ATM scheduling with queuing delay predictions

1993 ◽  
Author(s):  
Daniel B. Schwartz
Keyword(s):  
Queuing Delay

The queuing delay analysis at relay node based on cooperative GBN-ARQ protocols

Optik ◽  
2014 ◽  
Vol 125 (19) ◽  
pp. 5893-5898 ◽  
Author(s):  
Cui Zhang ◽  
Shengye Huang ◽  
Qiusheng Zeng
Keyword(s):  
Relay Node ◽  
Delay Analysis ◽  
Queuing Delay

scholarly journals Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1449 ◽  
Author(s):  
Yulong Shi ◽  
Yang Zhang ◽  
Hans-Arno Jacobsen ◽  
Lulu Tang ◽  
Geoffrey Elliott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Loss Rate ◽  
Differentiated Services ◽  
Gradient Boosting ◽  
Queuing Delay ◽  
End To End Delay ◽  
Extreme Gradient Boosting ◽  
Real Value ◽  
Difficult Issue

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

Optimized Packet Filtering Honeypot with Snooping Agents in Intrusion Detection System for WLAN

2018 ◽  
Vol 12 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Gulshan Kumar ◽  
Rahul Saha ◽  
Mandeep Singh ◽  
Mritunjay Kumar Rai
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Wireless Lan ◽  
Network Performance ◽  
Detection System ◽  
Queuing Delay ◽  
Strongly Coupled ◽  
Network Intrusion ◽  
Simulation Scenario ◽  
The Impact

Wireless LAN networks are considered to be widely used and efficient infrastructure used in different domains of communication. In this paper, we worked on Network Intrusion Detection System (NIDS) to prevent intruder's activities by using snooping agents and honeypot on the network. The idea behind using snooping agents and honeypot is to provide network management in term of monitoring. Honey pot is placed just after the Firewall and intrusion system have strongly coupled synchronize with snooping agents Monitoring is considered at packet level and pattern level of the traffic. Simulation filtered and monitor traffic for highlight the intrusion in the network. Further attack sequence has been created and have shown the effects of attack sequence on scenario which have both honey pot and snoop agent with different network performance parameters like throughput, network load, queuing delay, retransmission attempt and packet. The simulation scenario shows the impact of attack on the network performance.

scholarly journals Multicast Utility Maximization with Load Balanc-ing in Private Cloud Environment

2018 ◽  
Vol 7 (2.19) ◽  
pp. 12
Author(s):  
N Senthamarai ◽  
M Vijayalakshmi
Keyword(s):  
Cloud Computing ◽  
Utility Maximization ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Cloud Environment ◽  
Selection Algorithm ◽  
Private Cloud ◽  
Queuing Delay ◽  
Routing Problem ◽  
Transmission Overhead

Multicast is a technique for one-to-many communication over the network. It plays important role in cloud computing and reduces the transmission overhead in the private cloud environment. In this paper, build an efficient multicast tree for the multicast routing problem in which a network consists of different categories of nodes, where each category can have one or more nodes of the same characteristic which is different from the characteristics of nodes from other categories. So it is used to reduce the message traffic in such a network, to build a multicast tree and minimize the queuing delay using multicast selection algorithm.

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