Reconfiguration Method Research Articles

QoS-aware data center network reconfiguration method based on deep reinforcement learning

Maintaining high-performance operation under dynamic and nonuniform network traffic has been a technical challenge in current data center networks (DCNs). With the aim to provide better quality of service (QoS) for diverse applications, this work presents a dynamic and adaptive DCN reconfiguration framework based on deep reinforcement learning (DCR2L). The proposed framework is integrated into the SDN control plane of the DCN, implementing real-time and automatic DCN reconfiguration. Performance of the DCR2L framework is experimentally demonstrated in our DCN lab, including 4 racks and 16 servers. Experimental results show a network latency improvement of 6.9% based on the DCR2L at an average network bandwidth of 2.3 Gb/s. Based on measured traffic and physical parameters in the experiment, performance of the DCR2L framework is numerically assessed with the realistic traffic of diverse QoS requirements for both electrical and optical DCNs and for different data center scales. Leaf–spine electrical and OPSquare optical networks are set up in an OMNeT++ simulator. For a data plane network consisting of 16 racks and 320 servers, results indicate that the DCR2L framework improves the network latency of up to 16.4% under leaf–spine and up to 24.6% under OPSquare for the overall traffic with respect to the classical heuristic method. For a DCN scale of 10,240 servers, the DCR2L framework provides up to 12.5% lower latency for the leaf–spine electrical network and up to 17.8% latency improvement for the OPSquare optical network.

Path planning method for spacecraft formation reconfiguration based on CGAPIO

Path planning method for spacecraft formation reconfiguration based on CGAPIO

Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network

In this work, a novel real-time autonomous dynamic reconfiguration (ADR) method is proposed to reduce the cost of power loss and switch action of distribution network based on the deep learning (DL) algorithm. The proposed ADR method can be decision-making from the historical control dataset and the real-time system state. To obtain the historical control dataset efficiently, the heuristic rule is first introduced to improve the mixed integer convex programming method for making reconfiguration strategies to support grid operate in cost effective manner. To learn the reconfiguration strategy from the historical dataset, a long-short term memory network (LSTM) is presented to provide the effective training on the historical control dataset, and a switch action function considering real-time differences of operation cost is formulated, which can be combined with the trained LSTM model to achieve ADR. Case studies on the IEEE 33-bus system and a realistic Taiwan power company (TPC) 84-bus system verify that the ADR can obtain an ideal reconfiguration solution in the order of milliseconds and has high robustness.

Reconfiguration Strategy for Fault Tolerance in a Cascaded Multilevel Inverter Using a Z-Source Converter

The cascade multilevel inverters are widely used in industrial manufacturing processes for DC-AC conversion. Therefore, the reliability and efficiency improvement, optimized control, and fault-tolerant strategies are areas of interest for researchers. The fault tolerance strategies applied to cascade multilevel inverters are classified as material redundancy and analytical redundancy. This paper presents the use of the Z-source converter as a fault reconfiguration method applied to a cascade multilevel inverter. On the one hand, the proposed approach has the characteristic of combining the use of material redundancy (modifying the output voltage by changing the Z-source operation), and on the other hand, it has the use of analytical redundancy (modifying the switching sequence of the multilevel inverter, changing from symmetrical to asymmetrical operation mode). This approach has been validated by experimental results of the system under fault-free conditions and employing the Z-source converter as the main fault reconfiguration element. The proposed fault reconfiguration strategy allows the cascaded multilevel inverter to continue to operate even in the presence of a fault by having continuous operation.

A novel adaptive dynamic photovoltaic reconfiguration system to mitigate mismatch effects

Unequal solar irradiance of the Photovoltaic (PV) modules diminishes the PV array's maximum power output; this effect is due to Partial Shading Conditions (PSCs). There are a few technical options to fix this issue. One of these is the dynamic reconfiguration technique, which means that the electrical connections between the PV modules in the array are dynamically modified to spread shading effects and increase the power output. This paper proposed a novel adaptive reconfiguration method for 3×3 Total-Cross-Tied (TCT) PV array to enhance the maximum power under partial shading conditions. In this work, the PV module's electrical connections are altered dynamically with the proposed logic algorithm's help to maintain the identical row currents in the array. This technique is implemented on 3×3 size of an array using MATLAB and validated experimentally using OPAL-RT software. Further, the proposed technique performance is compared with the existing PV array connections such as Series-Parallel (SP), Total-Cross-Tied (TCT), Bridge-Link (BL), Honey-Comb (HC), and Shade Dispersion Positioning (SDP). Additionally, for different reconfiguration methods, revenue generation from energy savings is estimated. Based on the observations from the results, it is understood that the proposed technique enhanced the maximum power of the TCT array at a higher level as compared to the other PV array connections under PSCs.

A novel reconfigurable PD based on complementary hybrid circuit

ABSTRACT Reconfigurable methods including areflective phase-shifter circuit are proposed based on complementary hybrid network power divider (PD). The analysis shows that this method can achieve continuously tunable power division ratio (PDR) and wide phase difference by changing the two horizontal transmission lines of the complementary hybrid network. In this way, areconfigurable PD based on acomplementary hybrid circuit-based reflective phase-shifter is designed to work at 2.45 GHz by replacing two horizontal transmission lines with 50 Ω reflective phase-shift circuits. The PDR of this fabricated reconfigurable PD is changed to the range of −29.12 to −0.24 dB, with the bias voltage being 0–8 V; meanwhile, the return loss and isolation are better than 23.2 dB.

A Direct Preconditioner for Coupling Matrix Reconfiguration of Bandpass Filters With Irregular Couplings Using Continuation Method

In this article, a direct preconditioner is proposed for coupling matrix reconfiguration of bandpass filters with irregular couplings using the coefficient-parameter continuation method. With the preconditioner, the continuation method can be effectively used for filter synthesis and model extraction of bandpass filters with irregular couplings. In dealing with practical filters that contain a coupling topology with regular cross couplings introduced intentionally or unintentionally, the proposed preconditioner, being used as the start system in the continuation method, can swiftly lead to the solution that matches the physical realization. The preconditioner is physics-based and can be easily obtained by reconfiguring the regular coupling topology with the conventional method. The continuation process with a coefficient-parameter homotopy for coupling matrix reconfiguration is formulated for the first time, which transforms a coupling matrix in the canonical form to that in the desired one with irregular couplings. The convergence of the solution and a bootstrapping approach for dealing with multiple irregular couplings are also discussed. The method provides an enabling tool for robot automatic tuning (RAT) of advanced microwave bandpass filters. Three practical examples are presented, including one synthesis example and two model extraction examples for RAT, demonstrating the effectiveness of the reconfiguration method for practical applications.

The Cloud-edge-based Dynamic Reconfiguration to Service Workflow for Mobile Ecommerce Environments

The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.

A Comprehensive Review of Metaheuristic Methods for the Reconfiguration of Electric Power Distribution Systems and Comparison With a Novel Approach Based on Efficient Genetic Algorithm

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.

L-Shape Propagated Array Configuration With Dynamic Reconfiguration Algorithm for Enhancing Energy Conversion Rate of Partial Shaded Photovoltaic Systems

Partial shading is an unavoidable factor that reduces the performance of solar PV systems. The PV system receives uneven irradiation due to partial shading which causes the mismatch loss. The partial shading distracts the irradiation from the PV modules that makes the healthy modules as idle or low performing modules. The mismatch loss can be mitigated by uniformly distributing the partial shading over the PV array. In this work, L-shaped propagated array configuration method with a new dynamic reconfiguration algorithm have proposed for enhancing the energy conversion under the partial shading conditions. A new kind of array configuration is implemented in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> PV array for the better shade dispersion. Further, a dynamic reconfiguration algorithm is employed to disperse the effect of partial shading. The combination of new array configuration and reconfiguration method is simulated in MATLAB/Simulink® and implemented in hardware. The outputs are measured under all possible shading patterns and validated with the outputs of convention methods for observing the enhanced energy conversion rate of the proposed system.

