Relevant bibliographies by topics / Random early detection (RED)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Random early detection (RED)'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Random early detection (RED)"

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Chen, Jianyong, Cunying Hu, and Zhen Ji. "Self-Tuning Random Early Detection Algorithm to Improve Performance of Network Transmission." Mathematical Problems in Engineering 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/872347.

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We use a discrete-time dynamical feedback system model of TCP/RED to study the performance of Random Early Detection (RED) for different values of control parameters. Our analysis shows that the queue length is able to keep stable at a given target if the maximum probabilitypmax⁡and exponential averaging weightwsatisfy some conditions. From the mathematical analysis, a new self-tuning RED is proposed to improve the performance of TCP-RED network. The appropriatepmax⁡is dynamically obtained according to history information of bothpmax⁡and the average queue size in a period of time. Andwis properly chosen according to a linear stability condition of the average queue length. From simulations withns-2, it is found that the self-tuning RED is more robust to stabilize queue length in terms of less deviation from the target and smaller fluctuation amplitude, compared to adaptive RED, Random Early Marking (REM), and Proportional-Integral (PI) controller.
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Abdel-Jaber, Hussein, Fadi Thabtah, and Mike Woodward. "Modeling discrete-time analytical models based on random early detection: Exponential and linear." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 03 (2015): 1550028. http://dx.doi.org/10.1142/s1793962315500282.

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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.
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HO, CHARLOTTE YUK-FAN, BINGO WING-KUEN LING, and HERBERT H. C. IU. "SYMBOLIC DYNAMICAL MODEL OF AVERAGE QUEUE SIZE OF RANDOM EARLY DETECTION ALGORITHM." International Journal of Bifurcation and Chaos 20, no. 05 (2010): 1415–37. http://dx.doi.org/10.1142/s0218127410026575.

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In this paper, a symbolic dynamical model of the average queue size of the random early detection (RED) algorithm is proposed. The conditions for both the system parameters and the initial conditions that the average queue size of the RED algorithm would converge to a fixed point are derived. These results are useful for network engineers to design both the system parameters and the initial conditions so that internet networks can achieve a good performance.
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Abdel-Jaber, Hussein. "An Exponential Active Queue Management Method Based on Random Early Detection." Journal of Computer Networks and Communications 2020 (May 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8090468.

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Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.
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Bhatnagar, Shalabh, and Rajesh Patro. "A proof of convergence of the B-RED and P-RED algorithms for random early detection." IEEE Communications Letters 13, no. 10 (2009): 809–11. http://dx.doi.org/10.1109/lcomm.2009.091276.

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Sharma, Saurabh, Dipti Jindal, and Rashi Agarwal. "Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-hoc Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1305. http://dx.doi.org/10.11591/ijece.v8i3.pp1305-1314.

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This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.
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Jiang, Xianliang, Jiangang Yang, Guang Jin, and Wei Wei. "RED-FT: A Scalable Random Early Detection Scheme with Flow Trust against DoS Attacks." IEEE Communications Letters 17, no. 5 (2013): 1032–35. http://dx.doi.org/10.1109/lcomm.2013.022713.122652.

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Mahmood Lafta, Wisam, Saba Qasim Jabbar, and Guangzhi Ma. "Performance Evaluation of Heterogeneous Network Based on RED and WRED." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 540. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp540-545.

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<p>The developing of wireless networks becomes a very important issue nowadays, since it is considered as an easy-using tool without building new infrastructure to cover a wide working area. Applying TCP protocols with the application demands are implemented this work by considering heterogeneous environment of wireless networks connecting with wired networks. The TCP congestion is critical problem is faced heterogeneous environments, this problem is appeared through sending and receiving huge data from wireless networks to wired networks and vice versa. This work is proposed a new approach of using active queue management (AQM) technique with random early detection (RED) and weight random early detection (WRED) strategies to avoid the expected congestion between the heterogeneous environments. Our simulation results show that the quality of service (QoS) is improved by reducing the queue delay and buffer usage, and by increasing the average throughput and utilizationof the system. The simulation is carried out by using OPNET software to test the proposed models for different scenarios.</p>
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Kurdi, Heba, Amal Al-Aldawsari, Isra Al-Turaiki, and Abdulrahman S. Aldawood. "Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining." Plants 10, no. 1 (2021): 95. http://dx.doi.org/10.3390/plants10010095.

