Comparing Modern Differential Evolution Variants in Multiobjective Cloud Scheduling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260466" target="_blank" >RIV/61989100:27240/25:10260466 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/abs/10.1145/3712255.3726659" target="_blank" >https://dl.acm.org/doi/abs/10.1145/3712255.3726659</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3712255.3726659" target="_blank" >10.1145/3712255.3726659</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Modern Differential Evolution Variants in Multiobjective Cloud Scheduling

Popis výsledku v původním jazyce

Cloud scheduling is a complex optimization problem balancing fast task execution for cloud users and efficient resource utilization for cloud operators. The requirements by both parties are usually limited by Service Level Agreements and Quality of Service constraints that define the level of provided service. Although many scheduling algorithms exist, the ability of nature-inspired metaheuristics to solve complex multiobjective optimization problems motivates their investigation in the context of cloud scheduling. This work evaluates the ability of modern Differential Evolution algorithm versions to schedule cloud jobs. The computational experiments are performed with a recent cloud scheduling dataset and demonstrate a large variance in the results obtained by different algorithms. Finally, is pointed out that cloud scheduling is a real-world constrained multiobjective optimization problem with a good potential to serve as a benchmark for nature-inspired metaheuristics.

Název v anglickém jazyce

Comparing Modern Differential Evolution Variants in Multiobjective Cloud Scheduling

Popis výsledku anglicky

Cloud scheduling is a complex optimization problem balancing fast task execution for cloud users and efficient resource utilization for cloud operators. The requirements by both parties are usually limited by Service Level Agreements and Quality of Service constraints that define the level of provided service. Although many scheduling algorithms exist, the ability of nature-inspired metaheuristics to solve complex multiobjective optimization problems motivates their investigation in the context of cloud scheduling. This work evaluates the ability of modern Differential Evolution algorithm versions to schedule cloud jobs. The computational experiments are performed with a recent cloud scheduling dataset and demonstrate a large variance in the results obtained by different algorithms. Finally, is pointed out that cloud scheduling is a real-world constrained multiobjective optimization problem with a good potential to serve as a benchmark for nature-inspired metaheuristics.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GF22-34873K" target="_blank" >GF22-34873K: Vícekriteriální optimalizace s omezeními pomocí analýzy potenciálních ploch</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

GECCO 2025 Companion - Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion

ISBN

979-8-4007-1464-1

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

847-850

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Malaga

Datum konání akce

14. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001564494900241