A coordinated restoration method of electric buses and network reconfiguration in distribution systems under extreme events

A coordinated restoration method of electric buses and network reconfiguration in distribution systems under extreme events

Reconfigurable Production Line Design Method to Ensure High Efficiency in Both Steady and Emergency Situations

Reconfigurable Production Line Design Method to Ensure High Efficiency in Both Steady and Emergency Situations

An efficient wide-band signal detection and extraction method

Aiming at the influence of time-varying and frequency-varying of noise on the signal detection performance in the short wave wide-band channel and the large amount of computation in the channelized receiver model of the traditional low pass filter bank, a cross-channel reconfigurable multi-phase high-efficiency channelization method based on morphological processing is proposed in this paper .Firstly, The wide-band signal is coarsely filtered by the multi-phase structure of the uniform filter bank which is determined by the protection interval between signals, and then the bandwidth and position of the signal are determined by improved morphological operation and threshold decision of the power spectrum. Finally, the sub-band signals across the channel are combined to complete the approximate reconstruction of the sub-signals. Compared with the computational complexity of traditional channelized receiver model, the results show that this method has lower computational complexity. The simulation results show that the method can achieve the approximate constant false alarm rate(CFAR) under the colored noise environment, and has higher detection capability under different signal-to-noise ratios(SNR).

A Backtracking Ensemble Pruning Based Reconfiguration Method for Time-Triggered Flows in TTEthernet

A Backtracking Ensemble Pruning Based Reconfiguration Method for Time-Triggered Flows in TTEthernet

A mHEMT Power-Reconfigurable Distributed Amplifier Using a Gain Cell Switching Technique

A power-reconfigurable distributed amplifier (DA) is implemented using a 130 nm metamorphic high-electron mobility transistor (mHEMT) process. Using a mHEMT process with excellent high-frequency characteristics, output power and efficiency at high frequencies are improved compared with the existing power-reconfigurable distributed amplifiers. In this article, a gain cell switching technique is proposed as the power reconfiguration method for the distributed amplifier. The gain cell switching technique expands the gain bandwidth and power reconfigurability more than the conventional bias control. The proposed power-reconfigurable DA obtains a measured linear gain of 10.0–13.6 dB from 0.5 to 62.5 GHz and a measured output power of 20.2–21.5 dBm from 10 to 40 GHz. A measured power added efficiency (PAE) is 11.6–20.0% from 10 to 40 GHz. Under a low power mode (LPM), it obtains a measured linear gain of 4.4–7.1 dB from 0.5 to 65 GHz and a measured output power of 14.7–15.9 dBm from 10 to 40 GHz. At 10 GHz, a drain efficiency (DE) is 11.4% at 5.6 dB power back-off. This is 6.5% higher than the conventional DA or more than twice as high.

Optimization of Control Strategy for Photovoltaic Power Generation System in Weak Grid

With the continuous use of traditional fossil energy, the energy crisis and environmental pollution problems become increasingly obvious. As a recyclable, renewable and pollution-free clean energy, solar energy has received more and more social attention. Photovoltaic power generation has become one of the main forms of solar energy utilization for its convenience, safety and other advantages. This paper presents a control method to achieve maximum output efficiency tracking, guaranteeing the output current quality of photovoltaic cells. The design of photovoltaic power generation system and the setting of control parameters are often based on ideal conditions. However, in the real process such as long distance transmission, a large number of line impedances will inevitably be introduced into the power grid, which will lead to the increase of harmonic content of grid connected current and the generation of spike resonance among parameters. Under the combined action of non-linear load and line impedance, how to improve the system stability and reduce the negative impact on the inverted grid connected system has become a problem to be solved. This paper presents an impedance reconfiguration method to increase the stability of photovoltaic power generation system in weak grid.

Reconfiguration Aware Orchestration for Network Function Virtualization With Time-Varied Workload in Virtualized Datacenters

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.

