Placement and Chaining for Run-Time IoT Service Deployment in Edge-Cloud

Nguyen, Duong Tuan; Pham, Chuan; Nguyen, Kim Khoa; Cheriet, Mohamed

doi:10.1109/tnsm.2019.2948137

Cited by 37 publications

(24 citation statements)

References 23 publications

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“…Aimed at improving the availability of SFC, Qu [14] designed a coordinated protection mechanism that did not consider the cost of backup. Besides, Nguyen [4] considered the delay and bandwidth consumption of the underlying IoT network, as well as the dynamic characteristics of network traffic. He also explained the specific types of IoT devices and how the IoT devices and the SFC collaborate to complete services.…”

Section: B Reliability Improvingmentioning

confidence: 99%

“…Fig.4.b present the three migration strategies. In Fig.4.a, the two servers (3,4) where FW is hosted have a low utilization rate, which can be indicated by the green circle. However, even if the utilization of two working servers are both low, their energy consumption is still more than a moderately utilized server.…”

Section: ) Migration Schemementioning

confidence: 99%

“…When designing the mapping algorithm of SFC, many researchers aim at optimizing the cost of deployment, the reliability and end-to-end delay of service chain, the acceptance rate of service request, the rationality and effectiveness of resource allocation and other factors. Particularly, an IoT device and a Docker-based light-weight virtualization functional architecture has been proposed [2], and several SFC mapping algorithms are designed for IoT in [3][4][5][6]. Fu [3] proposed an SFC mapping scheme based on deep reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

“…Fu [3] proposed an SFC mapping scheme based on deep reinforcement learning. Nguyen [4] studied the resource consumption optimization problem of the service chain deployment in the cloud-side collaborative network under the IoT scenario. He [5] studied the optimal placement of VNFs with multiple instances.…”

Section: Introductionmentioning

confidence: 99%

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A Reliability-and-Energy-Balanced Service Function Chain Mapping and Migration Method for Internet of Things

Liao

et al. 2020

IEEE Access

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With the rapid development of Internet of Things (IoT) technology, Network Function Virtualization (NFV) is introduced in the edge network to provide flexible and personalized service. However, there still exist some problems to be solved, such as high cost, unbalanced load, and low availability. Therefore, a reliability-and-energy-balanced Service Function Chain (SFC) mapping and migration method is presented for IoT applications. First, aiming at improving network performance and reducing expenditure, an SFC mapping algorithm based on cost optimization, load balancing, and reliability is proposed to map SFC requests onto the network and provide backup. Second, aiming at optimizing resource configuration, an SFC migration method based on energy consumption and quality of service is proposed to integrate network resources. Simulation results show that the proposed method outperforms the compared algorithms by 15.5% and 24.55% in the acceptance ratio of SFC requests and the overall costs, respectively.

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Section: B Reliability Improvingmentioning

confidence: 99%

Section: ) Migration Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Reliability-and-Energy-Balanced Service Function Chain Mapping and Migration Method for Internet of Things

Liao

et al. 2020

IEEE Access

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show abstract

“…For each operator of the other two layers, the function selects a worst-fit resource that supports the operator requirements. Since the goal is just to select candidate resources and not a deployment placement, if there is no resource fit, it ignores the operator and moves to the next one (lines [11][12][13][14][15][16]. At last, the combination of resources evaluated by the model contains those selected in each layer.…”

Section: Resource Selection Techniquementioning

confidence: 99%