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In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.
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Abbas, G., A. K. Nagar, H. Tawfik, and J. Y. Goulermas. "Pricing and Unresponsive Flows Purging for Global Rate Enhancement." Journal of Electrical and Computer Engineering 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/379652.

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Pricing-based Active Queue Management (AQM), such as Random Exponential Marking (REM), outperforms other probabilistic counterpart techniques, like Random Early Detection (RED), in terms of both high utilization and negligible loss and delay. However, the pricing-based protocols do not take account of unresponsive flows that can significantly alter the subsequent rate allocation. This letter presents Purge (Pricing and Un-Responsive flows purging for Global rate Enhancement) that extends the REM framework to regulate unresponsive flows. We show that Purge is effective at providing fairness and requires small memory and low-complexity operations.
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Dissertations / Theses on the topic "Random early detection (RED)"

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Movsesyan, Aleksandr. "Reliable Ethernet." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/602.

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Networks within data centers, such as connections between servers and disk arrays, need lossless flow control allowing all packets to move quickly through the network to reach their destination. This paper proposes a new algorithm for congestion control to satisfy the needs of such networks and to answer the question: Is it possible to provide circuit-less reliability and flow control in an Ethernet network? TCP uses an end-to-end congestion control algorithm, which is based on end-to-end round trip time (RTT). Therefore its flow control and error detection/correction approach is dependent on end-to-end RTT. Other approaches utilize specialized data link layer networks such as InfiniBand and Fibre Channel to provide network reliability. The algorithm proposed in this thesis builds on the ubiquitous Ethernet protocol to provide reliability at the data link layer without the overhead and cost of the specialized networks or the delay induced by TCP’s end-to-end approach. This approach requires modifications to the Ethernet switches to implement a back pressure based flow control algorithm. This back pressure algorithm utilizes a modified version of the Random Early Detection (RED) algorithm to detect congestion. Our simulation results show that the algorithm can quickly recover from congestion and that the average latency of the network is close to the average latency when no congestion is present. With correct threshold and alpha values, buffer sizes in the network and on the source nodes can be kept small to allow little needed additional hardware to implement the system.
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Li, Zhi. "Fuzzy logic based robust control of queue management and optimal treatment of traffic over TCP/IP networks." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001461/.

