A Unified Mechanism for Cloud Scheduling of Scientific Workflows

Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads eac...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ali Kamran [verfasserIn] Umar Farooq [verfasserIn] Ihsan Rabbi [verfasserIn] Kashif Zia [verfasserIn] Muhammad Assam [verfasserIn] Hadeel Alsolai [verfasserIn] Fahd N. Al-Wesabi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Cloud computing scheduling scientific workflows WorkFlowSim CloudSim

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 71233-71246
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:71233-71246

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3187704

Katalog-ID:	DOAJ036533475

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author	Ali Kamran
spellingShingle	Ali Kamran misc TK1-9971 misc Cloud computing misc scheduling misc scientific workflows misc WorkFlowSim misc CloudSim misc Electrical engineering. Electronics. Nuclear engineering A Unified Mechanism for Cloud Scheduling of Scientific Workflows
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topic	misc TK1-9971 misc Cloud computing misc scheduling misc scientific workflows misc WorkFlowSim misc CloudSim misc Electrical engineering. Electronics. Nuclear engineering
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title	A Unified Mechanism for Cloud Scheduling of Scientific Workflows
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title_sort	unified mechanism for cloud scheduling of scientific workflows
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title_auth	A Unified Mechanism for Cloud Scheduling of Scientific Workflows
abstract	Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads each, for, five well-known workflows. It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows. Different algorithms performed better, when they were applied to various workloads of a particular workflow. This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload. Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time. For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm. The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16–63, 30–68, 0–69, 30–68, and 9–71 in corresponding order. This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance. It is, however, required once and, thus, the increase in execution time might be marginal. The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.
abstractGer	Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads each, for, five well-known workflows. It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows. Different algorithms performed better, when they were applied to various workloads of a particular workflow. This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload. Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time. For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm. The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16–63, 30–68, 0–69, 30–68, and 9–71 in corresponding order. This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance. It is, however, required once and, thus, the increase in execution time might be marginal. The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.
abstract_unstemmed	Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads each, for, five well-known workflows. It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows. Different algorithms performed better, when they were applied to various workloads of a particular workflow. This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload. Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time. For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm. The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16–63, 30–68, 0–69, 30–68, and 9–71 in corresponding order. This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance. It is, however, required once and, thus, the increase in execution time might be marginal. The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.
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title_short	A Unified Mechanism for Cloud Scheduling of Scientific Workflows
url	https://doi.org/10.1109/ACCESS.2022.3187704 https://doaj.org/article/55317eea188d4422b4c92407db1bd19c https://ieeexplore.ieee.org/document/9812609/ https://doaj.org/toc/2169-3536
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author2	Umar Farooq Ihsan Rabbi Kashif Zia Muhammad Assam Hadeel Alsolai Fahd N. Al-Wesabi
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