds. In: 2014 IEEE World Congress on Services, pp. 402–409 (2014)

  8. Hsu, C.H., Feng, W.C.: A feasibility analysis of power awareness in commodity-based high-performance clusters. In: IEEE International Conference on Cluster Computing, pp. 1–10 (2005)

  9. Hsu, C.H., Feng, W.C.: A Power-Aware Run-Time System for High-Performance Computing. In: ACM/IEEE Conference on Supercomputing. IEEE, 2005, pp. 1–1 (2005)

  10. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Fut. Gen. Comput. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

  11. da Silva, R.F., Juve, G., Deelman, E., Glatard, T., Desprez, F., Thain, D., Tovar, B., Livny, M.: Toward fine-grained online task characteristics estimation in scientific workflows. In: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science, pp. 58–67 (2013)

  12. Qin, X., Jiang, H.: A novel fault-tolerant scheduling algorithm for precedence constrained tasks in real-time heterogeneous systems. Parallel Comput. 32(5–6), 331–356 (2006)

    Article  MathSciNet  Google Scholar 

  13. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco (1991)

    MATH  Google Scholar 

  14. da Silva, R., Juve, G., Deelman, E.: Toward fine-grained online task characteristics estimation in scientific workflows. In: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science, pp. 58–67 (2013)

  15. Chen, W., Deelman, E.: Workflow overhead analysis and optimizations. In: 6th workshop on Workflows in support of large-scale science, pp. 11–20 (2011)

  16. Muthuvelu, N., Liu, J., Soe, N.L., Venugopal, S., Sulistio, A., Buyya, R.: A dynamic job grouping-based scheduling for deploying applications with fine-grained tasks on global grids. In: Australasian Workshop on Grid Computing and e-Research 2005, pp. 41–48 (2005)

  17. Muthuvelu, N., Chai, I., Eswaran, C.: An adaptive and parameterized job grouping algorithm for scheduling grid jobs. In: International Conference on Advanced Communication Technology. IEEE, pp. 975–980 (2008)

  18. Muthuvelu, N., Chai, I., Chikkannan, E., Buyya, R.: On-Line Task Granularity Adaptation for Dynamic Grid Applications. In: 10th International Conference on Algorithms and Architectures for Parallel Processing. Springer, pp. 266–277 (2010)

  19. Muthuvelu, N., Vecchiola, C., Chai, I., Chikkannan, E., Buyya, R.: Task granularity policies for deploying bag-of-task applications on global grids. Fut. Gen. Comput. Syst. 29(1), 170–181 (2013)

    Article  Google Scholar 

  20. Ng, W., Keat, A.T., Fong, L.T., Chaw, L., Chee, S.: Scheduling framework for bandwidth-aware job grouping-based scheduling in grid computing. Technical Report 2 (2006)

  21. Ang, T.F., Ng, W.K., Ling, T.C., Por, L.Y., Liew, C.S.: A bandwidth-aware job grouping-based scheduling on grid environment. Inf. Technol. J. 8, 372–377 (2009)

    Article  Google Scholar 

  22. Liu, Q., Liao, Y.: Grouping-based fine-grained job scheduling in grid computing. In: First International Workshop on Education Technology and Computer Science, pp. 556–559 (2009)

  23. Singh, G., Su, M.-H., Vahi, K., Deelman, E., Berriman, B., Good, J., Katz, D., Mehta, G.: Workflow task clustering for best effort systems with Pegasus. In: 15th ACM Mardi Gras Conference, no. 9. ACM, New York, pp. 1–8 (2008)

  24. da Silva, R. F., Glatard, T., Desprez, F.: On-Line, Non-clairvoyant Optimization of Workflow Activity Granularity on Grids. In: 19th International Conference on Parallel Processing. Springer, Berlin, pp. 255–266 (2013)

  25. Chen, W., Deelman, E.: orkflowSim: A toolkit for simulating scientific workflows in distributed environments. In: IEEE 8th International Conference on E-Science, pp. 1–8 (2012)

  26. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2011)

    Google Scholar 

  27. Chen, W., Ferreira Da Silva, R., Deelman, E., Sakellariou, R.: Using imbalance metrics to optimize task clustering in scientific workflow executions. Fut. Gen. Comput. Syst. 46, 69–84 (2015)

    Article  Google Scholar 

  28. Chen, W., Silva, R.F.D., Deelman, E., Sakellariou, R.: Balanced Task Clustering in Scientific Workflows. In: IEEE 9th International Conference on e-Science. IEEE, pp. 188–195 (2013)