Rekonfiguracija distributivne mreže i otočna kompenzacija uz prisustvo vetro generatora

In this paper, the distribution network reconfiguration with simultaneous capacitor switching, in the presence of wind generators, by Simulated Annealing (SA) is presented. Analysed test network has 69 nodes including the slack one and 73 branches, all of which can commutate. Following assumptions are made: load in nodes is changed according to Gauss distribution and wind generator power with Weibull one, every hour, then there are two wind generators of 200 kW maximum power each (10% of total, nominal active power load) and they can be allocated to any node but the slack one. The same is valid for the capacitor banks regarding allocation. This switching logic is unrealistic. On its basis more realistic one was issued with fixed nodes for allocation of wind generators and capacitor banks (the most frequently visited nodes), by Monte Carlo graphical method. Input power factor is to be greater than 0.85 which is not fulfilled with commencing configuration (from the start) so that allocation of capacitor banks is mandatory. Another constraint is that the network should not be overcompensated. Four realistic scenarios are investigated. In the first one only network with wind generators is analysed and the rest are dedicated to all possible combinations of the regulation. The programme is automated indicating the price of configuration, generated banks, input data (active and reactive load, power and location of wind generators) and savings which change on an hourly basis. The wind generators are uniformly distributed in accordance to nodes (for the less realistic scenario) and generate only active power complying with Weibull distribution. The graphical results are presented for a 1000-hour operation (operation in one thousand hours, every hour different) and the analysis is done for a thousand-hour work. The presented method shows that considerable savings can be achieved by simultaneous application of reconfiguration method and capacitor switching with already allocated wind generators.

SSHC: A Secure and Scalable Hybrid Consensus Protocol for Sharding Blockchains With a Formal Security Framework

Sharding blockchains are proposed to solve the scalability problem while maintaining security and decentralization. However, there are still many issues to be solved. First, the member selection and assignment process are not strictly analyzed, which might lead to an increase in the adversary proportion. Second, current intra-shard consensus algorithms are inefficient. Besides, cross-shard transaction processing costs expensive system overhead. Moreover, there is a lack of a formal security framework. In this article, we propose a secure and scalable hybrid consensus (SSHC). First, we propose a fair sharding selection scheme to select committee members, including mining processes and member lists confirmation by a reference committee. Second, a pipelined Byzantine fault tolerance for intra-shard consensus is designed, combining the pipelined technology with threshold signatures. Third, we propose a responsive sharding transaction batch processing mechanism to handle cross-shard transactions, which reduces the number of calls to Byzantine fault tolerance algorithms. Fourth, a secure committee reconfiguration method is designed to update shard members efficiently. Furthermore, we employ a formal security framework to design and analyze a sharding blockchain. For an adversary whose computational power fraction is less than <inline-formula><tex-math notation="LaTeX">$1/3$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3047487.gif"/></alternatives></inline-formula>, by reasonably setting a corruption parameter and other related parameters, SSHC is proved to achieve consistency and liveness.

A Hybrid Offline Optimization Method for Reconfiguration of Multi-UAV Formations

Formation reconfiguration of multiple unmanned aerial vehicles (UAVs) is a challenging problem. Mathematically, this problem is an optimal control problem subject to continuous state inequality constraints and terminal state equality constraints. The first challenge is that there are an infinite number of constraints to be satisfied for the continuous state inequality constraints, which makes the problem extremely difficult to be solved. The second challenge is that the control and state are usually both been discretized. This will result in noncontinuous control input. In addition, the discretized system may not always accurately approximate the original system. In this article, a hybrid offline optimization scheme is proposed to tackle these problems. Unlike the existing methods, the state variables are not required to be discretized and continuous control inputs can be obtained. In addition, the continuous state inequality constraints are tackled without increasing the total number of constraints. Simulation results show that the proposed hybrid optimization method outperforms the state-of-the-art method—the hybrid particle swarm optimization and genetic algorithm.