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Improving network performance in terms of efficiency, fairness in the bandwidth, and system stability has been a research issue for decades. Current Internet traffic control maintains sophistication in end TCPs but simplicity in routers. In each router, incoming packets queue up in a buffer for transmission until the buffer is full, and then the packets are dropped. This router queue management strategy is referred to as Drop Tail. End TCPs eventually detect packet losses and slow down their sending rates to ease congestion in the network. This way, the aggregate sending rate converges to the network capacity. In the past, Drop Tail has been adopted in most routers in the Internet due to its simplicity of implementation and practicability with light traffic loads. However Drop Tail, with heavy-loaded traffic, causes not only high loss rate and low network throughput, but also long packet delay and lengthy congestion conditions. To address these problems, active queue management (AQM) has been proposed with the idea of proactively and selectively dropping packets before an output buffer is full. The essence of AQM is to drop packets in such a way that the congestion avoidance strategy of TCP works most effectively. Significant efforts in developing AQM have been made since random early detection (RED), the first prominent AQM other than Drop Tail, was introduced in 1993. Although various AQMs also tend to improve fairness in bandwidth among flows, the vulnerability of short-lived flows persists due to the conservative nature of TCP. It has been revealed that short-lived flows take up traffic with a relatively small percentage of bytes but in a large number of flows. From the user’s point of view, there is an expectation of timely delivery of short-lived flows. Our approach is to apply artificial intelligence technologies, particularly fuzzy logic (FL), to address these two issues: an effective AQM scheme, and preferential treatment for short-lived flows. Inspired by the success of FL in the robust control of nonlinear complex systems, our hypothesis is that the Internet is one of the most complex systems and FL can be applied to it. First of all, state of the art AQM schemes outperform Drop Tail, but their performance is not consistent under different network scenarios. Research reveals that this inconsistency is due to the selection of congestion indicators. Most existing AQM schemes are reliant on queue length, input rate, and extreme events occurring in the routers, such as a full queue and an empty queue. This drawback might be overcome by introducing an indicator which takes account of not only input traffic but also queue occupancy for early congestion notification. The congestion indicator chosen in this research is traffic load factor. Traffic load factor is in fact dimensionless and thus independent of link capacity, and also it is easy to use in more complex networks where different traffic classes coexist. The traffic load indicator is a descriptive measure of the complex communication network, and is well suited for use in FL control theory. Based on the traffic load indicator, AQM using FL – or FLAQM – is explored and two FLAQM algorithms are proposed. Secondly, a mice and elephants (ME) strategy is proposed for addressing the problem of the vulnerability of short-lived flows. The idea behind ME is to treat short-lived flows preferably over bulk flows. ME’s operational location is chosen at user premise gateways, where surplus processing resources are available compared to other places. By giving absolute priority to short-lived flows, both short and long-lived flows can benefit. One problem with ME is starvation of elephants or long-lived flows. This issue is addressed by dynamically adjusting the threshold distinguishing between mice and elephants with the guarantee that minimum capacity is maintained for elephants. The method used to dynamically adjust the threshold is to apply FL. FLAQM is deployed to control the elephant queue with consideration of capacity usage of mice packets. In addition, flow states in a ME router are periodically updated to maintain the data storage. The application of the traffic load factor for early congestion notification and the ME strategy have been evaluated via extensive experimental simulations with a range of traffic load conditions. The results show that the proposed two FLAQM algorithms outperform some well-known AQM schemes in all the investigated network circumstances in terms of both user-centric measures and network-centric measures. The ME strategy, with the use of FLAQM to control long-lived flow queues, improves not only the performance of short-lived flows but also the overall performance of the network without disadvantaging long-lived flows.
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Cheng, Wijian. "Automatic Red Tide Detection using MODIS Satellite Images." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3772.

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Red tides pose a significant economic and environmental threat in the Gulf of Mexico. Detecting red tide is important for understanding this phenomenon. In this thesis, machine learning approaches based on Random Forests, Support Vector Machines and K-Nearest Neighbors have been evaluated for red tide detection from MODIS satellite images. Detection results using machine learning algorithms were compared to ship collected ground truth red tide data. This work has three major contributions. First, machine learning approaches outperformed two of the latest thresholding red tide detection algorithms based on bio-optical characterization by more than 10% in terms of F measure and more than 4% in terms of area under the ROC curve. Machine Learning approaches are effective in more locations on the West Florida Shelf. Second, the thresholds developed in recent thresholding methods were introduced as input attributes to the machine learning approaches and this strategy improved Random Forests and KNearest Neighbors approaches' F-measures. Third, voting the machine learning and thresholding methods could achieve the better performance compared with using machine learning alone, which implied a combination between machine learning models and biocharacterization thresholding methods can be used to obtain effective red tide detection results.
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Tinnakornsrisuphap, Peerapol. "Dynamics of random early detection gateway under a large number of TCP flows." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/200.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Ghimire, Rajiv, and Mustafa Noor. "Evaluation and Optimization of Quality of Service (QoS) In IP Based Networks." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3920.

