This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two pe...This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.展开更多
Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue l...Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue length”is set below the buffer’s“minimum threshold”position which makes the router buffer quickly overflow.To deal with this issue,this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure.This assigns mean queue length(mql)and average queueing delay smaller values than those for RED and eventually reduces buffers overflow.A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance.The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists.This work also compares one of the proposed models(RED-Linear)with another analytical model named threshold-based linear reduction of arrival rate(TLRAR).The results of the mql,average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs,whereas,the results of our RED-Linear were not impacted and this shows superiority of our model.展开更多
Congestion is one of the well-studied problems in computer networks,which occurs when the request for network resources exceeds the buffer capacity.Many active queue management techniques such as BLUE and RED have bee...Congestion is one of the well-studied problems in computer networks,which occurs when the request for network resources exceeds the buffer capacity.Many active queue management techniques such as BLUE and RED have been proposed in the literature to control congestions in early stages.In this paper,we propose two discrete-time queueing network analytical models to drop the arrival packets in preliminary stages when the network becomes congested.The first model is based on Lambda Decreasing and it drops packets from a probability value to another higher value according to the buffer length.Whereas the second proposed model drops packets linearly based on the current queue length.We compare the performance of both our models with the original BLUE in order to decide which of these methods offers better quality of service.The comparison is done in terms of packet dropping probability,average queue length,throughput ratio,average queueing delay,and packet loss rate.展开更多
Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sha...Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items(attribute values)in the training data instances.In response to the above two issues,a new dynamic rule induction(DRI)method is proposed.Whenever a rule is produced and its related training data instances are discarded,DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion.Therefore,the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM.This enables DRI to generate near perfect rules and realistic classifiers.Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.展开更多
文摘This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.
文摘Congestion control is among primary topics in computer network in which random early detection(RED)method is one of its common techniques.Nevertheless,RED suffers from drawbacks in particular when its“average queue length”is set below the buffer’s“minimum threshold”position which makes the router buffer quickly overflow.To deal with this issue,this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure.This assigns mean queue length(mql)and average queueing delay smaller values than those for RED and eventually reduces buffers overflow.A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance.The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists.This work also compares one of the proposed models(RED-Linear)with another analytical model named threshold-based linear reduction of arrival rate(TLRAR).The results of the mql,average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs,whereas,the results of our RED-Linear were not impacted and this shows superiority of our model.
文摘Congestion is one of the well-studied problems in computer networks,which occurs when the request for network resources exceeds the buffer capacity.Many active queue management techniques such as BLUE and RED have been proposed in the literature to control congestions in early stages.In this paper,we propose two discrete-time queueing network analytical models to drop the arrival packets in preliminary stages when the network becomes congested.The first model is based on Lambda Decreasing and it drops packets from a probability value to another higher value according to the buffer length.Whereas the second proposed model drops packets linearly based on the current queue length.We compare the performance of both our models with the original BLUE in order to decide which of these methods offers better quality of service.The comparison is done in terms of packet dropping probability,average queue length,throughput ratio,average queueing delay,and packet loss rate.
文摘Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items(attribute values)in the training data instances.In response to the above two issues,a new dynamic rule induction(DRI)method is proposed.Whenever a rule is produced and its related training data instances are discarded,DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion.Therefore,the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM.This enables DRI to generate near perfect rules and realistic classifiers.Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.
