Carrier Network Link Loss Rate Reasoning Algorithm Based on Network Resources and Service Characteristics

How to depend on different services provide different guarantee of QoS, and to ensure the effective use of network resources will become an important task in overlay network. In this paper, we consider constraints of network bandwidth, delay, loss rate, cost ,and base on the resources of current global network, as well as user-specific QoS metric needs to choose the best QoS route based on genetic algorithm. Experiments show that the algorithm is feasible in overlay network routing,and it has the better global load balancing capabilities.

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Link Loss Inference Algorithm with Network Topology Aware in Communication Networks

Mathematical Problems in Engineering ◽

10.1155/2020/5836248 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Shunli Zhang

Keyword(s):

Communication Networks ◽

Network Topology ◽

Packet Loss ◽

Loss Rate ◽

Pass Rate ◽

Nonsingular Matrix ◽

Estimation Accuracy ◽

Inference Algorithm ◽

Network Operation ◽

Link Loss

When there are many suspected loss links, the links in the path with a higher pass rate are assumed to be nondrop packet links or assuming that the link with the largest number of shares is a loss link, but this assumption lacks valid proof. In order to overcome these shortcomings, this paper proposes a link loss inference algorithm with network topology aware. The network model is established based on the historical data of the network operation and network topology characteristics. A weighted relative entropy ranking method is proposed to quantify the suspected packet loss links in each independent subset. The packet loss rate of the packet loss link is obtained by solving the unique solution of the simplified nonsingular matrix. Through simulation experiments, it is verified that the proposed algorithm has achieved better results in terms of congestion link determination and link loss rate estimation accuracy.

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Link Loss Rate Inference Using Success Rate Cumulant Generating Function

2009 International Conference on Future Networks ◽

10.1109/icfn.2009.19 ◽

2009 ◽

Author(s):

Chengbo Huang ◽

Yongsheng Liang ◽

Yilong Xu ◽

Guisheng Yi

Keyword(s):

Success Rate ◽

Generating Function ◽

Loss Rate ◽

Cumulant Generating Function ◽

Link Loss

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Review of Flap Monitoring Technology in 2020

Facial Plastic Surgery ◽

10.1055/s-0040-1721105 ◽

2020 ◽

Vol 36 (06) ◽

pp. 722-726

Author(s):

Adam Jacobson ◽

Oriana Cohen

Keyword(s):

Head And Neck ◽

Free Flap ◽

Loss Rate ◽

Early Recognition ◽

Postoperative Patient ◽

Free Flap Monitoring ◽

Improved Outcomes ◽

Vascular Compromise ◽

Flap Monitoring ◽

Multiple Levels

AbstractAdvances in free flap reconstruction of complex head and neck defects have allowed for improved outcomes in the management of head and neck cancer. Technical refinements have decreased flap loss rate to less than 4%. However, the potential for flap failure exists at multiple levels, ranging from flap harvest and inset to pedicle lay and postoperative patient and positioning factors. While conventional methods of free flap monitoring (reliant on physical examination) remain the most frequently used, additional adjunctive methods have been developed. Herein we describe the various modalities of both invasive and noninvasive free flap monitoring available to date. Still, further prospective studies are needed to compare the various invasive and noninvasive technologies and to propel innovations to support the early recognition of vascular compromise with the goal of even greater rates of flap salvage.

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