Load Balancing Method Research Articles

Adaptive Load Balancing Using RR and ALB: Resource Provisioning in Cloud

Cloud Computing context, load balancing is an issue. With a rise in the number of cloud-based technology users and their need for a broad range of services utilizing resources successfully or effectively in a cloud environment is referred to as load balancing, has become a significant obstacle. Load balancing is crucial in storage systems to increase network capacity and speed up response times. The main goal is to present a new load-balancing mechanism that can balance incoming requests from users all over globally who are in different regions requesting data from remote data sources. This method will combine effective scheduling and cloud-based techniques. A dynamic load balancing method was developed to ensure that cloud environments have the ability to respond rapidly, in addition to running cloud resources efficiently and speeding up job processing times. Applications' incoming traffic is automatically split up across a number of targets, including Amazon EC2 instances, network addresses, and other entities by elastic load balancing. Elastic load balancing offers three distinct classifications of load balancer, and each one provides high availability, intelligent scalability, and robust security to guarantee the error-free functioning of your applications. Application load balancing and round robin are the two load balancing mechanisms in database cloud that are focus of this research study.

Artificial neural network based load balancing scheme for top of rack switches in optical data centers

Abstract Data centers serve as dedicated facilities for housing computer systems and their related components, including telecommunications and storage systems. They typically have high levels of security and environmental controls to ensure that the equipment housed within them functions optimally. Data center networks (DCNs) often employ load balancing algorithms to handle large volumes of traffic and ensure that all servers and switches are utilized equally, keeping the network running smoothly. However, as load on the server varies, therefore dynamic traffic management systems that can adjust traffic flow in real-time based on the current traffic state is required. This study presents an artificial neural network-based load balancing method. By training a feed-forward artificial neural network (ANN) using a back propagation (BP) learning algorithm, it evenly distributes workload over all of the nodes. Simulation results are also presented to prove the usefulness of the proposed load balancing mechanism. It is found that the load balancing scheme can reduce the packet blocking probability (PBP) by 10 folds and delay by about nearly 11 percent.

A Q-Learning-Based Load Balancing Method for Real-Time Task Processing in Edge-Cloud Networks

Edge computing has emerged as a promising solution to reduce communication delays and enhance the performance of real-time applications. However, due to the limited processing power of edge servers, it is challenging to achieve efficient task processing. In this paper, we propose a Q-learning-based load-balancing method that optimizes the distribution of real-time tasks between edge servers and cloud servers to reduce processing time. The proposed method is dynamic and adaptive, taking into account the constantly changing network status and server usage. To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are conducted in an Edge-Cloud network environment. The simulation results demonstrate that the proposed method significantly reduces processing time compared to traditional static load-balancing methods. The Q-learning algorithm enables the load-balancing system to dynamically learn the optimal decision-making strategy to allocate tasks to the most appropriate server. Overall, the proposed Q-learning-based load-balancing method provides a dynamic and efficient solution to balance the workload between edge servers and cloud servers. The proposed method effectively achieves real-time task processing in edge computing environments and can contribute to the development of high-performance edge computing systems.

Web Server Load Balance Design In Internet Network Using Nth Method

This study aims to build a load balance web server using the NTH load balance method, apply a balanced distribution of connection paths to the two web servers, and reduce the traffic load on the connection path and also the web server. This study uses the action research method, which uses this method to describe, interpret and explain a condition at the same time as carrying out interventions aimed at improvement or participation. The results of this study indicate that web server load balance can be carried out using the NTH method which is applied to proxy routers. The application of the NTH method can divide the connection path between the two servers in a balanced manner and can reduce the traffic load on each predetermined connection path. In addition to applying load balance, data replication in the database can also be done as well as synchronizing files in the web application directory..