  29. Mao, M., Humphrey, M.: Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: 2011 International Conference for High Performance Computing, pp. 1–12. Networking, Storage and Analysis (SC) (2011)

  30. Elzeki, O.M., Reshad, M.Z., Elsoud, M.A.: Improved max–min algorithm in cloud computing. Int. J. Comput. Appl. 50(12), 22–27 (2012)

    Google Scholar 

  31. Etminani, K., Naghibzadeh, M.: A Min-Min Max-Min selective algorithm for grid task scheduling. In: 3rd IEEE/IFIP International Conference in Central Asia on Internet. IEEE, pp. 1–7 (2007)

  32. Bhoi, U., Ramanuj, P.: Enhanced max–min task scheduling algorithm in cloud computing. Int. J. Appl. Innov. Eng. Manag. 2(4), 259–264 (2013)

    Google Scholar 

  33. Lin, W., Liang, C., Wang, J.Z., Buyya, R.: Bandwidth-aware divisible task scheduling for cloud computing. Software 44(2), 163–174 (2014)

    Google Scholar 

  34. Wang, X., Yeo, C.S., Buyya, R., Su, J.: Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Fut. Gen. Comput. Syst. 27(8), 1124–1134 (2011)

    Article  Google Scholar 

  35. Zeng, L., Veeravalli, B., Li, X.: SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J. Parallel Distrib. Comput. 75, 141–151 (2015)

    Article  Google Scholar 

  36. Xu, M., Cui, L., Wang, H., Bi, Y.: A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing. In: Proceedings - 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. ISPA 2009, pp. 629–634 (2009)

  37. Durillo, J.J., Nae, V., Prodan, R.: Multi-objective energy-efficient workflow scheduling using list-based heuristics. Fut. Gen. Comput. Syst. 36, 221–236 (2013)

    Article  Google Scholar 

  38. Lee, Y.C., Han, H., Zomaya, A.Y., Yousif, M.: Resource-efficient workflow scheduling in clouds. Knowl.-Based Syst. 80(February), 153–162 (2015)

    Article  Google Scholar 

  39. Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y., Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y., Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y.: A compromised-time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on cloud computing platform. Int. J. High Perform. Comput. Appl. 24(4), 445–456 (2010)

    Article  Google Scholar 

  40. Byun, E.K., Kee, Y.S., Kim, J.S., Maeng, S.: Cost optimized provisioning of elastic resources for application workflows. Fut. Gen. Comput. Syst. 27(8), 1011–1026 (2011)

    Article  Google Scholar 

  41. Bittencourt, L.F., Madeira, E.R.M.: HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. J. Internet Serv. Appl. 2(3), 207–227 (2011)

    Article  Google Scholar 

  42. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds. Fut. Gen. Comput. Syst. 29(1), 158–169 (2013)

    Article  Google Scholar 

  43. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans. Parallel Distrib. Syst. 23(8), 1400–1414 (2012)

    Article  Google Scholar 

  44. Zheng, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 633–651 (2013)

    Article  Google Scholar 

  45. Chopra, N., Singh, S.: Deadline and cost based workflow scheduling in hybrid cloud. In: Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, pp. 840–846 (2013)

  46. Li, Hongjia, Li, J., Yao, Wang, Nazarian, S., Lin, X., Wang, Y.: Fast and energy-aware resource provisioning and task scheduling for cloud systems. In: 18th International Symposium on Quality Electronic Design. IEEE, pp. 174–179 (2017)

  47. Chen, L., Li, X., Ruiz, R.: Resource renting for periodical cloud workflow applications. IEEE Trans. Serv. Comput. 1, 1–1 (2017)

    Google Scholar 

  48. Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Fut. Gen. Comput. Syst. 78, 257–271 (2018)

    Article  Google Scholar 

  49. Adhikari, M., Koley, S.: Cloud computing: a multi-workflow scheduling algorithm with dynamic reusability. Arab. J. Sci. Eng. 43(2), 645–660 (2018)

    Article  Google Scholar 

  50. Manasrah, A.M., Ali, H.B.: Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing (2018). https://doi.org/10.1155/2018/1934784

  51. Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Proceedings - International Conference on Advanced Information Networking and Applications (2010)

  52. Wu, Z., Ni, Z., Gu, L., Liu, X.: A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling. In: 2010 International Conference on Computational Intelligence and Security. IEEE, pp. 184–188 (2010)

