Convergence Delay Research Articles

Time-varying price dynamics of clean and dirty energy portfolios

This paper investigates the dynamic relationships between four key instruments related to clean and dirty energy assets: WTI futures, United States Oil Fund (USO), EnergySelect Sector SPDR Fund (XLE), and iShares Global Clean Energy ETF (ICLN). Econometric tests confirm a long-term relationship between all variables, with causality tests showing that clean energy ETF has a causal influence on most instruments. However, the causal patterns are not definitively interpretable in an economic framework. Moreover, using wavelet-based tests on a 1-min interval transaction dataset, we further find convergence delay between WTI and XLE, and to a lesser extent, USO, but not ICLN. This suggests that clean energy has the potential to be a distinct asset class. We also identify the time scales at which arbitrage opportunities and liquidity movements occur: 32–256 and 4–8 min, respectively. These are new stylized facts about clean and dirty energy market assets and contribute to the limited literature available on high-frequency dynamics in the said markets.

5G Edge Computing Access Node Selection Algorithm Based on Energy Efficiency and Delay

In order to address the dissipation of energy efficiency in 5G edge computing, and the problem of network delay, better improve the quality of user service, considering the data aggregation delay while improving the network energy efficiency. The author proposes a 5G edge computing access node selection algorithm based on energy efficiency and delay; through the energy efficiency and delay balanced data collection mechanism (EEDBDG), a new dynamic tree is used to organize the network topology, eliminating the hot zone problem; nodes dynamically choose routes and take turns acting as the root of the tree, which collects data and communicates directly with the base station. At the same time, three data collection strategies are proposed for different latency and energy efficiency requirements: delay optimal algorithm (EEDBDG-D), energy efficiency optimal algorithm (EEDBDG-E), and energy efficiency delay balance algorithm (EEDBDG-M). Experimental results show that, when the communication radius of nodes is limited, EEDBDG balances the energy consumption of nodes, prolongs the network life time, and shows outstanding performance in energy saving and time saving. Compared with GSEN, in the best case, the network lifetime of EEDBDG-E is increased by 72%, and the convergence delay of EEDBDG-D is reduced by 74%. Conclusion. The algorithm can effectively reduce the energy dissipation and delay of edge computing.

New Game-Theoretic Approach to Decentralized Path Selection and Sleep Scheduling for Mobile Edge Computing

Network function virtualization (NFV) implements mobile edge computing (MEC) services as software appliances, and allows resources to be adaptively allocated to accommodate demand variations. Scalability and network cost (including operational cost and response latency) are key challenges. This paper presents a new game-theoretic approach to minimizing the network cost, where access points (APs) select MEC servers and routes in a decentralized manner, and unloaded routers and links are deactivated for cost saving. The key idea is that we interpret the minimization of network cost as a mixed game with a non-monotonic cost function capturing both the operational cost and response latency. We prove that the game is conditionally an ordinary potential game and converges to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -approximate equilibriums. A closed-form expression is derived for the convergence delay. Another important aspect is that we integrate Stackelberg routing into the proposed mixed game to avoid inefficient equilibriums (with high cost or latency). We prove that the mixed game can converge faster to better equilibriums under linear response latency models. Extensive simulations corroborate the new game-theoretic approach can significantly outperform existing techniques in terms of efficiency, convergence, and scalability.

Distributed Weight Adaptive Control for Frequency Regulation of Islanded Microgrid

The consensus control method based on a multi-agent system has been widely applied in the distributed control and optimization of microgrids. However, the following drawbacks are still common in current research: (1) ignoring the influence of consensus control commands on the synchronization stability of the physical grid under primary control; (2) only focusing on improving one property ofcontrol performance, lacking comprehensive considerations of multiple properties. With the aim of solving these problems, in this paper we propose a weight-adaptive robust control strategy for implementing distributed frequency regulation of islanded microgrids. Firstly, the frequency synchronization stability of the physical layer is analyzed by means of a coupled oscillator theory and the design objectives of the controllable parameters for the information layer are formed. Subsequently, the relationship between the weight coefficients and the two important control performances of convergence speed and delay robustness is strictly analyzed. Based on this, an adaptive coefficient that can be autonomously adjusted according to the frequency deviation is designed to achieve a trade-off between convergence speed and delay robustness. Finally, three simulation studies are presented to verify the effectiveness of the proposed control strategy.

