Dynamic Load Balancing Algorithms Research Articles

Research on Online Interactive Teaching Platform of College English Combined with Semantic Association Network Modeling

Abstract The emergence of semantic association networks has injected a new impetus for the development of online English teaching and provided a new model reference for the design of online education platforms. In this paper, the research and design of an online interactive teaching platform for college English draws on the algorithmic advantages of the semantic associative network model and utilizes the self-operation of the semantic associative network to realize the functions of autonomous addition, deletion, modification, and checking. The text semantic similarity is predicted by word embedding model, convolutional neural network, and other algorithms so as to better achieve the integration of teaching resources, connecting English knowledge and highlighting the teaching focus in the online teaching process of college English. Dynamic load balancing algorithms are used to solve the problems of short-term surges in the number of visits and the concentration of call requests, and the optimization of load balancing algorithms is further realized through genetic algorithms to finally complete the design of the online teaching interactive platform. Comparison experiments concluded that the semantic association network proposed in this paper could hold a more stable repair effect when cleaning inconsistent data in the dataset, highlighting the effectiveness of the semantic association network model in this paper. The online interactive teaching platform designed in this paper also performs well in the performance test, with only a 0.01% abnormality rate in the concurrency performance test, and the load balancing ability test also achieves the expected effect.

A Review Load balancing algorithms in Fog Computing

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

Numerical simulation of angled-injected liquid jet breakup in supersonic crossflow by a hybrid VOF-LPT method

The breakup of angled-injected liquid jets in supersonic airflow is investigated numerically by a hybrid Volume of Fluid and Lagrangian Particle Tracking (VOF-LPT) method. A Multi-criterion adaptive mesh refinement (AMR) procedure and dynamic load balancing (DLB) algorithm are applied to improve the accuracy of interface and shock wave characteristics and reduce the use of computational resources and liquid mass loss. The flow characteristics of the spray field and penetration depth of the angled-injected liquid jet from the simulations agreed well with the experimental results. Under the supersonic crossflow conditions, the jet has momentum in the counter-flow direction that improves gas-liquid interactions. The penetration depth of the liquid jet increase with the increase of the injection angle. In particular, the penetration depth of the angled-injected liquid jet is given in the: y/d=0.12·sin(2θ/3)·(esin(2θ/3))3.185·q0.389(x/d)0.309. Moreover, the liquid jet at a larger injection angle has a larger spray spread angle and wider wake region due to the larger windward area. Furthermore, the total pressure loss of airflow increases with the injection angle increasing. Considering the total pressure loss for all injection conditions is lower than 14%, the total pressure loss caused by the injection angle increase can be negligible.

DetonationFoam: An open-source solver for simulation of gaseous detonation based on OpenFOAM

Detonation has promising applications in advanced propulsion systems, and numerical simulation is widely used to gain insights into the complex interaction between the hydrodynamic flow and chemical reactions involved in detonation. In this work, detonationFoam, an open-source solver for accurate and efficient simulation of compressible reactive flow and detonation are developed based on OpenFOAM. detonationFoam can simulate compressible, multi-component, reactive flow and it can accurately evaluate the detailed transport coefficients using the mixture-averaged transport model. Compared to rhoCentralFoam, the improved HLLC-P approximate Riemann solver is used in detonationFoam and it helps to accurately resolve shock waves appearing in detonation. Besides, the adaptive mesh refinement and dynamic load balancing algorithms are used in detonationFoam, which greatly improves the computational efficiency. Validation tests including homogenous ignition, unsteady diffusion, shock tube problem, premixed flame, planar detonation, double Mach reflection, detonation cellular structure and oblique detonation wave are conducted. These tests demonstrate that detonationFoam can be used to accurately and efficiently to simulate the compressible, multi-component reactive flow and detonation processes. Program summaryProgram Title: detonationFoamCPC Library link to program files:https://doi.org/10.17632/x45zh4nz28.1Developer's repository link:https://github.com/JieSun-pku/detonationFoamLicensing provisions: GPLv3Programming language: C++Nature of problem: Gaseous detonation involves different length scales and complicated chemistry. To accurately and efficiently simulate the gaseous detonation, adaptive mesh refinement needs to be conducted and detailed chemistry should be considered. Besides, the severe load imbalance caused by the chemical source term evaluation may greatly reduce the computation efficiency.Solution method: An open-source solver, detonationFoam is developed based on OpenFOAM. The species equations considering detailed chemistry are solved in detonationFoam and thereby detonation in a compressible, multi-component, reactive flow can be simulated. The adaptive mesh refinement technique and the dynamic load balancing algorithm are incorporated into detonationFoam. It is demonstrated that detonationFoam can accurately and efficiently simulate gaseous detonation.

