Optimal Load Distribution Research Articles

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Synergistic approach to wear rate forecasting in AZ31/5% Yttria stabilized zirconia reinforced composite through response surface methodology-genetic algorithm integration

In this work, the wear behavior of a novel AZ31 magnesium alloy reinforced with 5% yttria-stabilized zirconia (YSZ) composite was evaluated using a hybrid Response Surface Methodology (RSM) and Genetic Algorithm (GA) approach. The composite was fabricated using ultrasonic-assisted stir squeeze casting technique, ensuring homogeneous distribution of spherical YSZ particles, as validated by the scanning electron microscope integrated with energy dispersive spectroscope. Wear tests were carried out according to the ASTM standards using a pin-on-disc (POD) tribometer, with applied load (AL), sliding speed (SS), and sliding distance (SD) as the main parameters. An empirical wear rate regression model was developed using RSM/Box-behnken design, and Genetic Algorithm was deployed for parametric optimization, achieving a minimal wear rate of 0.0144 g/m under a load of 30 N, sliding speed of 260 rpm, and sliding distance of 400 m. Confirmation tests were performed to validate the GA predictions. The wear mechanisms were observed, showing reduced wear in GA-optimized samples due to optimized load distribution resulting minimized ploughing, grooving and delamination. This work highlights the efficacy of the hybridized RSM / GA for the wear performance in advanced magnesium alloy matrix composites.

An optimal load distribution and real-time control strategy for integrated energy system based on nonlinear model predictive control

Efficient operational scheduling and control are vital for the operation process of the Integrated Energy System (IES), especially under varying operational conditions. An optimal load distribution and real-time control strategy for IES based on Nonlinear Model Predictive Control has been developed to synergistically improve equipment output performance and reduce operating costs. In the proposed strategy, the IES model that integrates linear parameter-varying models for devices and dynamic models for pipe networks, has been constructed via system identification methodology under varying operational conditions. Thus, a hierarchical operation and control framework is established using the model predictive control strategy, including real-time prediction objective, optimal load distribution objective, and optimization algorithms. Consequently, a case study is performed to investigate the supply-demand balance and economic benefit of the proposed strategy. From the aspect of supply-demand balance, the average energy supply-demand discrepancy by using the proposed strategy (for cooling and heating) is approximately 25 %∼33 % that under the steady-state strategy. By means of the proposed strategy, an outstanding equipment output performance will be achieved and supply-demand discrepancy can be dramatically reduced. From the perspective of economic performance, the proposed strategy, which fully considers the nonlinearity of devices, exhibits a 16 % decrease in the operational cost compared to that under the linear dynamic strategy. Through the proposed strategy, efficiency losses are expected to degrade with an excellent economic benefit. Therefore, the suggested strategy is promising and suitable for varying operational condition scenarios.

Influence of bioinspired morphing on the flow field characteristics of UAV wings at low Reynolds number

The aerodynamic performance of unmanned aerial vehicles (UAV) can be improved by optimizing the surface flow characteristics over a wide range of angle of attack (AoA) through novel mechanisms. Recently, the bioinspired camber morphing concept has received greater attention because of the proven ability of nature species towards the retention of aerodynamic performance under different environmental conditions. In particular, birds like Eagles ( Accipitriformes) increase their wing camber in the course of flight to achieve maximum climbing altitude with good manoeuvring capability. The biomimetic designs such as the corrugated bone structure of Eel fish ( Anguilliformes) helps to achieve the wing camber morphing with optimal aerodynamic load distributions. The present work is focused on the bioinspired variable camber morphing (VCM) strategy to enhance the flow control behaviour and aerodynamic forces for a specific UAV wing configuration at various AoA. Here, NACA 4412 airfoil is used as a baseline wing configuration and the camber morphing mechanisms which are derived through Eel fish and Eagle are analysed. The model with Eagle wing morphing (EWM) mechanism is considered as a primary case of VCM and Eel fish’s corrugated structure is taken as a secondary case of VCM model. The coefficient of lift ( C L ), coefficient of drag ( C D), coefficient of pressure ( C p) and endurance factor are estimated for both morphed and baseline wing configurations through high fidelity numerical simulations. Interestingly, it is observed that the EWM wing configuration has excellent surface flow control characteristics than the CSM wing configuration and the results are presented with a detailed discussion.

