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Journal of Computer Science
Article . 2016 . Peer-reviewed
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Journal of Computer Science
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https://dx.doi.org/10.60692/8c...
Other literature type . 2016
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https://dx.doi.org/10.60692/4v...
Other literature type . 2016
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Article . 2020
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LPSO: Another Algorithm for Workflow Scheduling in the Cloud

LPSO: خوارزمية أخرى لجدولة سير العمل في السحابة
Authors: Toan Phan Thanh; Loc Nguyen The; Said Elnaffar; Cuong Nguyen Doan;

LPSO: Another Algorithm for Workflow Scheduling in the Cloud

Abstract

Aunque muchos investigadores han discutido el problema de la programación del flujo de trabajo, se han introducido algunas soluciones eficientes para la computación en la nube. En este artículo, presentamos LPSO, un nuevo algoritmo para la programación del flujo de trabajo. Basado en el método de optimización de enjambre de partículas, nuestro algoritmo propuesto no solo garantiza la rápida convergencia sino que también evita quedar atrapado en extremos locales. Nuestros experimentos de simulación que utilizan escenarios reales de prueba de CloudSim revelan que LPSO es superior a los algoritmos propuestos anteriormente. Además, la desviación entre la solución encontrada por LPSO y la solución óptima es insignificante.

Bien que le problème de planification du flux de travail ait été discuté par de nombreux chercheurs, quelques solutions efficaces ont été introduites pour l'informatique en nuage. Dans cet article, nous présentons LPSO, un nouvel algorithme pour la planification du flux de travail. Basé sur la méthode d'optimisation de l'essaim de particules, notre algorithme proposé assure non seulement la convergence rapide, mais évite également d'être piégé sur les extrema locaux. Nos expériences de simulation utilisant des scénarios réels de test CloudSim révèlent que LPSO est supérieur aux algorithmes précédemment proposés. En outre, l'écart entre la solution trouvée par LPSO et la solution optimale est négligeable.

Although workflow scheduling problem has been discussed by many researchers, a few efficient solutions have been introduced for Cloud computing.In this article, we present LPSO, a novel algorithm for workflow scheduling.Based on the Particle Swarm Optimization method, our proposed algorithm not only ensures the fast convergence but also avoid being trapped on local extrema.Our simulation experiments using CloudSim testing real scenarios reveal that LPSO is superior to formerly proposed algorithms.Moreover, the deviation between the solution found by LPSO and the optimal solution is negligible.

على الرغم من مناقشة العديد من الباحثين لمشكلة جدولة سير العمل، فقد تم تقديم عدد قليل من الحلول الفعالة للحوسبة السحابية. في هذه المقالة، نقدم LPSO، وهي خوارزمية جديدة لجدولة سير العمل. استنادًا إلى طريقة تحسين سرب الجسيمات، لا تضمن خوارزميتنا المقترحة التقارب السريع فحسب، بل تتجنب أيضًا الوقوع في فخ الأطراف المحلية. تكشف تجارب المحاكاة التي أجريناها باستخدام السيناريوهات الحقيقية لاختبار CloudSim أن LPSO متفوقة على الخوارزميات المقترحة سابقًا. علاوة على ذلك، فإن الانحراف بين الحل الذي وجدته LPSO والحل الأمثل لا يكاد يذكر.

Keywords

FOS: Computer and information sciences, CloudSim, Computer Networks and Communications, Cloud Computing and Big Data Technologies, Mathematical analysis, Workflow, Database, Distributed Grid Computing Systems, Artificial Intelligence, FOS: Mathematics, Cloud computing, Optimization Methods in Machine Learning, Large-Scale Optimization, Particle swarm optimization, Mathematical optimization, Cloud Computing, Job shop scheduling, Computer science, Distributed computing, Task Scheduling, Algorithm, High-Performance Computing, Operating system, Schedule, Maxima and minima, Computer Science, Physical Sciences, Scheduling (production processes), Mathematics, Information Systems, Workflow Management

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold