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. 2021 May 31;21(11):3800.
doi: 10.3390/s21113800.

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

David Chunhu Li  1 , Chiing-Ting Huang  2 , Chia-Wei Tseng  2 , Li-Der Chou  2
Affiliations

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

David Chunhu Li et al. Sensors (Basel). .

Abstract

Edge computing exhibits the advantages of real-time operation, low latency, and low network cost. It has become a key technology for realizing smart Internet of Things applications. Microservices are being used by an increasing number of edge computing networks because of their sufficiently small code, reduced program complexity, and flexible deployment. However, edge computing has more limited resources than cloud computing, and thus edge computing networks have higher requirements for the overall resource scheduling of running microservices. Accordingly, the resource management of microservice applications in edge computing networks is a crucial issue. In this study, we developed and implemented a microservice resource management platform for edge computing networks. We designed a fuzzy-based microservice computing resource scaling (FMCRS) algorithm that can dynamically control the resource expansion scale of microservices. We proposed and implemented two microservice resource expansion methods based on the resource usage of edge network computing nodes. We conducted the experimental analysis in six scenarios and the experimental results proved that the designed microservice resource management platform can reduce the response time for microservice resource adjustments and dynamically expand microservices horizontally and vertically. Compared with other state-of-the-art microservice resource management methods, FMCRS can reduce sudden surges in overall network resource allocation, and thus, it is more suitable for the edge computing microservice management environment.

Keywords: Internet of Things; edge computing; fuzzy system; microservice; resource management; scaling.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Gaussian membership function of the microservice resource management fuzzy sets.
Figure 1
Figure 1
Gaussian membership function of the microservice resource management fuzzy sets.
Figure 2
Figure 2
Use case of the microservice resource management fuzzy interference system.
Figure 3
Figure 3
Horizontal scaling of the FMCRS algorithm.
Figure 4
Figure 4
Vertical scaling of the FMCRS algorithm.
Figure 5
Figure 5
The system architecture of the fuzzy-based microservice resource management platform for edge computing.
Figure 6
Figure 6
Components of the status-collecting module and the associated external components. (a) Flowchart of information exchange between the status-collecting module and cAdvisor; (b) diagram of the internal components of the status-collecting module.
Figure 7
Figure 7
Experiment evaluation system architecture.
Figure 8
Figure 8
Microservice deployment time on the designed fuzzy-based microservice resource management platform.
Figure 9
Figure 9
Computing resource scaling time of microservices on the designed fuzzy-based microservice resource management platform.
Figure 10
Figure 10
Computing resource monitoring architecture of the fuzzy-based microservice resource management platform.
Figure 11
Figure 11
Computing usage of microservices monitored on the designed platform. (a) CPU usages of five microservices monitored. (b) Memory usages of five microservices monitored.
Figure 12
Figure 12
Computing usage of edge nodes monitored on the designed platform. (a) CPU usages of three edge computing nodes monitored. (b) Memory usages of three edge computing nodes monitored.
Figure 13
Figure 13
Horizontal scaling of a microservice on the designed platform.
Figure 14
Figure 14
Vertical scaling of the FMCRS algorithm. (a) Microservice status before applying vertical scaling of the FMCRS algorithm. (b) Microservice status after applying vertical scaling of the FMCRS algorithm.
Figure 15
Figure 15
Comparison of the resource management of microservices between two scaling methods.
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