Optimal scheduling of virtual power plants considering integrated demand response

Abstract In contemporary power system management, leveraging multilevel demand response strategies plays a pivotal role in balancing energy supply with demand, thereby enhancing system efficiency and flexibility. This paper introduces an innovative optimization model for virtual power plant operations that anticipates the scheduling needs several days in advance. It incorporates a comprehensive approach to demand response on the consumer side, highlighting two main types: price-based and substitution-based responses. Furthermore, the model integrates the functionalities of energy storage and conversion devices. This integration aims to facilitate effective load management, ensuring an optimized load distribution while minimizing the overall system costs.

Optimum management of microgrid generation containing distributed generation sources and energy storage devices by considering uncertainties

In recent years, the financial and energy crises have made the economical and safe operation of distribution microgrids one of the main challenges for operators. In this regard, the optimal distribution of the microgrid, considering the favorable indicators of Generation and consumption, significantly improves the quality of the development and economic operation of electricity grids. On the other hand, distribution microgrids have many problems and complications due to the presence of DG sources and the uncertainties caused by them in Generation and consumption, such as the inability to solve optimal load distribution using common and common calculation methods. Therefore, grid technical indicators such as voltage deviation and losses in solving load distribution add to the complexity of related issues. To solve the challenges raised in the distribution microgrid, this paper proposes an optimal load distribution model that models the uncertainties in the distribution microgrids using the Monte Carlo method. Using meta-heuristic algorithms, the optimal Generation function by modeling grid security constraints such as voltage deviation, losses, and a load limit of the system should be in economic operation mode, and then the power distribution equations should be solved. The battery can supply 1 kW of active power to the system at a cost of 1 unit/kW during the high cost period if it absorbs 1 kW of active power at a cost of 0.92 units/kW during the low cost period. Additionally, the active and reactive power losses have been decreased to 0.3kW and 0.2kW, respectively, by utilizing the suggested methodology. The simulation results show that the proposed method performs satisfactorily and that the modeling is simulated in MATLAB software.

Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

Performance optimization and energy minimization of cloud data center using optimal switching and load distribution model

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR).

Towards a new frontier in wrist rehabilitation: The traction-free posture orthosis

This study presents the novel Traction-Free Posture Orthosis, designed to address wrist stiffness without the use of traditional traction methods. The orthosis employs a velcro system to stabilize the position of the wrist in flexion or extension, thereby avoiding the compression and rotational disadvantages associated with non-perpendicular traction forces. Triangular-shaped cuts in the brace allow for plastic material flexibility, ensuring movement and comfort. Significant is the absence of a traction strap, which traditionally reduces the support surface and could create pressure points leading to patient discomfort or treatment rejection. The design facilitates a wide contact surface with the hand, optimal load distribution, and the capacity to adjust dual traction straps independently, adapting to the natural wrist movement. This single-piece base orthosis offers a stable, cost-effective, and patient-friendly alternative to conventional splints. However, patients with stiffness in both flexion and extension may require two separate orthoses, potentially increasing costs and patient inconvenience. Overall, the Traction-Free Posture Orthosis represents an innovative step in wrist rehabilitation, providing a quick-to-construct, easy-to-wear solution with the promise of enhanced patient compliance and comfort.

