RED Algorithms Research Articles

An Algorithmically Woven Community: Disclosing Mental Illness and Connecting with Similar Others on an Algorithm-Mediated Platform in China

Nowadays, algorithms are extensively used on social platforms for content recommendations and user connections. In this study, through semi-structured interviews with 22 China-based users of RED (i.e., an algorithm-mediated platform similar to TikTok), we investigate how users who have experienced mental illnesses (depression, anxiety, and bipolar disorder) understand RED algorithms and how these algorithms shape their self-disclosure, imagined audience, and community building. Specifically, both the norms of self-disclosure and content recipients are governed by algorithms, while users with limited agency form various folk theories to navigate the process. Moreover, based on accurate content recommendations, the algorithms of RED enable users who have experienced mental illnesses to connect with similar others across the platform. By proposing the concept of an “algorithmically woven community,” we conceptualize and visualize a novel mechanism of how an algorithm works akin to a responsive authority to weave a loosely knit, decentralized, and boundless network.

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data

Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.

Multi-indicator Active Queue Management Method

A considerable number of applications are running over IP networks. This increased the contention on the network resource, which ultimately results in congestion. Active queue management (AQM) aims to reduce the serious consequences of network congestion in the router buffer and its negative effects on network performance. AQM methods implement different techniques in accordance with congestion indicators, such as queue length and average queue length. The performance of the network is evaluated using delay, loss, and throughput. The gap between congestion indicators and network performance measurements leads to the decline in network performance. In this study, delay and loss predictions are used as congestion indicators in a novel stochastic approach for AQM. The proposed method estimates the congestion in the router buffer and then uses the indicators to calculate the dropping probability, which is responsible for managing the router buffer. The experimental results, based on two sets of experiments, have shown that the proposed method outperformed the existing benchmark algorithms including RED, ERED and BLUE algorithms. For instance, in the first experiment, the proposed method resides in the third-place in terms of delay when compared to the benchmark algorithms. In addition, the proposed method outperformed the benchmark algorithms in terms of packet loss, packet dropping, and packet retransmission. Overall, the proposed method outperformed the benchmark algorithms because it preserves packet loss while maintaining reasonable queuing delay.

Comparative Analysis of Drop Tail, Red and NLRED Congestion Control Algorithms Using NS2 Simulator

Comparative Analysis of Drop Tail, Red and NLRED Congestion Control Algorithms Using NS2 Simulator

A Comparative Study of BLUE, RED and REM AQM Algorithms

A Comparative Study of BLUE, RED and REM AQM Algorithms

Методы анализа динамических структур телекоммуникационных систем

Mathematical models of connectivity of the system associated with the network structures for their reliability are considered. For static systems in where the connection matrix consists of the probability characteristics, the rate of the connection is defined as a stochastic estimation. For complex systems, the calculation of this index has the NP-complexity. Therefore, the evaluation of connectivity is calculated from the approximate methods. Such techniques such as assessment Ezary-Proschan and Polesski and others are known. For dynamic systems the connected component is of fundamental importance because it is the mutual coupling between elements of the system ensure the acquisition highly-integrated properties of emergence. As a model of a dynamic system the equation of state of a shaping filter or basic differential model Chebotarev-Agaev is considered. Besides, the latter is adequately reduced to an equation of stochastic approximation, a simplified version of which is used in telecommunication technologies to assess the round-trip time RTT, RED algorithms and others. Evaluation of the status and results of the analysis for the different connections between the elements are presented.

Active Queue Management Based on State Variable Feedback Control

A new AQM algorithm based on state variable feedback control (SPC) is proposed. The future dynamic queue length in data buffer predicted by state space model is successfully introduced into feedback data's advanced prediction to compensate feedback delay. Finally, the control requirement of congestion is converted to optimal control objective function, and drop probability is obtained by solving the optimal problem. The simulation results show that the queue length with SPAQM algorithm reaches the desired value with minimal tracking error and lower drop probability. SPAQM algorithm has better performance than PID algorithms and RED algorithms in terms of disturbance rejection, stability, and robustness.

Performance Assessment of AGRED, RED and GRED Congestion Control Algorithms

Performance Assessment of AGRED, RED and GRED Congestion Control Algorithms

Cochlodinium polykrikoides red tide detection in the South Sea of Korea using spectral classification of MODIS data

To distinguish true red tide water (particularly Cochlodinium polykrikoides blooms) from non-red tide water (false satellite high chlorophyll water) in the South Sea of Korea, we developed a systematic classification method using spectral information from MODIS level products and applied it to five different harmful algal bloom events. Red tide and nonred tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 678 nm. The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio in areas with lower and higher contributions of CDOM to chlorophyll, respectively. After applying the red tide classification (using the four criteria), the spectral response of the red tide water, which is influenced by pigment concentration, showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water, due to decreasing phytoplankton absorption and increasing detritus/CDOM absorption at blue bands. The results were well matched with the discoloration of water (blue to dark red/brown) and delineated the areal coverage of C. polykrikoides blooms, revealing the nature of spatial and temporal variations in red tides. This simple spectral classification method led to increase user accuracy for C. polykrikoides and non-red tide blooms (>46% and >97%) and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, chlorophyll anomaly, fluorescence analysis, or proposed red tide detection algorithms.

Design and analysis of a model predictive controller for active queue management

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness.

STOCHASTICALLY SCALABLE FLOW CONTROL

Recent advances in the mathematical analysis of flow control have prompted the creation of the Scalable TCP (STCP) and Exponential RED (E-RED) algorithms. These are designed to be scalable under the popular deterministic delay stability modeling framework. In this article, we analyze stochastic models of STCP and STCP combined with E-RED link behavior. We find that under certain plausible network conditions, these probabilistic models also exhibit scalable behavior. In particular, we derive parameter choice schemes for which the equilibrium coefficients of variation of flow rates are bounded, however large, fast, or complex the network. Our model is shown to exhibit behavior similar to the mean field convergence that has recently been observed in TCP.

Congestion Control for Mixed TCP and UDP Traffic

In this letter, we propose a protocol sensitive random early detection algorithm for active queue management to improve fairness between TCP and UDP flows and to reduce delay time with small overheads. The algorithm classifies the packets into responsive and unresponsive flows, and applies the RED algorithm individually to each classified group. Using ns-2 simulations, we showed the effectiveness of the proposed PSRED algorithm compared with several well-known AQM schemes, such as RED and RED-PD algorithms.

Virtual rate control algorithm for active queue management in TCP networks

A virtual rate control (VRC) algorithm for active queue management (AQM) to regulate the queue length with small variation and to achieve high utilisation with small packet loss is proposed. Through ns simulations, the effectiveness of the proposed VRC algorithm as compared with several well-known AQM schemes such as RED, REM, and AVQ algorithms is shown.

FRED - fair random early detection algorithm for TCP over ATM networks

The authors present the FRED (fair random early detection) algorithm as a congestion control mechanism for TCP over ATM networks. The FRED algorithm enhances the RED gateway algorithm, by using the fact that TCP connections should be allocated buffer space in proportion to their bandwidth-delay products. Through simulation, the effectiveness of the proposed FRED algorithm is shown as compared with the drop-tail and the original RED algorithms.