A dynamic load balancing algorithm for CFD–DEM simulation with CPU–GPU heterogeneous computing

Computational fluid dynamics combined with discrete element method (CFD–DEM) is widely adopted to study particle–fluid multiphase systems. However, due to the global dynamical evolution and the naturally non-uniform distribution of particles, the computational load in parallel computing varies dynamically in each process, leading to significant load imbalance. Thus, a dynamic load balancing (DLB) algorithm is proposed for parallel CFD–DEM simulations in the CPU–GPU heterogeneous architecture. A type of computing load analyzing grid (AG) is developed which covers the entire simulation region. Then Kalman filtering and linear weighted method is used to solve the weight of fluid grids and particles. The domain decomposition scheme is updated according to the weights of AGs dynamically. This DLB method is tested for gas–solid fluidized beds and periodic gas–solid systems of different scales. Results show that the DLB algorithm can significantly speed up CFD–DEM simulation and the highest performance is 2.28 times faster with DLB.

An Intelligent Server load balancing based on Multi-criteria decision-making in SDN

In an environment of rising internet usage, it is difficult to manage network traffic while maintaining a high quality of service. In highly trafficked networks, load balancers are crucial for ensuring the quality of service. Although different approaches to load-balancing have been proposed in traditional networks, some of them require manual reconfiguration of the device to accommodate new services due to a lack of programmability. These problems can be solved through the use of software-defined networks. This research paper presents a dynamic load-balancing algorithm for software-defined networks based on server response time and content mapping. The proposed technique dispatches requests to servers based on real-time server loads. This technique comprises three different modules, such as a request classification module, a server monitoring module, and an optimized dynamic load-balancing module using content-based routing. There are a variety of robust mathematical tools to address complex problems that have multiple objectives. Multi-Criteria Decision-Making is one of them. The performance of the proposed scheme has been validated by applying the Weighted Sum Method of the multi-criteria decision-making technique. The proposed method Server load balancing based on Multi-criteria Decision Making[SDLB-MCDM] is compared with different load-balancing schemes such as round robin, random, load-balancing scheme based on server response time [LBBSRT], and An SDN-aided mechanism for web load- balancing based on server statistics [SD-WLB]. The experimental results of SDLB-MCDM show a significant improvement of 58% when weights are equal and 50% when unequal weights are assigned to various QoS parameters in comparison with the ROUND ROBIN, RANDOM, LBBSRT and SD-WLB techniques.

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

An Approach Based on Genetic and Grasshopper Optimization Algorithms for Dynamic Load Balancing in CloudIoT

CloudIoT is a new paradigm, which has emerged as a result of the combination of Cloud Computing (CC) and the Internet of Things (IoT). It has experienced a growing and rapid development, and it has become more popular in information and technology (IT) environments because of the advantages it offers. However, due to a strong use of this paradigm, especially in smart cities, the problem of imbalance load has emerged. Indeed, to satisfy the needs of the user, the intelligent objects send the collected data to the virtual machines (VMs) of the cloud in order to be processed. So, it is necessary to have an idea about the load of its VM. Thus, the problem of load balancing between VMs is strongly related to the technique used for the VMs selection. To tackle this problem, we propose in this paper a task scheduler called Scheduler Genetic Grasshopper Algorithm (SGGA). It allows to ensure a dynamic load balancing, as well as the optimization of the makespan and the resource usage. Our proposed SGGA is based on the combination of Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). First, the tasks sent by the IoTs are mapped to the VMs in order to build the initial population, then SGGA performs the genetic algorithm, which has expressed a considerable performance. However, the weakness of the GA is marked by its heaviness caused by the mutation operator, especially when the number of tasks increases. Because of this insufficiency, we have replaced the mutation operator with the grasshopper optimization algorithm. The results of the experiments show that our approach (SGGA) is the most efficient, compared to the recent approaches, in terms of the response time to obtain the optimal solution, makespan, throughput, an average resource utilization rate and the hypervolume indicator.