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The purpose of this thesis is to evaluate and analyze the performance of RED (Random Early Detection) algorithm and our proposed RED algorithm. As an active queue management RED has been considered an emerging issue in the last few years. Quality of service (QoS) is the latest issue in today’s internet world. The name QoS itself signifies that special treatment is given to the special traffic. With the passage of time the network traffic grew in an exponential way. With this, the end user failed to get the service for what they had paid and expected for. In order to overcome this problem, QoS within packet transmission came into discussion in internet world. RED is the active queue management system which randomly drops the packets whenever congestion occurs. It is one of the active queue management systems designed for achieving QoS. In order to deal with the existing problem or increase the performance of the existing algorithm, we tried to modify RED algorithm. Our purposed solution is able to minimize the problem of packet drop in a particular duration of time achieving the desired QoS. An experimental approach is used for the validation of the research hypothesis. Results show that the probability of packet dropping in our proposed RED algorithm during simulation scenarios significantly minimized by early calculating the probability value and then by calling the pushback mechanism according to that calculated probability value.<br>+46739567385(Rajiv), +46762125426(Mustafa)
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Abdel-Jaber, Hussein F. "Performance Modelling and Evaluation of Active Queue Management Techniques in Communication Networks. The development and performance evaluation of some new active queue management methods for internet congestion control based on fuzzy logic and random early detection using discrete-time queueing analysis and simulation." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4261.

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Since the field of computer networks has rapidly grown in the last two decades, congestion control of traffic loads within networks has become a high priority. Congestion occurs in network routers when the number of incoming packets exceeds the available network resources, such as buffer space and bandwidth allocation. This may result in a poor network performance with reference to average packet queueing delay, packet loss rate and throughput. To enhance the performance when the network becomes congested, several different active queue management (AQM) methods have been proposed and some of these are discussed in this thesis. Specifically, these AQM methods are surveyed in detail and their strengths and limitations are highlighted. A comparison is conducted between five known AQM methods, Random Early Detection (RED), Gentle Random Early Detection (GRED), Adaptive Random Early Detection (ARED), Dynamic Random Early Drop (DRED) and BLUE, based on several performance measures, including mean queue length, throughput, average queueing delay, overflow packet loss probability, packet dropping probability and the total of overflow loss and dropping probabilities for packets, with the aim of identifying which AQM method gives the most satisfactory results of the performance measures. This thesis presents a new AQM approach based on the RED algorithm that determines and controls the congested router buffers in an early stage. This approach is called Dynamic RED (REDD), which stabilises the average queue length between minimum and maximum threshold positions at a certain level called the target level to prevent building up the queues in the router buffers. A comparison is made between the proposed REDD, RED and ARED approaches regarding the above performance measures. Moreover, three methods based on RED and fuzzy logic are proposed to control the congested router buffers incipiently. These methods are named REDD1, REDD2, and REDD3 and their performances are also compared with RED using the above performance measures to identify which method achieves the most satisfactory results. Furthermore, a set of discrete-time queue analytical models are developed based on the following approaches: RED, GRED, DRED and BLUE, to detect the congestion at router buffers in an early stage. The proposed analytical models use the instantaneous queue length as a congestion measure to capture short term changes in the input and prevent packet loss due to overflow. The proposed analytical models are experimentally compared with their corresponding AQM simulations with reference to the above performance measures to identify which approach gives the most satisfactory results. The simulations for RED, GRED, ARED, DRED, BLUE, REDD, REDD1, REDD2 and REDD3 are run ten times, each time with a change of seed and the results of each run are used to obtain mean values, variance, standard deviation and 95% confidence intervals. The performance measures are calculated based on data collected only after the system has reached a steady state. After extensive experimentation, the results show that the proposed REDD, REDD1, REDD2 and REDD3 algorithms and some of the proposed analytical models such as DRED-Alpha, RED and GRED models offer somewhat better results of mean queue length and average queueing delay than these achieved by RED and its variants when the values of packet arrival probability are greater than the value of packet departure probability, i.e. in a congestion situation. This suggests that when traffic is largely of a non bursty nature, instantaneous queue length might be a better congestion measure to use rather than the average queue length as in the more traditional models.
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Duchemin, Tom. "Méthodologie d’analyse et de surveillance pour la prévention des arrêts maladie Hierarchizing Determinants of Sick Leave Insights From a Survey on Health and Well-being at the Workplace Response to Predictors of Long-Term Sick Leave in the Workplace Modeling sickness absence data : a scoping review A statistical algorithm for outbreak detection in a multi-site setting : the case of sick leave monitoring Monitoring sick leave data for early detection of influenza outbreaks." Thesis, Paris, HESAM, 2020. http://www.theses.fr/2020HESAC027.

