Logo do repositório
 
Publicação

Data-intensive task scheduling in geo-distributed cloud computing

2026-12Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorLiu, Zhaoze
dc.contributor.authorSun, Yajuan
dc.contributor.authorLiu, Cong
dc.contributor.authorZhao, Zhiming
dc.contributor.authorCheng, Long
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer Science + Business Media
dc.date.accessioned2026-05-27T11:33:03Z
dc.date.available2026-05-27T11:33:03Z
dc.date.issued2026-12
dc.descriptionLiu, Z., Sun, Y., Liu, C., Zhao, Z., & Cheng, L. (2026). Data-intensive task scheduling in geo-distributed cloud computing: a deep reinforcement learning approach. Journal of Cloud Computing, 15, Article 79. https://doi.org/10.1186/s13677-026-00894-6
dc.description.abstractGeo-distributed clouds have become an increasingly common architecture in modern cloud computing. By placing data centers in multiple geographical regions, cloud service providers shorten the distance between users and cloud resources, enabling low-latency service delivery worldwide. However, this architecture raises new challenges for scheduling data-intensive tasks, including limited inter-regional bandwidth and high traffic costs charged by Internet service providers. These factors can significantly impact both performance and cost efficiency. Existing scheduling approaches for geo-distributed clouds often rely on mathematical and meta-heuristic methods. They typically focus on batch tasks, unable to make real-time decisions and exhibit limited adaptability to changing workload patterns. To address these constraints, this paper introduces a deep reinforcement learning-based framework for scheduling data-intensive tasks across geographically distributed data centers. The framework performs real-time scheduling while considering bandwidth constraints and traffic costs, enabling multi-objective optimization of task response time and total operational cost. Extensive experiments under various workloads show that the proposed method achieves competitive and often better performance than the evaluated baselines.en
dc.description.versionpublishersversion
dc.description.versionepub_ahead_of_print
dc.format.extent15
dc.format.extent5472130
dc.identifier.doi10.1186/s13677-026-00894-6
dc.identifier.issn2192-113X
dc.identifier.otherPURE: 163724032
dc.identifier.otherPURE UUID: add590ca-534c-48fd-8aaa-fdfcbde238af
dc.identifier.otherWOS: 001771733400001
dc.identifier.otherScopus: 105039566351
dc.identifier.urihttp://hdl.handle.net/10362/203486
dc.identifier.urlhttps://www.scopus.com/pages/publications/105039566351
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001771733400001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectData-intensive task scheduling
dc.subjectDeep reinforcement learning
dc.subjectGeo-distributed cloud computing
dc.subjectSoftware
dc.subjectComputer Networks and Communications
dc.titleData-intensive task scheduling in geo-distributed cloud computingen
dc.title.subtitlea deep reinforcement learning approachen
dc.typejournal article
degois.publication.titleJournal of Cloud Computing
degois.publication.volume15
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Data-intensive_task_scheduling.pdf
Tamanho:
5.22 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)