The various applications of the Internet of Things and the Internet of Vehicles impose high requirements on the network environment, such as bandwidth and delay. To meet low-latency requirements, the concept of mobile edge computing has been introduced. Through virtualisation technology, service providers can rent computation resources from the infrastructure of the network operator, whereas network operators can deploy all kinds of service functions (SFs) to the edge network to reduce network latency. However, how to appropriately deploy SFs to the edge of the network presents a problem. Apart from improving the network efficiency of edge computing service deployment, how to effectively reduce the cost of service deployment is also important to achieve a performance-cost balance. In this paper, we present a novel SF deployment management platform that allows users to dynamically deploy edge computing service applications with the lowest network latency and service deployment costs in edge computing network environments. We describe the platform design and system implementation in detail. The core platform component is an SF deployment simulator that allows users to compare various SF deployment strategies. We also design and implement a genetic algorithm-based service deployment algorithm for edge computing (GSDAE) in network environments. This method can reduce the average network latency for a client who accesses a certain service for multiple tenants that rent computing resources and subsequently reduce the associated SF deployment costs. We evaluate the proposed platform by conducting extensive experiments, and experiment results show that our platform has a practical use for the management and deployment of edge computing applications given its low latency and deployment costs not only in pure edge computing environments but also in mixed edge and cloud computing scenarios.
The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.
The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator ( O). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture of O tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center of O. The service model of O is capable of reducing network latency in response to the low-latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows different Os to share network resources and speed up the development of edge computing in the future.
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.
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