  53. Feller, E., Rilling, L., Morin, C.: Energy-Aware Ant Colony Based Workload Placement in Clouds. In: 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE, pp. 26–33 (2011)

  54. Sawant, S.: A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment. Master’s Projects (2011)

  55. Server, S.: StorageServers, Technical Report (2013). https://storageservers.wordpress.com/

  56. Belady, C.L.: In the data center, power and cooling costs more than the it equipment it supports. http://www.electronics-cooling.com/2007/02/in-the-data-center-power-and-cooling-costs-more-than-the-it-equipment-it-supports/

  57. Bilal, K., Malik, S.U.R., Khan, S.U., Zomaya, A.Y.: Trends and challenges in cloud datacenters. IEEE Cloud Comput. 1(1), 10–20 (2014)

    Article  Google Scholar 

  58. Whitehead, B., Andrews, D., Shah, A., Maidment, G.: Assessing the environmental impact of data centres part 1: background, energy use and metrics. Build. Environ. 82, 151–159 (2014)

    Article  Google Scholar 

  59. Mathew, V., Sitaraman, R.K., Shenoy, P.: Energy-aware load balancing in content delivery networks. In: Proceedings - IEEE INFOCOM, pp. 954–962 (2012)

  60. Van Heddeghem, W., Lambert, S., Lannoo, B., Colle, D., Pickavet, M., Demeester, P.: Trends in worldwide ICT electricity consumption from 2007 to 2012. Comput. Commun. 50, 64–76 (2014)

    Article  Google Scholar 

  61. Cameron, K., Ge, R., Rong, F., Xizhou, X.: High-performance, power-aware distributed computing for scientific applications. Computer 38(11), 40–47 (2005)

    Article  Google Scholar 

  62. Srikantaiah, S., Kansal, A., Zhao, F.: Energy Aware Consolidation for Cloud Computing. In: Power Aware Computing and Systems (2008)

  63. Beloglazov, A., Buyya, R.: Energy Efficient Resource Management in Virtualized Cloud Data Centers. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE, USA, pp. 826–831 (2010)

  64. Duy, T. V. T., Sato, Y., Inoguchi, Y.: Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Ph.D. Forum (IPDPSW). IEEE, pp. 1–8 (2010)

  65. Li, J., Peng, J., Lei, Z., Zhang, W.: An energy-efficient scheduling approach based on private clouds. Comput. Eng. 4(10), 716–724 (2011)

    Google Scholar 

  66. Madani, N., Lebbat, A., Tallal, S., Medromi, H.: New cloud consolidation architecture for electrical energy consumption management. In: Africon. IEEE, 2013, pp. 1–3 (2013)

  67. Salimian, L., Esfahani, F.S., Nadimi-Shahraki, M.-H.: An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing 98(6), 641–660 (2016)

    Article  MathSciNet  Google Scholar 

  68. Monil, M.A.H., Qasim, R., Rahman, R.M.: Energy-aware VM consolidation approach using combination of heuristics and migration control. In: 2014 Ninth International Conference on Digital Information Management (ICDIM), pp. 74–79 (2014)

  69. Farahnakian, F., Liljeberg, P., Plosila, J.: LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers. In: 2013 39th Euromicro Conference on Software Engineering and Advanced Applications. IEEE, pp. 357–364 (2013)

  70. Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. IEEE, pp. 500–507 (2014)

  71. Ebrahimirad, V., Goudarzi, M., Rajabi, A.: Energy-aware scheduling for precedence-constrained parallel virtual machines in virtualized data centers. J. Grid Comput. 13(2), 233–253 (2015)

    Article  Google Scholar 

  72. Abdullahi, M., Ngadi, M.A., Abdulhamid, S.M.: Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Fut. Gen. Comput. Syst. 56, 640–650 (2016)

    Article  Google Scholar 

  73. Jiang, J., Lin, Y., Xie, G., Fu, L., Yang, J.: Time and energy optimization algorithms for the static scheduling of multiple workflows in heterogeneous computing system. J. Grid Comput. 15(4), 435–456 (2017)

    Article  Google Scholar 

  74. Sharma, M., Verma, A., Sangaiah, A.K.: Energy-Constrained Workflow Scheduling in Cloud Using E-DSOS Algorithm. In: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, pp. 159–169. Academic Press, New York (2018)

