Energy-Aware VM Placement and Task Scheduling in Cloud-IoT Computing: Classification and Performance Evaluation

Leila Ismail, Huned Materwala

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

Cloud Internet of Things (IoT) is a novel paradigm, where the limitations of IoT associated devices in terms of storage, data access, scalability, networking and computing, and complex analysis are solved through use of the cloud computing infrastructure. The pervasive adoption of cloud in the IoT framework, makes the underlying data centers exacerbate problems like the environmental carbon footprint and operational costs which arise from the high energy consumption of computing servers. Several works proposed virtual machine placement and task scheduling algorithms to reduce the energy consumption of the underlying cloud infrastructure. However, each algorithm uses a different environment, experimental setup, power consumption model and workload for its evaluation, making it difficult to compare among them. In this paper, we give a classification and evaluation of 13 different algorithms using a unified setup, with the aim of achieving an objective comparison. The workload used for the evaluation is selected to typify IoT applications, such as connected vehicles, wide area measurement systems for the power grid, and smart meters for advanced meter infrastructure. The detailed performance analysis is elaborated in this paper.

Original languageEnglish
Article number8437124
Pages (from-to)5166-5176
Number of pages11
JournalIEEE Internet of Things Journal
Volume5
Issue number6
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Cloud Internet of Things (IoT)
  • Energy-aware task scheduling algorithms
  • Energy-aware virtual machine placement algorithms
  • Green cloud computing
  • IoT

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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