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

Enable the proactively load-balanced control plane for SDN via intelligent switch-to-controller selection strategy

The novelty of the software-defined network(SDN) is to separate the control plane and the data plane for easier manipulation of the network. The distributed control plane is designed to achieve more powerful computation capacity and address the single-point failure problem. However, it also poses a new challenge that how to arrange switch-controller associations effectively. The direct static configuration cannot adapt the time-varying requests from switches well, and it would result in imbalance problems on the control plane and cause long-tail latency. Thus, it is necessary to take proper actions to adjust the switch-to-controller association dynamically. Existing controller-based load balancing methods need to communicate with the switches frequently and incur not only high assumptions of the rare control channel of SDN but also high computation costs. In this paper, we provide a switch-based solution that puts Reinforcement Learning agents on all Switches (RLoS). Instead of setting static rules predefined by operators, RLoS makes each switch actively select the best controller. RLoS treats every switch as an independent agent with its own neural network and parameters. With the carefully designed training algorithm, the agents could choose their preferable controllers via their local information. The results show that even with partial observation, the RLoS still can achieve considerable improvement in the load balance among all controllers compared with those controller-based association benchmarks. Our RLoS could decrease the maximum response latency among controllers by about 5%∼15% under different scenarios on average.

In Improved Algorithm for Load Balancing techniques Over Cloud Computing

The term "cloud computing" describes the services and programmes that data centres make available to users all over the world. Online access is provided for all services and programmes. Virtualization is a crucial component of the cloud computing environment, and virtual machine migration is one of its key components. Virtual machine migration is the process of moving an operational virtual machine to a destination computer where it continues to function normally with all of its installed programmes and operating system. Making server balancing easier. By dispersing the workload among available servers, this boosts performance. There are several load balancing algorithms, which may be divided into static and dynamic load balancing methods.

Reinforcement learning for communication load balancing: approaches and challenges

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

Design, static analysis, and dynamic system modeling of 3-PPRS parallel manipulator with load-balancing UPS leg

AbstractAs a heavy load is applied to the parallel manipulators, it causes inaccuracies while positioning the end-effector or unbalanced dynamic forces in the legs. Various load-balancing techniques overcome this. However, the disadvantage of most load-balancing mechanisms is that they add inertia to the assembly and decrease the speed of motion. This article studies a new load-balancing method (a passive damper mechanism). The passive balancing mechanism is proposed to negate the inertia effects while countering the static inaccuracies in the parallel mechanism. This is verified by the structural analysis of the mechanism. The impact of the damper element on the dynamics of the mechanism is unknown. Hence, a complete mathematical model for the balancing mechanism has been developed to study its impact on the dynamics of the entire structure. Laplace transformations characterize the system response. The inclusion of a passive damper in a 3-prismatic-prismatic-revolute-spherical system was examined and found to be stable and critically damped. Such a passive damper was envisaged to facilitate additional force transmission for the actuators, and the DC gain from the system response validates the torque support for the actuators.

Network Design Using Load Balancing Equal Cost Multi Path (ECMP) Method At Lexima Consultant Office

The development of technological advances also contributed to the advancement of Internet technology. Where the Internet is one of the needs that is considered important in all fields, this is evidenced by the number of Internet users from 2020 to 2023 having a significant increase. Lexima Consultant as one of the institutions engaged in information system creation services, of course, requires an internet connection. However, there are several obstacles faced, one of which is the absence of a load balancing and failover network system which sometimes makes the need for internet access with large bandwidth allocations to be disrupted and even cut off. Implementing the right technique at the Lexima Consultant office for sending data packets as the main factor supporting activities is certainly very much needed. In the research conducted, a load balancing technique was used using the ECMP method to optimize network performance and avoid internet network disturbances. From the research conducted, it can be concluded that the implementation of the load balancing method using the ECMP method has been able to divide connection paths and failover can run properly, so the possibility of disruption in the form of a network connection loss will be very small

The Network Load Balancer in Decentrilized Systems

This article explores the implementation of network load balancing in decentralized systems using OpenWrt, Quality of Service (QoS), and traffic balancing techniques. The increasing demand for high-quality net- work services and the surge in network traffic requires the adoption of more efficient load-balancing methods to main- tain network performance. This paper discusses the use of OpenWrt, an open-source firmware for network routers, to configure and manage network traffic. The article also covers the implementation of QoS and traffic balancing techniques to optimize network performance and reduce network congestion. The study employs iperf3 to evaluate network performance and demonstrates the effectiveness of the proposed network load-balancing approach. The index terms include OpenWrt, QoS, balancing, traffic, and ip- erf3.

