Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Silva, J (Autor) Outra; Eduardo Marques (Autor) FCUP; Lopes, L (Autor) FCUP; Silva, F (Autor) FCUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS

Vol. 10

Página Final: 38

ISSN: 2192-113X

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Outras Informações

ID Authenticus: P-00T-J76

DOI: 10.1186/s13677-021-00251-9

Abstract (EN): We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 21