Multidomain SDN-Based Gateways and Border Gateway Protocol

The Internet consists of distributed and interconnected autonomous systems (ASs). The flexibility afforded by the Internet architecture ensures that timely changes to the network topology can occur without centralized control. The Border Gateway Protocol (BGP) is an Internet protocol that routes the Internet traffic and exchanges the information between AS. However, BGP version 4 (BGP-4) currently suffers from a limitation called “high convergence delay” while doing routing updates, which is a very common limitation, damaging the performance of contemporary IP networks. Software-defined networking (SDN) was conceived at the beginning of the 21st century, and over the intervening years, it has gained traction. This technology has significantly enhanced traffic control, management, and monitoring, particularly in data centres and enterprise networks. Challenges have been found including how to provide administrative control, security, management, and monitoring at domain boundaries while introducing the SDN paradigm. In multidomain SDN, BGP-4 is used to exchange the information and route the traffic between domains or diverse AS. In this review, an investigation is provided on using the SDN paradigm to enhance multidomain traffic management and control and to optimize BGP operation. A detailed insight is provided into the penetration of the SDN paradigm into modern networking architectures and how it may help in facilitating future research to improve BGP-4.

Kalman filter polarization demultiplexing algorithm based on diagonalized matrix treatment.

When we implement the equalizations of polarization effects using a Kalman filter (KF) in a coherent optical fiber communication system, we will require to multiply many matrices. If the state vector describing the system has a dimension of n, the state error covariance matrix P will have the dimension of n × n, and other matrices used in the Kalman filter will also have the dimension of n × l (l is the dimension of the measurement vector). If n is very large, the KF-based algorithm will suffer from significant complexity, which results in an impractical KF-based polarization demultiplexing algorithm. In this paper, we propose a new structured KF-based polarization demultiplexing algorithm in which the state error covariance matrix P is diagonalized, which we call the diagonalized Kalman filter (DKF). We theoretically analyze the rationality of the DKF, and the validity of the DKF was verified in both 64 Gbaud polarization-division multiplexed (PDM) QPSK and 16QAM Nyquist coherent optical simulation systems. Compared with the conventional KF, simulation results proved that under a rotation of state of polarization from 1 to 10 Mrad/s for QPSK and 1 to 5 Mrad/s for 16QAM, a differential group delay from 15 to 75 ps, and a residual chromatic dispersion of 100 ps/nm, the OSNR penalties for the DKF are only within 0.5 dB for QPSK at the threshold BER = 3.8 × 10-3, and within 2 dB for 16-QAM at the threshold BER = 2 × 10-2, respectively, compare to the case of no impairment. In the meantime, for the proposed DKF, a computational complexity reduction of over 30% is achieved, compared with conventional KF, at the expense of about no more than 50 symbols convergence delay.

Dynamic Thresholds for a Reliable Diagnosis of Switched Systems

Safety, availability and reliability are the main concern of many industries. Thus, fault detection and isolation of industrial machines, which are in most cases switched systems, is a primary task in many companies. The presented paper proposes a new diagnostic approach for switched systems using two powerful tools: bond graph and observer. A diagnostic layer detects model errors using bond graph, and a smart algorithm identifies and locates faults using observer. Although observers serve as fault detectors, they also have their own errors caused by convergence delay of calculations; even in the case of no sensor defect, the residue does not converge to zero. In this paper, we propose a new method to solve this problem by integrating dynamic thresholds in the detection procedure, which helped to avoid false alarms and ensure a highly reliable diagnosis.

A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

Abstract The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized approach for providing Quality of Service (QoS) routing for heavy volume IoT data transmissions this paper proposes a machine learning-based routing algorithm with a multi-agent environment. The overall routing process is divided into two phases: route discovery phase and route maintenance phase. The route discovery or path finding phase is performed using rank calculation and Q-routing. Q-routing is performed with Q-Learning reinforcement machine learning approach, for selecting the next hop node. The proposed routing protocol first creates a Destination Oriented Directed Acyclic Graph (DODAG) using Q-Learning. The second phase is route maintenance. In this paper, we also propose an approach for route maintenance that considerably reduces control overheads as shown by the simulation and has shown less delay in routing convergence.