FE-analytical slice model and verification test for load distribution analysis and optimization of planetary gear train in wind turbine

Load distribution behavior of planetary gear train (PGT) is crucial for the contact and bending fatigue failure of wind turbine transmission system. With the increasing power of wind turbines, the load and failure rate of wind turbine become larger, which leads to the demand of accurate and efficient modeling, analysis and optimization for load distribution of PGT. In this paper, a new efficient and accurate FE-analytical slice model for load distribution analysis and optimization of PGT in wind turbine is proposed, which is explicitly formulated by the geometry slice model of external/internal gear contact, original FE-analytical slice model for local contact deformation, and efficient finite element (FE) model for global structural deformation. In the FE-analytical slice model, geometry errors are effectively descripted by discretizing gears into a series of slices, while gear tooth contact elasticity and structural elasticity are addressed by the FE-analytical method accurately and efficiently. Upon the proposed model, the load distribution behaviors of PGT with coupling effects of elasticity and errors are revealed and optimized by tooth modification and in-phase eccentricity arrangement strategy. The load distribution tests of PGT in wind turbine are conducted, verifying the proposed FE-analytical slice model which is significant to improve the load-bearing capacity and avoid the fatigue failure of wind turbine transmission system.

Fabrication of an interim buccal-retaining flange obturator for a patient undergoing partial maxillectomy

Abstract Prosthodontic treatment of palatal defects focuses on closing oronasal communication and improving masticatory function, speech, esthetics, and comfort. The most difficult aspect of rehabilitating patients with hemimaxillectomy is ensuring the prosthesis has enough retention and stability. During healing, a simpler design and effective retention should adequately meet the patient’s functional requirements. This case report presents the fabrication of an interim cost-effective and retentive buccal flange palatal obturator for a 65-year-old male patient who had an acquired maxillary defect resulting in mastication and deglutition difficulties. In this report, the retention of the obturator was achieved by utilizing the remaining teeth and employing a flexible thermoplastic buccal flange, with a wrought-wire U-shaped loop, on the buccal surfaces of the left canine, premolars, and molars. A complete palatal acrylic palate was designed to achieve optimal distribution of load to the surrounding tissues. To enhance patient comfort, the obturator featured a closed hollow bulb, reducing its overall bulk and making it lightweight. This interim prosthesis successfully rehabilitated the patient, resulting in improved masticatory efficiency, enhanced speech clarity, and an overall improvement in the patient’s quality of life.

Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model’s hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production.

Risk-driven optimal scheduling of renewable-oriented energy hub under demand response program and energy storages: A novel Entropic value-at-risk modeling

Energy hubs have revolutionized the field of energy management, emerging as state-of-the-art solutions for achieving efficient and sustainable energy systems. These hubs act as integrated platforms that seamlessly combine a variety of energy technologies, facilitating the synergistic interaction of multi-carrier energy systems with diverse energy sources. This paper outlines a framework for an energy hub that integrates a wide range of energy technologies, encompassing electrical, thermal, and cooling systems, alongside renewable resources and storage units. The proposed model incorporates demand response programs (DRPs) to effectively manage energy demand, optimize load distribution, enhance grid stability, and achieve substantial cost reductions. The effectiveness of the proposed framework is demonstrated through careful analysis of four distinct case studies, giving its potential in diverse energy management scenarios. To tackle uncertainties associated with power, heating, and cooling demands, as well as power and gas market prices, wind speed, and solar irradiation, this study utilizes EVaR as a stochastic modeling approach. The EVaR is a novel coherent risk measure incorporated to enhance risk management practices and surpass the limitations of traditional value-at-risk measures. According to the obtained results, due to the conservativeness of the operator, the income has decreased by 23 % while the system operation cost has increased by 36 % in the presence of the EVaR optimization approach.

A comparison of traditional and net structured intersomatic cages in the lombosacral region: A biomechanical analysis for enhancing discopathy treatment