    Chapter  Google Scholar 

  75. Shuja, J., Bilal, K., Madani, S.A., Othman, M., Ranjan, R., Balaji, P., Khan, S.U.: Survey of techniques and architectures for designing energy-efficient data centers. IEEE Syst. J. 10(2), 507–519 (2016)

    Article  Google Scholar 

  76. Mangalampalli, S., Pokkuluri, K.S., Kocherla, R., Rapaka, A., Kota, N.R.: An Efficient Workflow Scheduling Algorithm in Cloud Computing Using Cuckoo Search and PSO Algorithms, pp. 137–145 (2022). https://link.springer.com/chapter/10.1007/978-981-16-8987-1_15

  77. Uddin, M., Shah, A., Alsaqour, R., Memon, J.: Measuring efficiency of tier level data centers to implement green energy efficient data centers. Middle East J. Sci. Res. 15(2), 200–207 (2013)

    Google Scholar 

  78. Ma, Y., Gong, B., Sugihara, R., Gupta, R.: Energy-efficient deadline scheduling for heterogeneous systems. J. Parallel Distrib. Comput. 72(12), 1725–1740 (2012)

    Article  MATH  Google Scholar 

  79. Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Patil, S., Su, M.-H., Vahi, K., Livny, M.: Computing Grid. Pegasus: Mapping Scientific Workflows onto the Grid, pp. 11–20. Springer, Berlin (2004)

    Book  Google Scholar 

  80. Kolpe, T., Zhai, A., Sapatnekar, S.S.: Enabling improved power management in multicore processors through clustered DVFS. In: Design , Automation & Test in Europe, pp. 1–6 (2011)

  81. Choosing an App Engine Environment—App Engine Documentation | Google Cloud Platform. https://cloud.google.com/appengine/docs/the-appengine-environments

  82. EC2 Instance Types – Amazon Web Services (AWS). https://aws.amazon.com/ec2/instance-types/

  83. Intro to Microsoft Azure — Microsoft Azure. https://azure.microsoft.com/en-in/documentation/articles/fundamentals-introduction-to-azure/

  84. IBM - Cloud Computing for Builders & Innovators. http://www.ibm.com/cloud-computing/

  85. Hoffa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., Good, J.: On the use of cloud computing for scientific workflows. In: 2008 IEEE Fourth International Conference on eScience. IEEE, pp. 640–645 (2008)

  86. Juve, G., Deelman, E., Vahi, K., Mehta, G., Berman, B.P., Berriman, B., Maechling, P.: Scientific workflow applications on amazon EC2. In: e-science 2009 - Proceedings of the 2009 5th IEEE International Conference on e-Science Workshops (2009)

  87. Deelman, E.: Grids and clouds: making workflow applications work in heterogeneous distributed environments. Int. J. High Perform. Comput. Appl. 24(3), 284–298 (2010)

    Article  Google Scholar 

  88. Energy Optimizers Ltd (Plogg) — VentureRadar. https://www.ventureradar.com/organisation/Energy Optimizers Ltd (Plogg)/ffa1e019-6226-43a7-977d-5d3d9a3a03a4

  89. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  90. Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-Scale Science. IEEE, pp. 1–10 (2008)

  91. 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.: Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand. In: Optimizing Scientific Return for Astronomy Through Information Technologies, vol. 5493 (2004)

  92. Graves, R., Jordan, T.H., Callaghan, S., Deelman, E., Field, E., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., Okaya, D., Small, P., Vahi, K.: CyberShake: a physics-based seismic hazard model for southern California. Pure Appl. Geophys. 168(3–4), 367–381 (2011)

    Article  Google Scholar 

  93. Brown, D.A., Brady, P.R., Dietz, A., Cao, J., Johnson, B., McNabb, J.: A case study on the use of workflow technologies for scientific analysis: gravitational wave data analysis. In: Workflows for e-Science, pp. 39–59. Springer, London (2007)

    Chapter  Google Scholar 

  94. SIPHT. http://pegasus.isi.edu/applications/sipht

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Authors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, India

    Anita Choudhary, Girdhari Singh & Emmanuel S. Pilli

  2. National Institute of Technology Sikkim, Sikkim, India

    Mahesh Chandra Govil

  3. National Institute of Technology Uttarakhand (NITUK), Srinagar, India

    Lalit K. Awasthi

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  1. Anita Choudhary
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Choudhary, A., Govil, M.C., Singh, G. et al. Energy-aware scientific workflow scheduling in cloud environment. Cluster Comput 25, 3845–3874 (2022). https://doi.org/10.1007/s10586-022-03613-3

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