Load Balancing Effect Research Articles

Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing

Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation. The study employs PSO techniques to optimize CNN in order to address the investigation of algorithmic optimization in DL. The results show that the suggested model outperforms the other models like CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), and PSO –SVM(Support Vector Machine) to increase the performance and efficacy of the cloud systems. The experiment is implemented using Python and assessed using Google Cluster dataset that is accessible to the public. Findings: The use of ML and DL techniques are found to be more efficient in cloud infrastructure than the conventional methods. The study examines the performance of the RF, PSO and CNN and the hybrid RF-PSO-CNN models. The accuracy, precision, and F1. Score metrics were used to assess the performance of the classification models. The recommended model RF-PSO-CNN outperforms them with an accuracy of 90% than the contrasted methods like CNN-LSTM, CNN- GRU and PSO-SVM. As a result, both the classification assessment metrics and resource consumption show that the proposed model performs effectively. Novelty: The novel ensemble approach suggests the combined RF-PSO-CNN for LB in cloud Computing. The task predicted by RF is assigned to the resource chosen by PSO and CNN, thereby improving the efficiency of task prediction and resource allocation. Most of the research uses any two ML or DL methods for either predicting the tasks to be scheduled or which resource to allocate. The study uses a combination of the ML (RF) method, bio-inspired algorithm (PSO) and a DL (CNN) model for both task and resource prediction concurrently and it examines the effectiveness of LB in the cloud context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, Random Forest (RF), Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO)

Jamming avoidance trajectory planning and load balancing user association in mmWave UAV-assisted HetECN

Emergency communication network (ECN) can provide fast, efficient and high-capacity communication services for specific areas by using mmWave transmission and unmanned aerial vehicles (UAVs) serving as aerial base stations (ABSs) or relay nodes. Now, in order to satisfy diverse demands, ECN should support different types of nodes, access methods, and traffic distributions, which is referred to as heterogeneous ECN (HetECN). Therefore, inappropriate trajectory planning and unbalanced traffic loading can lead to UAV flight collisions and network congestion. In this article, we jointly optimize UAV jamming avoidance trajectory and user association strategy aimed to load balancing, to maximize the utilization of HetECN. Specifically, an improved artificial potential field (APF) method along with mmWave beam forming technology is used to obtain the jamming avoidance trajectory of UAVs, and the optimal deployment location of UAVs are determined based on the distribution of ground users (GUs). Subsequently, the matching game and alliance game are comprehensively used to determine the load balancing based GU-UAV associated strategy under various GU demands, thereby ensuring traffic load balancing and resource optimization allocation. In addition, altitude fine-tuning have been made to further power consumption, thereby improving overall network efficiency. Simulation results demonstrate that the proposed method can achieve the expected performance in network utilities such as coverage rate, network capacity, load balancing effect of mmWave UAV-assisted HetECNs.

Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions.

TEAM: A Layered-Cooperation Topology Evolution Algorithm for Multi-Sink Internet of Things

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

Analisis Pengaruh Penggunaan Internet Download Manager pada Load Balancing di Mikrotik

Internet Download Manager (IDM) is one of the popular applications used by users to download various types of files from the internet. Load balancing is a technique used to increase the efficiency of using network resources by distributing data traffic evenly across several connection lines. This research aims to determine the extent to which IDM influences the effectiveness of load balancing as well as the influence of using Internet Download Manager (IDM) in the context of load balancing using the Policy-based Load Balancing (PCC), Equal Cost Multi-Path (ECMP), and Next-Hop Target methods. (NTH) on the MikroTik RB751 device. In this research, we used the PPDiOO method for systematic data collection and Quality of Services (QoS) analysis as a parameter for network data collection techniques. The research subject is the system or user who uses the device, while the research object is the impact of using IDM on load balancing performance. The main independent variable is the use of IDM, while the dependent variable is load balancing performance with different methods. Our findings show that using IDM consistently provides significant improvements in network performance compared to not using IDM.

More than Demand Propelling: Load Balancing Effects of E-Prescription on Online Healthcare Platforms

More than Demand Propelling: Load Balancing Effects of E-Prescription on Online Healthcare Platforms

High Performance Evaluation of Helmholtz Potentials Using the Multi-Level Fast Multipole Algorithm

Evaluation of pair potentials is critical in a number of areas of physics. The classical <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -body problem has its root in evaluating the Laplace potential, and has spawned tree-algorithms, the fast multipole method (FMM), as well as kernel independent approaches. Over the years, FMM for Laplace potential has had a profound impact on a number of disciplines as it has been possible to develop highly scalable parallel versions of these algorithms. This is in stark contrast to parallel algorithms for oscillatory potentials such as the Helmholtz potential. The principal bottlenecks to scalable parallelism are the computation and communication costs of operations necessary to traverse up, across, and down the tree. In this article, we analyze asymptotic costs for both computation and communication in a parallel implementation, and describe techniques to overcome bottlenecks and achieve high performance evaluation of the Helmholtz potential for different distributions of particles. We demonstrate that the resulting implementation has a load balancing effect that significantly reduces the time-to-solution and enhances the scale of problems that can be treated using full wave physics.

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect.

Research on Wireless Sensor Network Access Control and Load Balancing in the Industrial Digital Twin Scenario

Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted.

Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes.

