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

Adriana Mijuskovic*, Alessandro Chiumento, Rob Bemthuis, Adina Aldea, Paul Havinga

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

69 Citations (Scopus)
102 Downloads (Pure)


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.
Original languageEnglish
Article number1832
Number of pages22
JournalSensors (Switzerland)
Issue number5
Publication statusPublished - 5 Mar 2021


  • Algorithm classification
  • Cloud computing
  • Edge computing
  • Evaluation framework
  • Fog computing
  • Resource management


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