A pseudo-equilibrium finite element for limit analysis of Reissner-Mindlin plates

A new finite element is developed for the yield design of Reissner-Mindlin plates based on the static theorem of limit analysis. This three-node triangular element satisfies the equilibrium equations and the mechanical boundary conditions on average, and, as such, it is not expected lower bounds on the collapse load from the computed results. The yield criterion is, however, exactly satisfied throughout the element. The relatively small nonlinear convex optimization problem posed here is treated as second-order cone programming and solved with a primal-dual interior-point algorithm implemented in the MOSEK optimization package. The proposed procedure exhibits excellent performance on a series of numerical tests, demonstrating that not satisfying the equilibrium equations and the mechanical boundary conditions rigorously is far from being a handicap. It is also observed that the solution may develop boundary layer along certain types of edges. This real physical phenomenon, likely to be manifested in Reissner-Mindlin plate solutions and that nearly no attention has been paid in the framework of yield design, is a source of convergence delay.

WITHDRAWN: Dynamic adaptation in channel estimation under change state scenario

User mobility is a critical concern in wireless communication. During user mobility the channels are Non-linearly vary with time. The non linear variation generates a highly dynamic interference model which leads to considerable processing delay in channel estimation. Filters used in the estimation process were either tuned for a reference frequency band or for a reference signal. However, the estimators face an issue of convergence delay when the interference is non-linear in nature. The existing approaches of estimation were developed with performance enhancement by adaptively controlling the weight factors for input in reference to output and reference value. However, the existing approaches perform the estimation with the assumption of user mobility as interference dynamic. No channel characteristic and positioning of user mobility is been considered. To address the issue, this paper present a new method for estimation constraint based on user positional movement from a reference link with channel characteristic consideration.

On the Anti-Interference Tolerance of Cognitive Frequency Hopping Communication Systems

Massive malicious jamming devices and advanced jamming techniques have been emerged along with the development of wireless communication networks. In order to effectively eliminate the harmful interference, a new scheme known as the cognitive frequency hopping (CHF) is proposed recently, which can evaluate the occupancy of frequency hopping slots and adjust the parameters dynamically according to the spectrum sensing results. Although the existing literature only shows that CFH systems can achieve reliable data transmission, the factors affecting the reliability are rarely analyzed. Therefore, we analyze the reliability performance of the CHF systems in this article, and define a new metric named anti-interference tolerance to measure the reliability performance of the CHF systems. Moreover, we derive the analytic expression of anti-interference tolerance by analyzing the effect of false alarm probability, missed detection probability, and communication link convergence delay. Simulation results validate the effectiveness of our analyses for measuring the reliability capacity of the CHF systems. To satisfy the demands of different communication scenarios, the CFH systems can adjust relevant parameters in the light of our theoretical derivation.

Continuous-wave terahertz quantitative dual-plane ptychography

Terahertz (THz) radiation lies between the millimeter and infrared region of the electromagnetic spectrum, which is typically defined as the frequency range of 0.1–10 THz and the corresponding wavelength ranges from 30 μm to 3 mm. Terahertz radiation due to wide spectrum, high penetration, low energy, and other important features, has been a valuable tool for imaging and non-destructive testing on a submillimeter scale. Continuous-wave (CW) terahertz ptychography is a type of phase-contrast technique with advantages of simple set-up and large field-of-view. It retrieves the complex-valued transmission function of the specimen and the probe function at the same time. The extended ptychographic iterative engine (ePIE) algorithm is used as the reconstruction algorithm in the field of ptychography, because it is relatively simple, and can use computer memory efficiently. However, the problem of algorithm convergence delay makes us unable to acquire the reconstruction result very quickly. Since the ptychography is a problem of retrieving phase information, physical constraints affect the convergence speed of the algorithm strongly. In this paper, we propose a dual-plane ePIE (dp-ePIE) algorithm for CW THz ptychography. By moving detector along the axis and capturing diffraction patterns of one zone of an object at two recording planes, then, two sets of patterns used as the constraints simultaneously can increase the diversity of experimental parameter. Hence, the convergence rate can be improved. The simulation results suggest better reconstruction fidelity with a faster convergence rate by the dp-ePIE algorithm. The dual-plane terahertz ptychography experimental setup is built based on 2.52 THz optically pumped laser and Pyrocam-III pyroelectric array detector. Compared with other methods to increase the diversity of measurement, the setup of dual-plane ptychography can be compact and simple, thus reducing the terahertz wave transmission loss. A polypropylene sample is adopted and it is approximated as a pure phase object. No-reference structural sharpness (NRSS) is utilized as a quantitative evaluation index. It takes 45.086 s to achieve NRSS value of 0.9831 by using the dp-ePIE algorithm in 10 iterations, while the NRSS value and calculation time for e-PIE algorithm are 0.9531 and 57.117 s (20 loops), respectively. The experimental results show that the dp-ePIE algorithm can obtain high-quality amplitude and phase distribution with less iterations than the traditional ePIE algorithm.