Cluster load balancing algorithm based on dynamic consistent hash

In server clusters, the static scheduling algorithm has superior performance when the user visits are relatively stable. In the face of sudden increase in user traffic, the dynamic scheduling algorithm has a better load balancing effect than the static scheduling algorithm. However, in the face of complex network environments, the static scheduling algorithm cannot adjust the load according to the performance of the server in real time. The dynamic scheduling algorithm using a single weight to evaluate server performance is unreliable, and load scheduling with reference to the number of connections has uncertainty. In view of this problem, this paper proposes a cluster load balancing algorithm based on dynamic consistent hash based on the study of load balancing based on LVS clusters. By analyzing the performance and load parameters, we divide the request process into in-cycle and out-of-cycle. By setting up the LVS cluster system, the performance weights, load parameters, number of virtual nodes and cycles of the algorithm in this paper are determined experimentally. Finally, the response time and throughput of the algorithm in this paper are compared with the WRR algorithm and WLC algorithm. The results show that the time and throughput of this algorithm are better than WRR algorithm and WLC algorithm.

Smart city information processing under internet of things and cloud computing.

This study is to explore the smart city information (SCI) processing technology based on the Internet of Things (IoT) and cloud computing, promoting the construction of smart cities in the direction of effective sharing and interconnection. In this study, a SCI system is constructed based on the information islands in the smart construction of various fields in smart cities. The smart environment monitoring, smart transportation, and smart epidemic prevention at the application layer of the SCI system are designed separately. A multi-objective optimization algorithm for cloud computing virtual machine resource allocation method (CC-VMRA method) is proposed, and the application of the IoT and cloud computing technology in the smart city information system is further analysed and simulated for the performance verification. The results show that the multi-objective optimization algorithm in the CC-VMRA method can greatly reduce the number of physical servers in the SCI system (less than 20), and the variance is not higher than 0.0024, which can enable the server cluster to achieve better load balancing effects. In addition, the packet loss rate of the Zigbee protocol used by the IoT gateway in the SCI system is far below the 0.1% indicator, and the delay is less than 10 ms. Therefore, the SCI system constructed by this study shows low latency and high utilization rate, which can provide experimental reference for the later construction of smart city.

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become a hot issue of current research. This article is also in the context of the mobile Internet of Things, researching the solution to the service matching problem. In order to provide high-quality grid services, the grid system needs the support of quality of service (QoS). Based on the existing QoS matching strategy, a service matching strategy based on dynamic grid QoS is proposed. This paper combines semantic information, considers service quality QoS and user preferences, and improves the success rate of service matching through hierarchical matching, so as to provide users with high-quality services. At the same time, the classic Max-Min algorithm in grid task scheduling is analysed, and on this basis, the impact of QoS on task scheduling is fully considered, and combined with the concept of execution cost, a grid task based on QoS constraints (MOS) is proposed. Through simulation verification and analysis, the strategy in this paper not only achieved higher matching performance, but also improved the success rate of task execution, and the effect of system load balancing was also significantly improved.

Analysis and optimisation of server load balancing-based multi-factor integrated algorithm

In distributed server application service deployment, load balancing is an important factor that affects service performance. However, the biggest factor affecting load balancing is the performance of the server and the performance ratio of the requested service execution. This paper used the application of Service-Oriented Architecture system as the embedding point, based on the analysis and comparison with traditional load balancing algorithms, such as: Random load balancing algorithm, Round-Robin algorithm, Consistent Hash algorithm, etc. This paper is aimed to design a new load factor-based algorithm to solve some defects of traditional load balancing algorithm by improving minimum concurrency number and integrating various factors that affect server performance. This paper proposes an optimisation algorithm that can more fully reflect server load capacity. The experiment results show that the load balancing effect of this algorithm has a better effect than the traditional load balancing algorithm.

Analysis and optimisation of server load balancing-based multi-factor integrated algorithm

In distributed server application service deployment, load balancing is an important factor that affects service performance. However, the biggest factor affecting load balancing is the performance of the server and the performance ratio of the requested service execution. This paper used the application of Service-Oriented Architecture system as the embedding point, based on the analysis and comparison with traditional load balancing algorithms, such as: Random load balancing algorithm, Round-Robin algorithm, Consistent Hash algorithm, etc. This paper is aimed to design a new load factor-based algorithm to solve some defects of traditional load balancing algorithm by improving minimum concurrency number and integrating various factors that affect server performance. This paper proposes an optimisation algorithm that can more fully reflect server load capacity. The experiment results show that the load balancing effect of this algorithm has a better effect than the traditional load balancing algorithm.

PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark

Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc. Subspace clustering is the most important method for high-dimensional data clustering. In order to solve the problem of parallel subspace clustering for high-dimensional big data, this paper proposes a parallel subspace clustering algorithm based on spark named PSubCLUS which is inspired by SubCLU, a classical subspace clustering algorithm. While Spark is the most popular big data parallel processing platform currently, PSubCLUS uses the Resilient Distributed Datasets (RDD) provided by Spark to store data points in a distributed way. The two main performing stages of this algorithm, one-dimensional subspace clustering and iterative clustering, can be executed in parallel on each worker node of cluster. PSubCLUS also uses a repartition method based on the number of data points to achieve load balancing. Experimental results show that PSubCLUS has good parallel speedup and ideal load balancing effect, which is suitable for solving the parallel subspace clustering of high-dimensional big data.

The predicted load balancing algorithm based on the dynamic exponential smoothing

AbstractTo a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

An Improved Dynamic Load Balancing Optimization Algorithm Based on CS

Aiming at the problem that the existing load balancing algorithm has a long response delay and a low response connection ratio, which cannot meet the timely processing of large amounts of data in the existing application process, this paper proposes a weighted minimum connection algorithm (CS-WLC). The algorithm utilizes the optimization feature of the Cuckoo Search Algorithm (CS) to classify the server first. Because the algorithm requires fewer parameters and the optimization rate is high, the time for selecting the minimum number of connections is reduced. The experimental results show that under the same conditions, the improved algorithm has smaller response delay and higher throughout capacity, and has better load balancing effect.