Cloud service reliability modelling and optimal task scheduling

Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IET communications Jg. 11; H. 2; S. 161 - 167
Hauptverfasser: Cui, Hongyan, Li, Yang, Liu, Xiaofei, Ansari, Nirwan, Liu, Yunjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 26.01.2017
Schlagworte:
Algorithms
Cloud computing
cloud scheduling problem
cloud service reliability modelling
Clouds
computational complexity
configurable computing resources
GA‐CAS algorithm
genetic algorithms
genetic algorithm‐based chaotic ant swarm algorithm
heterogeneous environment
Markov processes
Markov‐based method
multiobjective optimisation problem
Multiple objective analysis
network access
Networks
NP‐hard problem
optimal task scheduling
optimisation
Optimization
Reliability
reliability analysis
resource allocation
scheduling
service sharing
software reliability
task analysis
Task scheduling
GA-CAS algorithm
multiobjective optimisation problem
software reliability
service sharing
genetic algorithms
genetic algorithm-based chaotic ant swarm algorithm
task analysis
cloud service reliability modelling
optimal task scheduling
optimisation
resource allocation
configurable computing resources
NP-hard problem
reliability analysis
Markov-based method
cloud scheduling problem
scheduling
Markov processes
heterogeneous environment
cloud computing
network access
computational complexity
ISSN:1751-8628, 1751-8636
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP-hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov-based method. They formulate the cloud scheduling problem as a multi-objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm-based chaotic ant swarm (GA-CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi-objective optimisation problem. Simulation results have demonstrated that GA-CAS generally speeds up convergence and outperforms other meta-heuristic approaches.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2016.0417