The vertebral column represents an essential element for support, mobility, and the protection of the central nervous system. Various pathologies can compromise these vital functions, leading to pain and a decrease in the quality of life. Within the scope of this study, a novel redesign of the Intersomatic Cage, traditionally used in the presence of discopathy, was proposed. The adoption of additive manufacturing technology allowed for the creation of highly complex geometries, focusing on the lumbosacral tract, particularly on the L4-L5 and L5-S1 intervertebral discs. In addition to the tensile analysis carried out using Finite Element Analysis (FEA) in static simulations, a parallel study on the range of motion (ROM) of the aforementioned vertebral pairs was conducted. The ROM represents the relative movement range between various vertebral pairs. The introduction of the intersomatic cage between the vertebrae, replacing the pulpy nucleus of the intervertebral disc, could influence the ROM, thus having significant clinical implications. For the analysis, the ligaments were modelled using a 1D approach. Their constraint reaction and deformability upon load application were analysed to better understand the potential biomechanical implications arising from the adoption of the cages. During the FEA simulations, two types of cages were analysed: LLIF for L4-L5 and ALIF for L5-S1, subjecting them to four different loading conditions. The results indicate that the stresses exhibited by cages with a NET structure are generally lower compared to those of traditional cages. This stress reduction in cages with NET structure suggests a more optimal load distribution, but it is essential to assess potential repercussions on the surrounding bone structure.

Increase of the Efficiency of Heat Supply Units of the CHP Plants Due to the Choice of Rational Heat Release Modes

The possibilities of increasing the efficiency of heat supply turbines of CHPPs due to the choice of rational modes of operation of network water heaters are analyzed. With the help of a software and computing complex developed at the Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine and adapted by the authors to the operating conditions of CHPP generating equipment with one or two network heaters, a set of calculation studies of various ways of connecting them depending on the outdoor air temperature is conducted in the paper. Areas of positive effect associated with increase in the electric power of the power plant have been established. The calculation study was carried out at typical power plant water consumption from 1000 t/h to 4500 t/h, in the range of changes in the outdoor air temperature from -11 ºС to 10 ºС (heating season) and more than 10 ºС (hot water supply). The load change of the power unit was carried out due to the consumption of fresh steam at a constant pressure and temperature at the turbine inlet. As shown by the results of the operation of the T-100/120-130 heat supply turbine in operating conditions with one or two heat supply steam selections, in the area of positive outdoor air temperatures above 2 ºС, it is advisable to use one lower selection (with the upper one turned off) for all network water consumption. At the same time, additional electric power in the area of outdoor air temperatures above 6ºС can be from 0.25 MW to 2.15 MW. However, when the outdoor air temperature is less than 2 ºС, work with one lower heating selection becomes irrational. From the point of view of choosing rational modes of operation of turbine plants, the most important are the results of determining the optimal distribution of heat load between network heaters. The gain in electrical power of the turbine can be up to 2.46 MW in the nominal mode of operation with two heaters, and up to 7.84 MW in comparison with the use of single-stage heating. The nature of the influence of the distribution of the heat load indicates that during deprivation from the instructional uniform distribution of the heat load between network heaters, it is possible to obtain additional electricity.

Quantitative and qualitative condylar changes following stabilization splint therapy in patients with temporomandibular joint disorders with and without skeletal lateral mandibular asymmetry: a cone beam computed tomographic study

BackgroundTemporomandibular disorders (TMDs) encompass pain and dysfunction in the jaw, muscles, and adjacent structures. This study aimed to explore the quantitative (condylar position, morphology) and qualitative (bone mineral density (BMD)) therapeutic outcomes following a stabilization splint (S.S.) therapy in adult patients diagnosed with TMD (Arthralgia) with/without lateral mandibular asymmetry (MA) using cone beam computed tomography (CBCT).MethodsIn this retrospective clinical study, 60 adult TMD patients who received S.S. therapy were enrolled and allocated into the TMD group (TMDG) and TMD with MA group (TMD + MAG). The diagnosis was made according to the Diagnostic Criteria for TMD (DC/TMD) AXIS I. MA was measured from the mid-sagittal plane to the Menton point. CBCT was used to scan the temporomandibular joints pre- (T0) and post- (T1)-treatment for three-dimensional analysis. Intra- and intergroup statistical comparisons were performed using the Wilcoxon signed ranks and the Kruskal‒Wallis test.ResultsFor quantitative comparisons, there was a statistically significant difference between T0 and T1 in the joint spaces of TMD + MAG (anterior, superior, posterior, and coronal lateral on the deviated side as well as in the superior, coronal medial joint space of the contralateral side). Morphologically, the deviated side had a narrower condylar width, reduced condylar height, and a steeper eminence angle. In contrast, the contralateral side tended to have a greater condylar length. For qualitative measurements, BMD also showed statistical significance between T0 and T1 in the majority of the condyle slopes (AS, SS, PS, and LS on the deviated side and in AS and MS on the contralateral side) of TMD + MAG. Additionally, only the AS and PS showed significance in TMDG.ConclusionMultiple joint space widening (AJS and CMS) and narrowing (SJS, PJS, and CLS) could characterize the deviated side in TMD + MA. Factors like narrower condylar width, reduced condylar height, and steeper eminence angle on the deviated side can worsen TMD + MA. Proper alignment of the condyle-disc position is essential for optimal function and load distribution, potentially affecting bone mineral density (BMD). MA plays a prominent role in disturbing bone densities. S.S. therapy shows more evident outcomes in TMD + MAG (on the deviated side compared to the contralateral side) than the TMDG.