Load balancing techniques in cloud platform: A systematic study

In the current scenario, researchers have made a new invention and added to the computing paradigm every next second. Cloud computing is one of the most demanding, practical, accessible and extended technologies based on ‘pay as per use model’ and works on virtualisation via internet. Data sharing and accessing have become easy by properly organising various resources, such as storage, servers, development tools, software, etc, in cloud. Handling these resources has faced many challenges, such as cost management, system performance, migration, load imbalance, reliability and privacy etc. Load imbalance is one of the most important factors which are solved by load balancing techniques. This paper introduced the detailed classification of load balancing methods and techniques that are taken as a solution to overcome such problems and also helps future researchers. Also given is a proposed model for load balancing and some comparative studies of the heuristics methods based on platforms and simulator tools.

A Survey on Load Balancing in Mobile Cloud Computing using Cloudlet

Mobile applications and tasks are huge and handle huge data. These applications are offloaded and executed in the cloud. Instead of executing in the cloud, the cloudlet which is a small cloud situated near the access point is used for execution. The cloudlet has less number of VMs. When more tasks are offloaded to the cloudlet, there is no VM free as VMs are busy in executing tasks. This cloudlet is full of tasks and all VMs are busy. But nearby another cloudlet may have free VMs. Those cloudlets have free VMs. So the tasks that do not get the VM in the near cloudlet, are offloaded to nearby cloudlets. So all the cloudlets now have tasks for execution and resources are utilized optimally. This survey discusses the various load balancing methods proposed by different researchers and their issues.

Load balanced locality-aware parallel SGD on multicore architectures for latent factor based collaborative filtering

We investigate the parallelization of Stochastic Gradient Descent (SGD) for matrix completion on multicore architectures. We provide an experimental analysis of current SGD algorithms to find out their bottlenecks and limitations. Grid-based methods suffer from load imbalance among 2D blocks of the rating matrix, especially when datasets are skewed and sparse. Asynchronous methods, on the other hand, can face cache issues due to their memory access pattern. We propose bin-packing-based block balancing methods that are alternative to the recently proposed BaPa method. We then introduce Locality Aware SGD (LASGD), a grid-based asynchronous parallel SGD algorithm that efficiently utilizes cache by changing nonzero update sequence without affecting factor update order and carefully arranging latent factor matrices in the memory. Combined with our proposed load balancing methods, our experiments show that LASGD performs significantly better than alternative approaches in parallel shared-memory systems.

Load balancing inside programmable data planes based on network modeling prediction using a GNN with network behaviors

In data center networks, existing control plane- and end host-based load-balancing methods are encumbered by excessively large decision delays during rapid reactions to microbursts. However, existing programmable data plane-based load balancing methods require large overheads involved in active probing. Accurate network modeling can optimize load balancing. However, existing modeling methods suffer from low generalization and high overhead. In this study, we propose a network modeling method based on a graph neural network (GNN) with basic network behavior (hereinafter called GNN-Behavior). This is derived from two inherent correlations observed: the correlation between global network behavior and basic network behavior and the correlation among basic network behaviors. We employed the GNN with an improved message-passing neural network to learn such two inherent correlations. Particularly, we considered modeling end-to-end delay as a use case to validate GNN-Behavior. Furthermore, we propose a packet-level load-balancing scheme inside programmable data planes (PDPs) based on the accurate prediction of end-to-end delay from the GNN-Behavior model(LBPP). LBPP is a control plane-PDP collaborative method that integrates a global view from a controller and quick response from switches. Experimental results demonstrate the feasibility and effectiveness of the GNN-Behavior and LBPP. Compared with queuing theory (QT), RouteNet, and GNN-based scheme, GNN-Behavior increases goodness of fit (R2) by 73.1%, 11.1%, and 3.74%, respectively. Under an unknown traffic control strategy, the generalization ability of GNN-Behavior is considerably better than that of QT and RouteNet. Compared with flow-level ECMP, flowlet-level LetFlow, and packet-level DRILL, LBPP can reduce average flow completion time by up to 43.9%, 37.4%, and 17.2%, respectively.

FLoadNet: Load Balancing in Fog Networks With Cooperative Multiagent Using Actor–Critic Method

The growing demands of the Internet of Things (IoT) require a platform that supports real-time interactions and high availability of services to devices. In this context, the fog computing paradigm has emerged as an attractive solution for processing the data of IoT applications. Owing to the unpredictable traffic demands and resource heterogeneity in the fog environment, a smart workload distribution is essential to achieve high resource utilization and computing efficiency. To this end, this paper considers a joint link and server load balancing problem with multiple cooperative access points, (APs), in a combined edge-fog-cloud environment. The joint optimization problem is formulated as a stochastic game, and an actor-critic reinforcement learning framework, called FLoadNet, is proposed to optimize the joint policy of the multi-agents. FLoadNet consists of a centralized critic network, with parameter sharing and distributed individual actor networks in all the APs. Due to the learning dynamics and partially observable environment, we propose an extended critic network model, where cooperative APs learn to communicate among themselves while evaluating the value function. Unlike previous studies, the proposed critic network is designed to train both value and message functions, which is shown to significantly reduce the computational cost. The main goal of this work is to advance the development of efficient edge learning and the application of distributed learning algorithms specifically to fog network load balancing. The experimental results show that FLoadNet outperforms baseline load balancing methods.

Devising a method for balancing the load on a territorially distributed foggy environment

This study solves the task to redistribute the load on a geographically distributed foggy environment in order to achieve a load balance of virtual clusters. The necessity and possibility of developing a universal and at the same time scientifically based approach to load balancing has been determined. Object of study: the process of redistribution of load in a foggy environment between virtual, geographically distributed clusters. A load balancing method makes it possible to reduce delays and decrease the time for completing tasks on foggy nodes, which brings task processing closer to real time. To solve the task, a mathematical model of the functioning of a separate cluster in a foggy environment has been built. As a result of modeling, the problem of finding the optimal distribution of tasks across the nodes of the virtual cluster was obtained. The limitations of the problem take into account the characteristics of the physical nodes of support for the virtual cluster. The process of distributing the additional load was also simulated through the graph representation of tasks entering virtual clusters. The task to devise a method for load transfer between virtual clusters within a foggy environment is solved using the proposed iterative algorithm for finding a suitable cluster and placing the load. The simulation results showed that the balance of the foggy environment when using the proposed method increases significantly provided the network load is small. The scope of application of the results includes geographically distributed foggy systems, in particular the foggy layer of the industrial Internet of Things. A necessary practical condition for using the proposed results is the non-exceeding the specified limit of the total load on the foggy medium, usually 70 %

An Energy-Aware Load Balancing Method for IoT-Based Smart Recycling Machines Using an Artificial Chemical Reaction Optimization Algorithm

Recycling is very important for a sustainable and clean environment. Developed and developing countries are both facing the problem of waste management and recycling issues. On the other hand, the Internet of Things (IoT) is a famous and applicable infrastructure used to provide connection between physical devices. It is an important technology that has been researched and implemented in recent years that promises to positively influence several industries, including recycling and trash management. The impact of the IoT on recycling and waste management is examined using standard operating practices in recycling. Recycling facilities, for instance, can use IoT to manage and keep an eye on the recycling situation in various places while allocating the logistics for transportation and distribution processes to minimize recycling costs and lead times. So, companies can use historical patterns to track usage trends in their service regions, assess their accessibility to gather resources, and arrange their activities accordingly. Additionally, energy is a significant aspect of the IoT since several devices will be linked to the internet, and the devices, sensors, nodes, and objects are all energy-restricted. Because the devices are constrained by their nature, the load-balancing protocol is crucial in an IoT ecosystem. Due to the importance of this issue, this study presents an energy-aware load-balancing method for IoT-based smart recycling machines using an artificial chemical reaction optimization algorithm. The experimental results indicated that the proposed solution could achieve excellent performance. According to the obtained results, the imbalance degree (5.44%), energy consumption (11.38%), and delay time (9.05%) were reduced using the proposed method.

A novel approach for new architecture for green data centre

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.

A Survey on Issues and Challenges of Load Balancing Techniques in Cloud Computing Environment

Abstract: Cloud computing has transformed user services by introducing a new model that allows for pay-per-use access to information technology services at any time and from any location. Numerous businesses are moving their operations to the cloud as a result of the flexibility offered by cloud services, and additional data centers are being set up by service providers to provide users with services. However, it is essential to ensure efficient resource utilization and cost-effective task execution. Based on load balancing, task scheduling, resource management, quality of service, and workload management, several methods to improve performance and resource use have been reported in the literature. Data centers can use load balancing in the cloud to prevent virtual machines from being overloaded or under loaded, which is a problem in and of itself in cloud computing. As a result, developers and researchers must develop and implement a suitable load balancer for distributed and parallel cloud environments. In order for researchers to develop algorithms that are more effective, this survey provides a cutting-edge review of issues and obstacles associated with existing load-balancing methods.