CONSENSUS PROBLEMS IN WEIGHTED HIERARCHICAL GRAPHS

We present the hierarchical graph for the growth of weighted networks in which the structural growth is coupled with the edges’ weight dynamical evolution. We investigate consensus problems of the graph from weighted Laplacian spectra perspective, focusing on three important quantities of consensus problems, convergence speed, delay robustness, and first-order coherence, which are determined by the second smallest eigenvalue, largest eigenvalue, and sum of reciprocals of each nonzero eigenvalue of weighted Laplacian matrix, respectively. Unlike previous enquiries, we want to emphasize the importance of weight factor in the study of coherence problems. In what follows, we attempt to study that the weighted Laplacian eigenvalues of the weighted hierarchical graphs, which are determined through analytic recursive equations. We find in our study that the value of convergence speed and delay robustness in weighted hierarchical graphs increases as weight factor increases and the value of first-order coherence decreases as weight factor increases. Moreover, as is expected, weight factor affects the performance of consensus behavior and can be regarded as a leverage in the problem of consensus problems. This paper puts forward the proposal and the countermeasure for stability optimization of networks from the perspective of weight factor for future researchers.

Network Solutions for CoMP Coordinated Scheduling

Demanding throughput, latency and scalability requirements of 5G networks may be addressed by relying on dense deployments of small cells. Coordinated Multipoint (CoMP) Coordinated Scheduling (CS) techniques are introduced to reduce inter-cell interference in case of dense deployment, given that local CoMP-CS information from the evolved NodeBs (eNodeBs) in the cluster are timely collected at the scheduling decision entity. This work studies how the distribution of CoMP-CS cell information is affected by the backhaul infrastructure in terms of both physical and logical topology. The differentiation between physical and logical infrastructure is justified in the context of new approaches like Software Defined Networking and Network Function Virtualization that enable the dynamic configuration of the network. We consider either a Packet Switched Network with three possible topologies (namely, ring, mesh and star) or a Time Division Multiplexing Passive Optical Network (TDM-PON), both carrying heterogeneous traffic. To improve the convergence time in the TDM-PON, we propose a novel bandwidth allocation scheme to prioritize the signaling traffic with respect to data traffic. Performance of both distributed and centralized CoMP-CS are compared in terms of convergence delay and traffic overhead. Finally, we analyze the impact of the periodicity of CS operations on mobile performance, in terms of average UEs throughput, in the presence of different cell loads.

Acceleration of Later Convergence in a Density-Based Solver Using Adaptive Time Stepping

The convergence to the steady state of an implicit unstructured density-based compressible Navier-Stokes flow solver shows many cases exhibiting stalling or slower convergence in the later part mainly due to fluctuation in the local error. This causes a delay in convergence and often prevents machine zero convergence from being achieved. This paper presents the use of local error control for acceleration of later convergence. This method is tested against standard benchmark problems, and then on a more complex case of flow over an airfoil taken from recent published work. The results show that the method is capable of very rapid acceleration to machine zero convergence. The acceleration has been found to be of an order of 500–1000% faster in comparison to the same implicit scheme with Courant–Friedrichs–Lewy-based time stepping and no local error control. The present method acts on top of the implicit solver, and thus its implementation requires very little modification to an existing code.

