Escove: Energy‐sla‐aware edge–cloud computation offloading in vehicular networks

Leila Ismail, Huned Materwala

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident preven-tion, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state‐of‐the‐art ap-proach.

Original languageEnglish
Article number5233
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - Aug 1 2021

Keywords

  • Cloud computing
  • Computation offloading
  • Deadline
  • Edge computing
  • Energy‐efficiency
  • Latency
  • Quality of service
  • Queuing theory
  • Service level agreement
  • Vehicular network

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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