Optimization of Load Distribution Method for Hydropower Units Based on Output Fluctuation Constraint and Double-Layer Nested Model

During the load distribution of hydropower units, the frequent crossing of vibration zones as well as large output fluctuations affect the stability of the power station. A multi-objective double-layer intelligent nesting model that considers the constraint of the output fluctuation of units is proposed to address these problems. The nonlinear constraint unit commitment optimization model layer is built based on outer dynamic programming, and the load distribution optimization model layer is constructed based on the improved biogeography-based optimization algorithm. Simultaneously, the unit output fluctuation constraint is established based on whether the unit combination changes in order to limit the unit output fluctuation. The results of this model indicate that compared with traditional load allocation models, the application of the method proposed in this paper can reduce the fluctuation range of unit output by 85.01%. In addition, except for the inevitable vibration zone crossings during startup and shutdown processes, the unit does not cross the vibration zone during operation, which greatly improves the unit’s vibration isolation and optimization capabilities. The multi-objective double-layer intelligent nested model proposed in this paper has significant advantages in the field of load allocation for hydropower units. It effectively improves the stability and reliability of unit operation, and this method can be applied to practical load allocation processes. It is of great significance for the research on load allocation optimization of hydropower units.

Bi-Objective Optimization for Interval Max-Plus Linear Systems

This paper investigates the interval-valued-multi-objective-optimization problem, whose objective function is a vector-valued max-plus interval function and the constraint function is a real-affine function. The strong and weak solvabilities of the interval-valued-optimization problem are introduced, and the solvability criteria are established. A necessary and sufficient condition for the strong solvability of the multi-objective-optimization problem is provided. In particular, for the bi-objective-optimization problem, a necessary and sufficient condition of the weak solvability is provided, and all the solvable sub-problems are found out. The interval optimal solution is obtained by constructing the set of all optimal solutions of the solvable sub-problems. The optimal load distribution is used to demonstrate how the presented results work in real-life examples.

Load Balancing using Weight Based scheme in AWS

This paper presents research on load balancing methods for cloud computing platforms. Load balancing is an important element of cloud systems since it allows for optimal resource utilization and consistent performance levels. The study investigates several load balancing technologies and evaluates their effectiveness in coping with the dynamic and heterogeneous nature of cloud workloads. The study conducts experiments and analyses to discover significant parameters influencing load balancing performance, such as workload allocation, server capacity, and network latency. With the weight-based technique, different servers are dynamically given requests based on their weights, which are determined by the processing capacities of the servers. By distributing the work proportionately, this approach aims to decrease user request response times and avoid server overload. The effectiveness of the proposed approach is evaluated via simulations, demonstrating its potential to improve system performance and scalability relative to traditional load balancing methods. Generally speaking, this paper presents an optimized load distribution approach that raises the efficiency and stability of cloud computing systems. The findings enable to improve load balancing methods matched to the particular requirements and issues of cloud computing, eventually enhancing system scalability, reliability, and user