A Review on Load Balancing Algorithms in Cloud Computing

Cloud computing is a new standard for large-scale distributed and parallel computing that is still in its infancy. At a predetermined time, provides common resources, information, software packages, and other resources in accordance with client requirements. As cloud computing becomes more popular and more people become interested in assistive computing, better and quicker service is necessary. It is necessary to enhance the management of the good load balancing solutions that are already accessible. Recently, numerous clients from all over the world have been seeking various services at a quick rate, negatively impacting cloud computing performance. The various load balancing techniques used in cloud computing are effective in customizing demand by identifying the appropriate virtual machines. In this review, a comparative study will be done between many load balancing algorithms in different cloud computing, as many studies have been done on this important topic. Important research will also be analyzed and defined, and analyzes will be written for each research that talks about dynamic and non-dynamic load balancing techniques and algorithms. Many details will also be mentioned from the researcher's point of view as a summary of each research.

Research on High-Speed Railway Communication Strategy Based on LIFI Technology

In order to improve the effect of high-speed railway communication, this paper studies the high-speed railway communication strategy combined with LIFI technology and improves the effect of high-speed railway communication through intelligent communication methods. Moreover, this paper studies the access point allocation problem in indoor LiFi/WiFi hybrid network, fully considers the dynamic change of network load, and introduces a dynamic load balancing algorithm that determines AP allocation in hybrid network based on the data transmission rate as the switching threshold. In addition, this paper proposes two AP allocation methods, such as dynamic switching threshold and minimum data rate constraint, to construct a reliable high-speed railway communication model. The experimental results verify that the high-speed railway communication strategy based on LIFI technology proposed in this paper can effectively improve the high-speed railway communication effect.

State-of-the-Art Dynamic Load Balancing Algorithms for Cloud Computing

Cloud computing is a technological commitment that allows it to achieve a set goal, improving business performance. Cloud computing offers many applications like scalability, extensive infrastructure, storage, resource pooling, virtualization, and a wide range of online services. Cloud service providers provide a variety of technologies to cloud computing users all the time. Algorithms for load balancing were applied to enhance the speed and performance of nodes in cloud environments and protect each device from affecting their threshold by reducing their performance. Classification of different algorithms was made according to two factors, i.e., the person initiating the algorithms and the current state of the process. Dynamic load balancing algorithms are based on challenges like high availability, scalability, fault tolerance, virtual machine migration, and low response time. Dynamic load balancing algorithms apply some policies because they use the current state of a system. This paper provides the study of state-of-the-art algorithms used in cloud computing and performance parameters, which are based on different load balancing systems.

Оценка накладных расходов при выполнении расчётов на локально измельчаемых сетках

A method for presenting grid data and algorithms for their processing when performing calculations on locally refined adaptive grids is considered. Estimates are obtained of the dependence of the number of operations and computational overheads on such parameters as the size of the base regular grid, the depth of refinement, and the number of processors. Algorithms for dynamic load balancing of processors, which provide a low level of overhead costs, are considered.

Capacity-constrained Wardrop equilibria and application to multi-connectivity in 5G networks

In this paper, a distributed, non-cooperative and dynamic load-balancing algorithm is proposed in the context of multi-commodity adversarial network equilibria with constrained providers’ capacities. The algorithm is proven to converge to a generalised Wardrop user-equilibrium, referred to as Beckmann equilibrium in the literature, in which, for each commodity, the latencies of the unsaturated providers are equalized. The algorithm is then used as a Multi-connectivity algorithm in the context of 5G heterogeneous networks, in which the user equipments are able to use different access networks simultaneously to increase the transmission capacity and/or to improve the transmission reliability. The proposed controller provides a solution for dynamic traffic steering by distributing the traffic load over the available heterogeneous access points, considered as capacity providers. Simulation results validate the approach. The developed network simulator is available as an open-source environment De Santis et al. (2020).

A Proposed Load Balancing Algorithm Over Cloud Computing (Balanced Throttled)

Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.

A Comparative Performance Analysis for Static and Dynamic Load Balancing Techniques in Software Defined Network Environment

Traditional load balancer suffers from inflexibility, high cost and the difficulty to manage the network. Software-Defined Network (SDN) gives a promising solution for traditional load balancer limitations through enabling programmability and centralized control over the network, which offers inexpensive and scalable solutions. This paper investigates the impact of increasing the workload requests from 0 up to 180 requests per second (req/sec) in order to explore average network throughput under the deployment of static as well as dynamic load balancing algorithms by the POX controller. The study was based on the utilization of HTTPerf based on the fact that it provides a flexible facility for the generation of various HTTP workloads as well for the measurement of server performance. Our experiments revealed that as the number of requests increases the throughput increases as well. Dynamic least bandwidth-based load balance scheme has shown a remarkable improvement in terms of average network throughput up to 8%, 3.3% and 2.56 %, as compared with static load balancing schemes like random, round-robin and weighted round-robin. However, Dynamic least bandwidth recorded a slight ineffective improvement less than 1 % was recorded when a comparison was carried out dynamic least connections, this directed us to the fact that their performance was almost the same. Based on what dynamic least bandwidth scheme has achieved in terms of average network throughput, more efforts should be driven by the researchers on algorithm development like introducing static weight for each server as well as the application of the algorithm on several POX controllers to avoid a single point of failure.

