An efficient astronomical image processing technique using advance dynamic workflow scheduler in cloud environment
Tóm tắt
The Montage project of NASA introduces an efficient astronomical image mosaic processing service with the help of Montage workflow applications. These workflow applications contain a set of input astronomical images that are able to produce custom mosaics of the sky to understand the universe’s activities. In the last 2 decades, extensive research has been done for processing Montage application in the various distributed computing paradigms such as Grids and Clusters but fails to achieve the required performance in terms of makespan. Recently, Cloud computing is the latest computing paradigm that provides online computing resources for efficiently processing these applications. In this article, we introduce an efficient astronomical image processing technique (EAIPT) for Montage workflow application in the cloud environment that shows notable improvements in terms of minimization of makespan without increasing the time complexity with the help of an advance dynamic scheduler. The proposed dynamic scheduler tries to schedule the current workflow task on the virtual machine that already processed its last parent task based on proposed scheduling constraints. In this way, it bypasses the communication time among different VMs. The performance of the proposed algorithm is compared with the existing state of art algorithms. The experimental results prove that our proposed technique has significant performance over compared algorithms in terms of schedule length ratio (SLR), the percentage of best results, and average running time metrics.
Tài liệu tham khảo
Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K (2013) Characterizing and profiling scientific workflows. Futur Gener Comput Syst 29(3):682–692 (Special section: recent developments in high performance computing and security)
Prathibha S (2013) Monitoring the performance analysis of executing workflow applications with different resource types in a cloud environment. In: 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014), (VIT University, Chennai, India)
Deelman E, Singh G, Livny M, Berriman B, Good J (2008) The cost of doing science on the Cloud: The montage example. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, SC’08, (Piscataway, NJ, USA), IEEE Press, 50:1–50:12
Pegasus, https://github.com/pegasus-isi/montage-workflow-v2 Online; accessed: 1 Jun 2022
Montage, http://montage.ipac.caltech.edu Online; accessed: 1 Jun 2022
STScI, https://www.stsci.edu Online; accessed: 1 Jun 2022
SDSS, Sloan digital sky survey, http://www.sdss.org Online; accessed: 1 Jun 2022
IPAC, The two micron all sky survey, https://irsa.ipac.caltech.edu/Missions/2mass.html Online; accessed: 1 Jun 2022
IPAC, Infrared processing and analysis center, http://www.ipac.caltech.edu/ Online; accessed: 1 Jun 2022
Adhikari M, Amgoth T (2018) Heuristic-based load-balancing algorithm for IaaS cloud. Futur Gener Comput Syst 81:156–165
Ghafarian T, Javadi B (2015) Cloud-aware data intensive workflow scheduling on volunteer computing systems. Futur Gener Comput Syst 51:87–97
Abrishami S, Naghibzadeh M (2012) Deadline-constrained workflow scheduling in software as a service cloud. Scientia Iranica 19(3):680–689
Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Futur Gener Comput Syst 27(8):1011–1026
Chen W, Xie G, Li R, Bai Y, Fan C, Li K (2017) Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Futur Gener Comput Syst 74:1–11
Lee YC, Han H, Zomaya AY, Yousif M (2015) Resource-efficient workflow scheduling in clouds. Knowl-Based Syst 80:153–162
De Prado RP, García-Galán S, Expósito JEM, López LRL, Reche RR (2014) Processing astronomical image mosaic workflows with an expert broker in cloud computing. Image Process Commun 19(4):5–20
Mandani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 121(12)
Arunkumar Reddy D, Venkata Krishna P (2021) Feedback-based fuzzy resource management in IoT using fog computing. Evol Intel 14(2):669–681
Bharathi S, Chervenak A, Deelman E, Mehta G, Su M-H, Vahi K (2008) Characterization of scientific workflows. In: Third Workshop on Workflows in Support of Large-Scale Science, 2008. WORKS 2008. pp 1–10
Ahmad W, Alam B (2021) An efficient list scheduling algorithm with task duplication for scientific big data workflow in heterogeneous computing environments. Concurr Comput Pract Exp 33(5):e5987
Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274
Arabnejad H, Barbosa J-G (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694. https://doi.org/10.1109/tpds.2013.57
Ahmad W, Alam B, Ahuja S, Malik S (2021) A dynamic VM provisioning and de-provisioning based cost-efficient deadline-aware scheduling algorithm for Big Data workflow applications in a cloud environment. Clust Comput 24(1):249–278
Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows. In: Proceeding third workshop on workflows in support of large-scale science, IEEE, p 1–10
WorkflowGenerator-Pegasus Workflow Management System https://confluence.pegasus.isi.edu/display/pegasus/Deprecated+Workflow+Generator Online; accessed 1 Jun 2022