Load Balancing In Cloud Computing Research Articles

Dynamic Load Balancing in Cloud Computing: A Review and a Novel Approach

In cloud computing, load balancing is essential since it guarantees effective resource utilisation and reduces response times. It effectively distributes incoming workload across available servers. Load balancing involves dividing tasks among multiple systems or resources over the internet. By distributing traffic and workloads, load balancing ensures that no server or computer is overloaded, underutilized, or idle in a cloud computing environment. To enhance overall performance in the cloud, it optimises a number of variables, including execution time, response time, and system stability. To increase the effectiveness and reliability of cloud services, load balancing evenly distributes traffic, workloads, and computing resources across the environment. The proposed method, Enhanced Dynamic Load Balancing for Cloud Computing, adjusts load distribution and maintains a balanced system by considering variables like server capacity, workload distribution, and system load. By incorporating these factors and employing adaptive threshold adjustment, this approach optimizes resource allocation and enhances system performance. Experimental research shows that the proposed new novel approach is more effective and efficient than current load balancing techniques. In this context of cloud computing, this ground-breaking method offers a workable substitute for dynamic load balancing.

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.

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.

Resource Aware Orthogonal Projected Regressive MapReduce Lottery Load Balancing in Cloud Computing

Cloud Computing is an internet-based network technology that provides various services and requirements to customers through online computing resources. In the cloud, Load balancing is the most significant issue that includes both hardware and software platforms for the execution of demand of the user request. Furthermore, for handling multiple user requests, load balancing is necessary. Therefore, an efficient load-balancing technique is required to optimize and ensure user satisfaction by utilizing the virtual machine's resources efficiently. A novel Orthogonal Projected Regressive MapReduce Lottery Load Balancing (PORLOB) technique is introduced for resource-efficient task scheduling with minimal Makespan and complexity. In the PORLOB technique, many cloud user requests are transmitted to the cloud server from different locations. The load balancer uses the index table for maintaining the virtual machines. The MapReduce function includes two steps, namely, map and reduce. Based on the resource estimation, the map function performs the regression analysis and provides three resource statuses of the virtual machine: overloaded, less loaded, and balanced. In the reduction phase, the load balancer uses the lottery scheduling technique to balance the workload by migrating the task from an overloaded Virtual Machine to a less-loaded VM.

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Cloud computing load balancing based on improved genetic algorithm

Cloud computing load balancing based on improved genetic algorithm

Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework

Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework

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.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

Retracted: Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm.

[This retracts the article DOI: 10.1155/2022/3120883.].

Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Inquisitive Genetic-Based Wolf Optimization for Load Balancing in Cloud Computing

Abstract Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods.

DYNAMIC LOAD BALANCING FOR CLOUD: A REVIEW

The newest internet-based technology that places a strong emphasis on business computing is called cloud computing. Load balancing aids in enhancing the centralized server's efficiency. Using an analytical tool called cloud analyst, numerous algorithms are analyzed in the current work. Load balancing algorithms are also compared. The cloud computing load balancing issue is a significant one, a crucial element for proper system performance, and it has the potential to slow the industry's rapid growth. Recently, there has been a fast increase in the number of customers from all over the world requesting various services.

Dispatcher I-queue Optimization in Join Idle Queue Algorithm for Improved Load Balancing in Cloud Computing

Objectives: Modern data centers serving web and mobile applications employ distributed load balancers. The Join Idle Queue (JIQ) algorithm is ideally suited for load balancing in a distributed setup. It attains fast response time by directing service requests to idle servers which can immediately process them. However, JIQ is not optimal in tracking idle servers leaving room for improvement.
 Methods: We observed that JIQ assigns idle servers non-uniformly to load balancers leaving some load balancers with no access to idle servers. We propose a variant of the JIQ algorithm, Join Idle Queue Dispatcher I-queue Optimization (JIQ-DIO), which increases the probability of load balancers having access to idle servers without additional communication overhead leading to improved response time.
 Findings: We simulated JIQ-DIO on CloudSim Plus 3.0 and compared it with standard JIQ and its different variants. JIQ-DIO was found to increase the probability of incoming requests being directed to idle servers and lead to more than two-fold improvement in response time across a broad range of parameters.

Fractional IWSOA-LB: Fractional Improved Whale Social Optimization Based VM Migration Strategy for Load Balancing in Cloud Computing

Fractional IWSOA-LB: Fractional Improved Whale Social Optimization Based VM Migration Strategy for Load Balancing in Cloud Computing

Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

Cloud Computing is the dynamic provisioning of resources to provide services to end-users over the internet. The realization of cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, load balancing is the most challenging one. Therefore, in the past few years, research into various static and dynamic algorithms to achieve optimal results is gaining importance. This research proposes Swarm Intelligence (SI) as a load-balancing solution for cloud computing. Several alternatives in the literature (like genetic algorithm, ACO, PSO, BAT, GWO, and many others) are investigated, but none consider the load balancing convergence time with global optimization. Among these algorithms, this research emphasizes Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). A combined approach of GWO-PSO that capitalizes on the benefits of fast convergence and global optimization is proposed in this paper. These two techniques enhance system efficiency and resource allocation, working together to solve the load-balancing challenge. Compared to other traditional approaches, the findings of this research are promising while achieving globally optimized fast convergence and reducing overall response time. On average, the overall response time of the proposed technique is reduced to 12% as compared to other algorithms. Furthermore, the best optimal value obtained from the objective function of the proposed GWO-PSO algorithm improves PSO to 97.253% in terms of convergence.

Cloud computing load balancing based on improved genetic algorithm

Cloud computing load balancing based on improved genetic algorithm

Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing

You can’t obtain the outcomes you need without planning, thus it’s at the heart of cloud computing. This article’s major goal is to decrease value-added time, increase resource utilisation, and make cloud services viable for a single activity. In recent years, metaheuristic algorithms have drew attention to the correct functioning of work scheduling algorithms among the many job scheduling techniques. With sports leagues, the algorithm based on the League Championship (LCA) is fascinating because it can be used to identify the best team/task for programming.This article uses the Improved League Championship Algorithm (ILCA) to schedule tasks, reducing deployment time, cloud usage, and cost. The ILCA is implemented through the Cloudsim simulator and the Java programming language with a nonpreventive planning strategy. ILCA also enhances economies of scale and minimises the value of using the cloud. As it has proven to be versatile in terms of time to manufacture, resource usage and economics, ILCA could be a good candidate for a cloud broker as it has proven to be versatile in termsof time to manufacture, resource usage and economics usage.

The Availability and Load Balancing in Cloud Computing

Cloud computing is new model that permit to the clients, associations, to buy the necessary adminis-trations as indicated by our requirements. It is used to upload their data and retrieve data accordingto the needs over the internet. This model offer several services like to store data, easy and conve-nient web services, etc. In this era Cloud Application is developed the different services some arePlatform-as-a-Service (Paas), Software-as-a-Service (Saas), Infrastructures-as-a-Service (Iaas), andCloud computing improve their services day by client also demand reliable and new services forefficiency and reliability. Load Balancing be major provocation in cloud computing. Techniquecalled load balancing is used to distribute load of the data over the cloud network. It is also used tominimize the resource usage.