Research on dynamic load balancing of data flow under big data platform

In the big data platform, because of the large amount of data, the problem of load imbalance is prominent. Most of the current load balancing methods have problems such as high data flow loss rate and long response time; therefore, more effective load balancing method is urgently needed. Taking HBase as the research subject, the study analyzed the dynamic load balancing method of data flow. First, the HBase platform was introduced briefly, and then the dynamic load-balancing algorithm was designed. The data flow was divided into blocks, and then the load of nodes was predicted based on the grey prediction GM(1,1) model. Finally, the load was migrated through the dynamic adjustable method to achieve load balancing. The experimental results showed that the accuracy of the method for load prediction was high, the average error percentage was 0.93%, and the average response time was short; under 3000 tasks, the response time of the method designed in this study was 14.17% shorter than that of the method combining TV white space (TVWS) and long-term evolution (LTE); the average flow of nodes with the largest load was also smaller, and the data flow loss rate was basically 0%. The experimental results show the effectiveness of the proposed method, which can be further promoted and applied in practice.

PackStealLB: A scalable distributed load balancer based on work stealing and workload discretization

The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application’s workload using heuristics to circumvent the NP-hard complexity of the problem However, scheduling heuristics must be fast to avoid hindering application performance when distributing the workload on large and distributed environments. In this work, we present a technique for low overhead, high quality scheduling decisions for parallel iterative applications. The technique relies on combined application workload information paired with distributed scheduling algorithms. An initial distributed step among scheduling agents group application tasks in packs of similar load to minimize messages among them. This information is used by our scheduling algorithm, PackStealLB, for its distributed-memory work stealing heuristic. Experimental results showed that PackStealLB is able to improve the performance of a molecular dynamics benchmark by up to 41%, outperforming other scheduling algorithms in most scenarios over almost one thousand cores.

Load balancing with Job Migration Algorithm for improving performance on grid computing: Experimental Results

Grid is the collection of geographically distributed computing resources. For efficient management of these resources, the manager must maximize its utilization, which can be achieved by efficient load balancing with Job Migration techniques.Job Migration from overloaded resources to underloaded is an attempt to load balancing across all processors, thus reduce average response time. The decision of migration is based on the information exchange between resources.In this paper, the authors propose a novel Job Migration Algorithm for Dynamic Load Balancing (JMADLB), in which parameters such as CPU load and queue length have been considered and have been used for the selection of overloaded resources (or underloaded ones) in Grid. Here, the overloaded resources do not accept any new job; but, the new jobs are migrated to underloaded resources, even though this mechanism migrate extra jobs to obtain load balancing. The performances of the proposed algorithms were tested in Alea 2 simulator by using different parameters like response time, resources utilization and waiting time in the global queue. In addition, they were compared with other scheduling algorithms such as First Come First Served (FCFS) and Earliest Deadline First (EDF).

A load balancing scheme for gaming server applying reinforcement learning in IoT

A lot of data generated on the game server causes overtime in IoT environment. Recently, both researchers and developers have developed great interests in load balancing schemes in gaming servers. The existing literature have proposed algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading in Internt of Things (IoT) environment. The dynamic load balancing algorithms have applied a technique of calculating the workload of the network and dynamically allocating the workload according to the network situation, taking into account the capacity of the servers. However, the various previous researches proposed are difficult to reflect the real world by imposing a lot of restrictions and assumptions on the IoT environment, and it is not enough to meet the wide range of service requirements for the IoT environment. Therefore, we proposed an agent that applies a deep reinforced learning method to distribute loads for gaming servers. The agent has accomplished this by measuring network loads and analyzing a large amount of user data. We specifically have chosen deep reinforcement learning because no labels would need to be obtained in advance and it enabled our agent to immediately make the right decisions to load balancing in IoT environment. We have showed several siginicicant functions of our proposed scheme and derived through mathematical analysis. Also, we have compared performances of our proposed scheme and a previus research, ProGreGA, widely used scheme through simulation.

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO.