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Resource scheduling methods in cloud and fog computing environments: a systematic literature review

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Abstract

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments.

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  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Aryan Rahimikhanghah, Melika Tajkey & Bahareh Rezazadeh

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan

    Amir Masoud Rahmani

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  1. Aryan Rahimikhanghah
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Rahimikhanghah, A., Tajkey, M., Rezazadeh, B. et al. Resource scheduling methods in cloud and fog computing environments: a systematic literature review. Cluster Comput 25, 911–945 (2022). https://doi.org/10.1007/s10586-021-03467-1

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