Next Article in Journal
Efficient Allocation for Downlink Multi-Channel NOMA Systems Considering Complex Constraints
Next Article in Special Issue
Efficient Implementation of NIST LWC ESTATE Algorithm Using OpenCL and Web Assembly for Secure Communication in Edge Computing Environment
Previous Article in Journal
A Novel GFDM Waveform Design Based on Cascaded WHT-LWT Transform for the Beyond 5G Wireless Communications
Previous Article in Special Issue
Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing
Review

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

1
Department of Pervasive Systems, University of Twente, 7522 NB Enschede, The Netherlands
2
Department of Industrial Engineering and Business Information Systems, University of Twente, 7522 NB Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Taehong Kim
Sensors 2021, 21(5), 1832; https://doi.org/10.3390/s21051832
Received: 30 January 2021 / Revised: 20 February 2021 / Accepted: 24 February 2021 / Published: 5 March 2021
(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure)
Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome. View Full-Text
Keywords: resource management; cloud computing; fog computing; edge computing; algorithm classification; evaluation framework resource management; cloud computing; fog computing; edge computing; algorithm classification; evaluation framework
Show Figures

Figure 1

MDPI and ACS Style

Mijuskovic, A.; Chiumento, A.; Bemthuis, R.; Aldea, A.; Havinga, P. Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification. Sensors 2021, 21, 1832. https://doi.org/10.3390/s21051832

AMA Style

Mijuskovic A, Chiumento A, Bemthuis R, Aldea A, Havinga P. Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification. Sensors. 2021; 21(5):1832. https://doi.org/10.3390/s21051832

Chicago/Turabian Style

Mijuskovic, Adriana, Alessandro Chiumento, Rob Bemthuis, Adina Aldea, and Paul Havinga. 2021. "Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification" Sensors 21, no. 5: 1832. https://doi.org/10.3390/s21051832

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop