Load Balancing Algorithm Research Articles

Automatically Expanding User-Management System for Massive Users in the Cloud Platform

Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms.

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

ReT-ELBa: A Novel Algorithm for Efficient Load Balancing in Cloud Computing

This paper presents a novel load-balancing algorithm for balancing load on the cloud environment which we call Response Time Efficient Load Balancer (ReT-ELBa). Load balancing involves a dynamic and equal distribution of workloads among processors or Virtual Machines to achieve better resource utilisation. The study gives an insight into the design, implementation and evaluation of an enhanced load-balancing algorithm that allocates tasks across Virtual Machines on the cloud, which minimises Response Time and increases resource utilisation. The ReT-ELBa distributes tasks based on their sizes, the requirements for their executions, as well as the state of the Virtual Machines. The Cloud Analyst simulation tool was employed to simulate or evaluate various cases for the ReT-ELBa in comparison with the Throttled and Round Robin algorithms. The study revealed that the ReT-ELBa outperformed the two algorithms in relation to Response Time. The ReT-ELBa also outperformed the two algorithms in relation to Data Centre Processing Time for all simulated cases, except one (Case 3), which recorded a small difference of 0.07ms. Even the Response Time was the Primary focus of this research, the Data Centre Processing Time of the algorithm was also analysed to present a more elaborate representation of the performances of the algorithms.

A Comprehensive survey of Load Balancing Techniques: From Classic Methods to Modern Algorithms

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

LIGHTWEIGHT ENVIRONMENTS FOR TESTING SPEED AND RELIABILITY OF SOFTWARE BASED ON OPERATING SYSTEM LEVEL VIRTUALIZATION

An environment for performance software testing has been described, based on the use of containerization technology through the use of appropriate software (Docker or others) on the one hand, and a tool for measuring performance and carrying out load testing such as ApacheBench or similar. The advantage of containerization technology for achieving the goal was evaluated. It is shown that in this case, the test environment can be deployed on different computers separately, rather than being deployed centrally. This provides following advantages: less cost in terms of computing resources compared to other ways of deploying environments on local computers. Certain types of environment configurations can be easily distributed and replicated between individual computers and workstations through the distribution and application of prepared configuration files. Compared to cloud environments, this approach does not require additional material costs for support. The scheme of operation of such an environment for non-functional testing has been analyzed.. The environment also allows you to simulate more complex configurations, for example, to study the operation of various load balancing algorithms/systems. The created testing environment was tested on the example of performance of testing web applications. It showed that the created environment allows you to calculate the approximate response time of the application and the presence of failures depending on the number of users who use the application at the same time. Also, load testing allows you to calculate the normal and critical number of application users and predict and prevent potential failures. The mechanism for creating test environments considered in this work can be used to build models that are necessary for studying and/or modeling certain types of software testing based on loads, load balancing algorithms between workstations, and others. Since such an approach can be used on almost any personal workstation with relatively small requirements for computing resources and the deployment and distribution of environments is quite simple, such an approach can be implemented not only in business, but also in universities for courses that involve the use similar environments.

RETRACTED ARTICLE: Application of IoT optical sensing image processing equipment based on load balancing algorithm in sports physical fitness detection

RETRACTED ARTICLE: Application of IoT optical sensing image processing equipment based on load balancing algorithm in sports physical fitness detection

Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms

This paper studies the sensitivity (or insensitivity) of a class of load balancing algorithms that achieve asymptotic zero-waiting in the sub-Halfin-Whitt regime, named LB-zero. Most existing results on zero-waiting load balancing algorithms assume the service time distribution is exponential. This paper establishes the large-system insensitivity of LB-zero for jobs whose service time follows a Coxian distribution with a finite number of phases. This result justifies that LB-zero achieves asymptotic zero-waiting for a large class of service time distributions as the Coxian family is dense in the class of positive-valued distributions. To prove this result, this paper develops a new technique, called “iterative state-space peeling” (ISSP). ISSP first identifies an iterative relation between the upper and lower bounds on the queue states and then proves that the system lives near the fixed point of the iterative bounds with a high probability. Based on ISSP, the steady-state distribution of the queue length is further analyzed by applying Stein’s method in the neighborhood of the fixed point. ISSP, like state-space collapse in heavy-traffic analysis, is a general approach that may be used to study other complex stochastic systems.

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.

Review of Techniques and Algorithms of Load Balancing in Cloud Computing

Review of Techniques and Algorithms of Load Balancing in Cloud Computing

Analysis of performance and trust in load balancing algorithm in cloud computing environment

Analysis of performance and trust in load balancing algorithm in cloud computing environment

A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm

This research offers four work and computer tool setups. The dynamic Resource Allocation Algorithm is crucial to the system. This lets you manage changing supply. Once the PWMA knows how much work is coming up, it may divide resources and plan. The Load Balancing Algorithm (LBA) distributes work evenly to avoid over- or under-utilization and it also provides access content faster via the Adaptive Caching Algorithm (ACA). The proposed system surpasses the top alternative in several domains, such as data transmission, reaction time, energy conservation, load distribution effectiveness, and recovery time from failures. This is because the suggested solution incorporates many disparate approaches. Graphs and charts are visual representations that effectively illustrate the similarities and differences between the two methodologies. The hybrid technique is especially beneficial when the workload is unpredictable and prone to fluctuations. To do this, it instructs you on the fundamentals of efficient and adaptable computer resource management.

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.

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Analysis of the Integration Path of Chinese Traditional Culture in College English Education and Teaching under the Background of Big Data

Abstract The cloud platform teaching system utilizes the optimized load balancing algorithm and DP algorithm to allocate and cluster massive teaching resources respectively, organize massive Chinese cultural knowledge scientifically and rationally into relevant network modules, and present it to English learners in a combination of graphic and text, audio and video. By investigating the selection of resource content and the quality of each functional module on the cloud platform, it can be found that the five primary cultural contents are generally well balanced, with the two that account for the most significant proportion of the content being cultural practices and cultural products, and the proportion of the other three varying little. Most students preferred the functional modules of Learning Center (84.3%), Resource Center, Extracurricular Paradise, and PK Pavilion, but only 12% preferred the Psychological Center module. The correlation coefficient between different factors and the intensity of Chinese culture penetration is the highest, 0.924. The average score of the degree of realization of the five teaching objectives in teaching practice is 3.838, close to “basically realized”. The scores of the four major dimensions of the university students increased significantly (p<0.05). The most significant improvement was seen in the quality of thinking dimension, with a mean increase of 2.678 points.

A secure solution based on load-balancing algorithms between regions in the cloud environment

The problem treated in this article is the storage of sensitive data in the cloud environment and how to choose regions and zones to minimize the number of transfer file events. Handling sensitive data in the global internet network many times can increase risks and minimize security levels. Our work consists of scheduling several files on the different regions based on the security and load balancing parameters in the cloud. Each file is characterized by its size. If data is misplaced from the start it will require a transfer from one region to another and sometimes from one area to another. The objective is to find a schedule that assigns these files to the appropriate region ensuring the load balancing executed in each region to guarantee the minimum number of migrations. This problem is NP-hard. A novel model regarding the regional security and load balancing of files in the cloud environment is proposed in this article. This model is based on the component called “Scheduler” which utilizes the proposed algorithms to solve the problem. This model is a secure solution to guarantee an efficient dispersion of the stored files to avoid the most storage in one region. Consequently, damage to this region does not cause a loss of big data. In addition, a novel method called the “Grouping method” is proposed. Several variants of the application of this method are utilized to propose novel algorithms for solving the studied problem. Initially, seven algorithms are proposed in this article. The experimental results show that there is no dominance between these algorithms. Therefore, three combinations of these seven algorithms generate three other algorithms with better results. Based on the dominance rule, only six algorithms are selected to discuss the performance of the proposed algorithms. Four classes of instances are generated to measure and test the performance of algorithms. In total, 1,360 instances are tested. Three metrics are used to assess the algorithms and make a comparison between them. The experimental results show that the best algorithm is the “Best-value of four algorithms” in 86.5% of cases with an average gap of 0.021 and an average running time of 0.0018 s.