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Alors que les arrêts maladie sont le signe d’un mal-être croissant chez les salariés et qu’ils pèsent un coût certain pour la collectivité, la numérisation et le partage systématique des données offrent de belles opportunités pour leur prévention. Nous avons ainsi profité de cette opportunité pour développer un éventail d’outils de prévention basés sur des méthodes d’analyse statistique. Dans un premier temps, ces travaux de thèse proposent une analyse des mécanismes expliquant les arrêts maladie chez le salarié. L’analyse d’une enquête nationale a premièrement permis d’identifier et de hiérarchiser leurs principaux facteurs déterminants grâce à l’algorithme des forêts aléatoires. Ensuite, une analyse de données administratives a identifié des trajectoires d’absence pouvant mener à des arrêts graves grâce à des analyses séquentielles et à de la modélisation multi-état. Dans un second temps, des outils ont été développés afin d’identifier des situations anormales d’arrêt maladie à l’échelle de l’entreprise. Une typologie d’entreprise a premièrement été construite afin de produire des valeurs repère pour que les entreprises évaluent précisément leur situation. Un algorithme de détection des pics d’absence, adapté de modèles de surveillance épidémiologique, a enfin été développé pour pouvoir identifier automatiquement les entreprises en excès<br>At a time when sick leave is a sign of growing ill-being for workers and a cost burden for the society, the systematic digitalization and distribution of data offers great opportunities for its prevention. We have therefore taken advantage of this opportunity to develop a range of prevention tools based on statistical analysis methods. In a first part, this work proposes an analysis of the mechanisms explaining sick leave among workers. The analysis of a national survey has first identified and prioritised their main determinants using random forest. Then, an analysis of administrative data had helped to identify absence trajectories that could lead to serious sick leaves thanks to sequential analyses and multi-state modelling. In a second step, tools were developed to identify abnormal situations of sick leave at company level. A company typology was first built to produce benchmark values for companies to accurately assess their situation. Finally, an algorithm for identifying absence peaks, adapted from epidemiological surveillance models, was finally developed to automatically identify companies in difficulty
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Jones, Christina Michele. "Applications and challenges in mass spectrometry-based untargeted metabolomics." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54830.

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Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.
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Lin, Wei-Chung, and 林偉正. "An Improved Random Early Detection (RED) Algorithm for Congestion Control." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/16896950377494718155.

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碩士<br>國立中興大學<br>資訊科學系所<br>95<br>Many proposals have been adopted in controlling the congestions in the routers, including Random Early Detection (RED) and Drop-tail, and have shown to improve the loss rate, throughput, fairness, etc. of the network. Although RED algorithm is designed for TCP for a active queue management, we found that when comes to dropping the packets, it treats packets equally, ignoring the effect of the the size of the packets. This results in higher loss rate of packets and lower throughput for smaller packets. In this thesis, we propose to improve the original RED algorithm by differentiating packet sizes and devise RED_average algorithm and further improved PS_average algorithm. We then use ns-2 to simulate the performance of the aforementioned three algorithm.based on three MTU sizes. The results showed that if we take the factor of the packet size into consideration, the RED_average algorithm has a better loss rate and throughput. The PS_average, which takes the average packet size into consideration to adjust the intended loss rate for smaller packates, has a even further improved performance. We have shown that by the above two new algorithms, a better balance for the loss rate for all packets can be achieved, and thus improved utilization of the network resources.
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Vaidya, Rahul. "Online Optimization Of RED Routers." Thesis, 2004. http://etd.iisc.ernet.in/handle/2005/1134.