Impact of MRAI Timer on BGP Updates and Convergence Time

&lt;p&gt;BGP is a vital routing protocol for the communication amongst autonomous systems in the internet and has been broadly applied in all categories of large scale network. The inter-domain routing protocol (BGP) shows slow convergence, which effects on many internet applications due to its high convergence delay. The network operators broadly use different MRAI timers in BGP routers to deal with the issue of growing convergence time of the network. The variation in MRAI timer and its impact on network convergence and update messages has been broadly studied over the years. The increasing size of autonomous systems leads to rise in number of MRAI timers. Hence, the optimum use of MRAI timers can decrease the problem of slow convergence and necessity of huge number of MRAI timers. The proposed system uses the ckle minimum route advertisement interval timer (FMRAI) for fast update of routing table, which leads to reduce the convergence time of a network. In comparison with static MRAI timer of 30s the FMRAI timer leads to better result in terms of convergence time and number of update messages.&lt;/p&gt;

Mathematical Model of the Relationship between BGP Convergence Delay and Network Topologies

BGP is a distant vector inters Autonomous System (AS) routing protocol that comes up after EGP to eliminate the inefficiency of EGP with respect to flexibility and scalability and to give support of an actual routing protocol. BGP handles the scalability problem using Classless Inter-Domain Routing (CIDR) and solves the inefficiency of EGP by accumulating all the possible route information to a destination and running a decision process to select a route to be used and to advertise to the peers. Recently BGP protocol starts to encounter several problems such as routing table growth, load balancing problems, BGP hijacking and transit-AS problems and increasing time of Convergence delay. Convergence delay is the time between the selection process for the best path and when the routers settled. Convergence delay started recently to be an issue for internet and larger network as it started to be increased which causes instability in the network. Instability lead to lost packets, delayed delivery, loss of connectivity and long end-to-end delay in the Internet as well as added overhead to BGP routers. The goal of this research is to study the behavior of different network topology in terms of BGP convergence delay besides defining a mathematical model to represent the relationship between convergence delay and number of nodes. Simulation results show that Mesh topology has the highest convergence delay. The study of the relation between convergence delay and number of nodes leads to mathematical equations which some of them represent linear relationship while others represent compound relationship.

Convergence delay in ospf: a brief review

convergence delay in ospf: a brief review

Consensus of Fractional-Order Multiagent Systems with Double Integral and Time Delay

This paper is devoted to the consensus problems for a fractional-order multiagent system (FOMAS) with double integral and time delay, the dynamics of which are double-integrator fractional-order model, where there are two state variables in each agent. The consensus problems are investigated for two types of the double-integrator FOMAS with time delay: the double-integrator FOMAS with time delay whose network topology is undirected topology and the double-integrator FOMAS with time delay whose network topology is directed topology with a spanning tree in this paper. Based on graph theory, Laplace transform, and frequency-domain theory of the fractional-order operator, two maximum tolerable delays are obtained to ensure that the two types of the double-integrator FOMAS with time delay can asymptotically reach consensus. Furthermore, it is proven that the results are also suitable for integer-order dynamical model. Finally, the relationship between the speed of convergence and time delay is revealed, and simulation results are presented as a proof of concept.

The Adaptive $s$-Step Conjugate Gradient Method

The performance of Krylov subspace methods on large-scale parallel computers is often limited by communication, or movement of data. This has inspired the development of $s$-step (or communication-avoiding) variants, in which iterations are computed in blocks of $s$. This reformulation can reduce the number of global synchronizations per iteration by a factor of $O(s)$ and has been shown to produce speedups in certain practical settings. Although the $s$-step variants are mathematically equivalent to their classical counterparts, they can behave quite differently in finite precision depending on the parameter $s$. If $s$ is too large, the $s$-step method can suffer convergence delay and decreased attainable accuracy relative to the classical method. As a potential remedy to the loss of accuracy in $s$-step Krylov subspace methods, we introduce and analyze a variable $s$-step conjugate gradient method in which $s$ may vary between blocks. From a bound on the growth of the residual gap in block $k$ containi...