Load-balancing routing algorithms for service congestion avoidance in LEO optical satellite networks

The low-Earth-orbit optical satellite network (LOSN) is an attractive solution to realize the end-to-end service quality of large-scale services, and it is the basis of establishing the 6G mobile network with high throughput and high reliability. However, the current LOSN is practically unable to achieve such high throughput for global service access. First, the centralized deployment of ground gateways will cause a heavy traffic load in the space segment of the LOSN, which becomes the bottleneck constraining the further growth of network throughput. Second, the high-speed movement of LEO satellites will cause continuous movement of traffic load, which will affect the scalability of routing computing. In this paper, two routing algorithms are proposed to increase the throughput in the dynamic LOSN topology. The congestion-aware load balancing (CALB) algorithm is proposed to address the inter-satellite link congestion in the dynamic LOSN topology. Then, a load-balancing routing algorithm based on satellite–ground cooperation (SGC-LB) is proposed to further reduce the impact of the network bottleneck. To evaluate the performance of the proposed routing algorithm, extensive simulations were performed on a 288-satellite Walker-Star constellation with inter-satellite links. The service blockage rate and network bandwidth utilization rate are evaluated showing the effectiveness of the proposed routing algorithm. The network using the SGC-LB algorithm can accommodate 60.00%, 56.00%, 42.22%, and 33.33% more services than using the Shortest Path algorithm, Sway algorithm, CALB algorithm, and Anycast algorithm, respectively, with zero service congestion during the simulation. The SGC-LB also gains a 6.04%, 5.00%, 5.04%, and 0.77% higher network utilization rate than the Shortest Path, Sway, CALB, and Anycast algorithms, respectively.

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

<p>The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).</p>

Exploring Social Credit for Fairness in Cloud Computing Networks

Cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to reap the benefits of cloud computing's advantages at an even lower cost. However, this new paradigm comes with a slew of challenges: (i) security, when operating with the devices of strangers and (ii) fairness when not operating in a market-based system. This paper aims to address these two issues by proposing an algorithm based on social credits for a P2P cloud system that uses a social network to establish its security measures. We implement our new Social Credit algorithm along with two other task-migration-based load-balancing algorithms adapted for a P2P social cloud in “Cloudsim Plus”, and the relative gains are shown in terms of fairness and other related metrics.

A scalable modified deep reinforcement learning algorithm for serverless IoT microservice composition infrastructure in fog layer

Nowadays many modern and Artificial Intelligence (AI) enabled Internet of Things (IoT) applications consist of chains connecting microservices distributed across the fog and cloud layers to achieve a certain functionality. These microservices should be assigned to the available processing instances placed on the things, fog and cloud layers while considering the resource utilization, Quality of Service (QoS) parameters and requirements of the user. Despite several existing optimization approaches, IoT microservice composition remains a challenge in serverless computing architecture due to the large-scale environment, its dynamic nature and the heterogeneous nodes with limited capacity. To address these challenges, a novel IoT microservice composition based on serverless architecture is proposed in the fog layer. Serverless computing is a new paradigm which offers the ability to build scalable applications without worrying about back-end management making this paradigm particularly effective in IoT environment. We specifically propose a modified Deep Reinforcement Learning (DRL)-based Microservice Chaining at Fog Layer (DRLMCF) algorithm to improve the distributed chained microservice placement in order to minimize resource utilization and delay in a fog based serverless architecture. Unlike existing methods, the proposed DRLMCF is built on realistic assumptions and does not require expert tuning or a large amount of labeled data to make optimal decisions. Furthermore, our method is not entirely dependent on a central controller and fog nodes take part in the decision making thus making the algorithm scalable in IoT environment. Compared to state-of-the art methods including load balance, shortest path and random schedule algorithms, evaluation by simulating several real-world scenarios demonstrates the proposed DRLMCF improves end to end delay up to 57.3% and the number of successfully chained microservices by up to 84%.

Use of Enhanced Greedy Algorithm for Load Balancing in Cloud Computing

Because of the Internet’s phenomenal growth in recent years, computing resources are now more widely available. It led to the development of a new computing concept known as Cloud Computing, allowing users to share resources such as networks, servers, storage, applications, services, software, and data across multiple devices on demand for economical and fast. Load balancing is an important branch of cloud computing as it optimizes machine utilization by distributing tasks equally over resources. It occurs among physical hosts or Virtual Machines in a cloud environment. Round robin is a commonly used algorithm in load balancing. RR gives a time quantum for each task and is in circular order. It is noted that it suffers from many problems, such as the waste of time and the high cost. In the present study, the greedy algorithm was enhanced and implemented to allocate and schedule tasks that come to the cloud on Virtual Machines in balance. The task with the longest execution time is given to the virtual machine with the least load using an improved greedy algorithm. The outcomes demonstrate that the suggested algorithm outperformed round robin in makespan. Also, all Virtual Machines in the proposed algorithm finish their work simultaneously, whereas round robin is unbalanced.