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Books on the topic "Random early detection (RED)"

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Wu, Kana, NaNa Keum, Reiko Nishihara, and Edward L. Giovannucci. Cancers of the Colon and Rectum. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0036.

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Worldwide, colorectal cancer (CRC) is the third most common cancer in men and second in women, with annual estimates of 1.4 million newly diagnosed cases and over 690,000 deaths. Incidence rates relate closely to economic development. Although incidence rates have stabilized at a high level in most economically developed countries, they continue to increase in many traditionally low-risk countries, following the uptake of Western patterns of diet and physical inactivity. In principle, CRC is among the most preventable of all common cancers. Potentially modifiable risk factors include obesity, physical inactivity, high intake of red or processed meat, tobacco smoking, and heavy alcohol use. Several screening tests effectively reduce both the incidence and death rates of CRC through the detection of precancerous lesions and the treatment of early stage cancers. Despite the preventability of CRC, incidence rates over the last twenty years have decreased in only a few countries.
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Book chapters on the topic "Random early detection (RED)"

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Sharma, Neelam, Shyam Singh Rajput, Amit Kumar Dwivedi, and Manish Shrimali. "P-RED: Probability Based Random Early Detection Algorithm for Queue Management in MANET." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3773-3_62.

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Gan, Yung-Sze, and Chen-Khong Tham. "Random Early Detection Assisted Layered Multicast." In Management of Multimedia on the Internet. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45812-3_27.

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Upadhyay, Anand, Umesh Palival, and Sumit Jaiswal. "Early Brain Tumor Detection Using Random Forest Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49339-4_26.

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Byun, Do Jun, and John S. Baras. "Adaptive Virtual Queue Random Early Detection in Satellite Networks." In Wireless Technology. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-71787-6_4.

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Adamu, Aminu, Vsevolod Shorgin, Sergey Melnikov, and Yuliya Gaidamaka. "Flexible Random Early Detection Algorithm for Queue Management in Routers." In Distributed Computer and Communication Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66471-8_16.

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Hailong, Li, Tan Wei Liak, Li-Jin Thng Ian, and Li Xiaorong. "Fiber Delay Line-Random Early Detection QoS Scheme for Optical Burst Switching Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25969-5_69.

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Abdel-jaber, Hussein, Jafar Ababneh, Fadi Thabtah, Amjad M. Daoud, and Mahmoud Baklizi. "Performance Analysis of the Proposed Adaptive Gentle Random Early Detection Method under NonCongestion and Congestion Situations." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22603-8_52.

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Dash, Prasant Kumar, Sukant Kishoro Bisoy, Narendra Kumar Kamila, and Madhumita Panda. "Parameter Setting and Stability of PI Controller for AQM Router." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0501-3.ch015.

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AQM is used to signal congestion early by dropping packets before the buffer becomes completely full. It has following goals: small queuing delays, low packets losses and high link utilization. This work investigates the dynamic of Proportional Integral (PI) controller in the presence of large FTP flows in homogeneous scenario. We systematically derive quantitative guidelines and set the parameters as a function of the scenario parameters bottleneck-bandwidth, round-trip-time, number of TCP flows in order to avoid severe oscillation of the queue size. To compare the best of PI we have considered Random Early Detection (RED), Random Exponential Marking (REM) and Adaptive Virtual Queue (AVQ) as active queue management schemes. Then we analyzed the performance of PI with DropTail, REM and AVQ in wired-cum-wireless network. Our result shows that, properly configuring the parameter of PI can stabilize queue length under dynamic environments and retain good performance even if the network parameters, such as the number of flows N, target queue length, round-trip time RTT.
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Gambhir, Shalini, Yugal Kumar, Sanjay Malik, Geeta Yadav, and Amita Malik. "Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches." In Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7131-5.ch003.

