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. 2022 Mar 21;22(6):2404.
doi: 10.3390/s22062404.

Integrated Resource Management for Fog Networks

Jui-Pin Yang  1 , Hui-Kai Su  2
Affiliations

Integrated Resource Management for Fog Networks

Jui-Pin Yang et al. Sensors (Basel). .

Abstract

In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks.

Keywords: energy perception; fog network; hotspot offload; load balancing; resource management; service level agreement.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
System architecture of fog networks.
Figure 2
Figure 2
The framework of integrated resource management.
Figure 3
Figure 3
Architecture of intelligent energy perception.
Figure 4
Figure 4
SLA mapping mechanism.
Figure 5
Figure 5
Request-generating models.
Figure 6
Figure 6
The average requests where sixteen fog nodes have high loads of hotspot services.
Figure 7
Figure 7
The average requests where thirty-two fog nodes have high loads of hotspot services.
Figure 8
Figure 8
The average requests where sixteen fog nodes have different load conditions.
See this image and copyright information in PMC

References

    1. Liskov B., Ghemawat S., Gruber R., Johnson P., Shrira L., Williams M. Replication in the Harp file system. ACM Symp. Oper. Syst. Princ. 1991:226–238. doi: 10.1145/121133.121169. - DOI
    1. Sitaram D., Dan A., Yu P. Issues in the design of multi-server file systems to cope with load skew; Proceedings of the International Conference on Parallel and Distributed Information Systems; San Diego, CA, USA. 20–22January 1993; pp. 214–224.
    1. Chervenak A.L., Patterson D.A., Katz R.H. Choosing the best storage system for video service; Proceedings of the International Conference on Multimedia; San Diego, CA, USA. 5–9 November 1995; pp. 109–119.
    1. Ghemawat S., Gobioff H., Leung S.T. The Google file system. ACM SIGOPS Oper. Syst. Rev. 2003;37:29–43. doi: 10.1145/1165389.945450. - DOI
    1. Yang J.P. Intelligent Offload Detection for Achieving Approximately Optimal Load Balancing. IEEE Access. 2018;6:58609–58618. doi: 10.1109/ACCESS.2018.2873287. - DOI

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