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Classification schemes have been applied in the medical arena to explore patients' data and extract a predictive model.This model helps doctors to improve their prognosis, diagnosis, or treatment planning processes.The aim of this work is to utilize and compare different decision tree classifiers for early diagnosis of Dengue. Six approaches, mainly J48 tree, random tree, REP tree, SOM, logistic regression, and naïve Bayes, have been utilized to study real-world Dengue data collected from different hospitals in the Delhi, India region during 2015-2016. Standard statistical metrics are used to assess the efficiency of the proposed Dengue disease diagnostic system, and the outcomes showed that REP tree is best among these classifiers with 82.7% efficient in supplying an exact diagnosis.
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Bisoy, Sukant Kishoro, Prasant Kumar Pattnaik, and Narendra Kumar Kamila. "Throughput and Compatibility Analysis of TCP Variants in Heterogeneous Environment." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0501-3.ch011.

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When TCP Reno and TCP Vegas connections share a link, TCP Reno generally steals more bandwidth and dominates TCP Vegas because of its aggressive nature. This is the major reason why TCP Vegas has not gained much popularity and deployment in the Internet despite its excellent standalone performance. This work systematically examines compatibility between Reno and Vegas in wired as well as in wireless networks. Popular Active Queue Management (AQM) technique named as Random Early Detection (RED) to minimize the incompatibility between Reno and Vegas in wired network. For wireless network two ad hoc routing protocols such as Ad Hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance Vector (DSDV) are considered. Simulation results show that the incompatibility between Reno and Vegas in wired network is minimized using popular RED techniques. But in wireless ad hoc network environment Reno's aggressive behavior gets deteriorated while sharing with Vegas. Moreover, Reno and Vegas are more compatible in wireless network than wired network when both coexist in same time.
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Conference papers on the topic "Random early detection (RED)"

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Manjunath, Sreelakshmi, and Gaurav Raina. "Analyses of compound TCP with Random Early Detection (RED) queue management." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162875.

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A. Akintola, A., G. A. Aderounmu, L. A. Akanbi, and M. O. Adigun. "Modeling and Performance Analysis of Dynamic Random Early Detection (DRED) Gateway for Congestion Avoidance." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2920.

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One of the most prominent congestion avoidance schemes in the Internet architecture is the Random Early Detection (RED) algorithm. Several modifications and enhancements have been made to the original RED so as to make it more responsive to congestion avoidance at the gateways. In this paper, we introduced the Dynamic Random Early Detection (DRED) model, which uses a newly introduced parameter i.e. warning line. A robust and efficacious technique to measure the burstiness of incoming traffic has been developed and tested. This involves the estimation of the average queue size, avg, which is dynamically adjusted hence the name of our scheme. The empirical results obtained from the simulations show that our DRED scheme responds early enough to the increased number of packets at the gateway. Also, the maximum drop probability of packets show improved performance over the original RED. It was concluded that our scheme demonstrated superiority by avoiding global synchronization and there is great reduction in the fluctuations of the actual queue size. Also, its early response avoids buffer overflow at the gateways when the queue is near full.
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Siregar, B., M. S. Manik, R. Rahmat, U. Andayani, and F. Fahmi. "Implementation of network monitoring and packets capturing using random early detection (RED) method." In 2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat). IEEE, 2017. http://dx.doi.org/10.1109/comnetsat.2017.8263571.

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Chhabra, Kiran, Manali Kshirsagar, and Arun Zadgaonkar. "Performance improvement of RED: Random early detection using input sensitivity with threshold modification." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253707.

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Govindaswamy, Visvasuresh, Gergely Zaruba, and G. Balasekaran. "Receiver-Window Modified Random Early Detection (RED-RWM) Active Queue Management Scheme: Modeling and Analysis." In 2006 IEEE International Conference on Communications. IEEE, 2006. http://dx.doi.org/10.1109/icc.2006.254721.

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Rajput, Shyam Singh, Vinod Kumar, and Santosh Kumar Paul. "Comparative analysis of random early detection (RED) and virtual output queue (VOQ) algorithms in differentiated services network." In 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2014. http://dx.doi.org/10.1109/spin.2014.6776954.

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Lin, Xiao-Hui, Kai-Yu Zhou, Hui Wang, and Gong-Cao Su. "Scalable Fair Random Early Detection." In 2006 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2006. http://dx.doi.org/10.1109/wicom.2006.372.

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Lin, Dong, and Robert Morris. "Dynamics of random early detection." In the ACM SIGCOMM '97 conference. ACM Press, 1997. http://dx.doi.org/10.1145/263105.263154.

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Hendrawan and Prima Hernandia. "Random Early Detection utilizing genetics algorithm." In 2014 8th International Conference on Telecommunication Systems Services and Applications (TSSA). IEEE, 2014. http://dx.doi.org/10.1109/tssa.2014.7065952.

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Yang, Lin, Jin Zhu, Wanqing Xie, and Xiaobin Tan. "Stable tuning for random early detection algorithm." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895874.

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Reports on the topic "Random early detection (RED)"

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Deshpande, Alina. RED Alert – Early warning or detection of global re-emerging infectious disease (RED). Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1261795.

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Perkins, Dustin. Invasive exotic plant monitoring at Colorado National Monument: 2019 field season. Edited by Alice Wondrak Biel. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2286650.

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Invasive exotic plant (IEP) species are a significant threat to natural ecosystem integrity and biodiversity, and controlling them is a high priority for the National Park Service. The North-ern Colorado Plateau Network (NCPN) selected the early detection of IEPs as one of 11 monitoring protocols to be implemented as part of its long-term monitoring program. This report represents work completed at Colorado National Monument during 2019. During monitoring conducted June 12–19, a total of 20 IEP species were detected on monitoring routes and transects. Of these, 12 were priority species that accounted for 791 separate IEP patches. IEPs were most prevalent along riparian areas. Yellow sweetclover (Melilotis officinale) and yellow salsify (Tragopogon dubius) were the most commonly detected priority IEPs along monitoring routes, representing 73% of all priority patches. Patches of less than 40 m2 were typical of nearly all priority IEP species except yellow sweetclover. A patch management index (PMI) was created by combining patch size class and percent cover for each patch. In 2019, a large majority of priority IEP patches were assigned a PMI score of low (46%) or very low (50%), indicating small and/or sparse patches where control is generally still feasible. This is similar to the numbers for 2017, when 99% of patches scored low or very low in PMI. Seventy-eight percent of tree patches were classified as seedlings or saplings, which require less effort to control than mature trees. Cheatgrass (Anisantha tectorum) was the most common IEP recorded in transects, found in 30–77% of transects across the different routes. It was the only species found in transects on all monitoring routes. When treated and untreated extra areas near the West Entrance were compared, the treated area had comparable or higher lev-els of IEPs than the untreated area. When segments of monitoring routes conducted between 2003 and 2019 were compared, results were mixed, due to the different species monitored in different time periods. But in general, the number of IEPs per 100 meters is increasing or remaining constant over time. There were notable increases in IEP patches per 100 meters on several routes in 2019: field bindweed (Convolvulus arvensis) along East Glade Park Road; Siberian elm (Ulmus pumila) in Red Canyon; yellow salsify along East Glade Park Road, No Thoroughfare Canyon, No Thoroughfare Trail, and Red Canyon; and yellow sweetclover in No Thoroughfare Canyon and Red Canyon. Network staff will return to re-sample monitoring routes in 2021.
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