Load Balancing In Cloud Computing Research Articles

Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB)

In this paper, we delve into the core of our proposed dynamic load balancing mechanism, the Hybrid Spotted Hyena based Load Balancing algorithm (HSHLB). We begin by presenting a comprehensive overview of the Spotted Hyena Optimization Algorithm (SHOA) and the fundamental behaviors it encompasses, namely migration and attacking. We then introduce the Load Balancing Algorithm (LB) and outline its principles, emphasizing its distinct characteristics. Recognizing the strengths and weaknesses of both SHOA and LB, we embark on a journey to hybridize these two optimization techniques, resulting in the potent HSHLB. We elucidate the intricate process of combining these algorithms, elucidating how HSHLB harnesses their respective strengths while mitigating their limitations. This hybridization is pivotal to the overarching goal of achieving dynamic load balancing within cloud computing environments. As we progress through this section, we provide invaluable insights into the inner workings of HSHLB, offering readers a comprehensive understanding of its algorithmic steps, parameters, and intricacies. For clarity and enhanced comprehension, we incorporate pseudocode or flowcharts to illustrate the practical implementation of HSHLB. In sum, Section 3 lays the foundation for the practical application of HSHLB in achieving dynamic load balancing, setting the stage for subsequent sections where we delve into implementation details, results, and analysis. HSHLB emerges as a promising solution to the multifaceted challenges of load balancing in cloud computing, leveraging the unique strengths of SHOA and LB to optimize resource allocation and enhance Quality of Service (QoS).

Dynamic Load Balancing in Cloud Computing: Improving Efficiency and Performance in Real Life Applications

Dynamic Load Balancing in Cloud Computing: Improving Efficiency and Performance in Real Life Applications

Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm: Survey

Load balancing is essential within the cloud. Distributing the workload over many servers permits for a more efficient machine. Optimal resource use, decreased time to response, and more advantageous gadget performance are all results. Among the numerous challenges that traditional methods of accomplishing equilibrium come upon are problems with adaptability and the unpredictability of cloud structures. This survey focuses on the demanding situations associated with load balancing in cloud computing and proposes the usage of specialized algorithms as an answer. It determines the most reliable alternatives for facts placement in the cloud. Meta-heuristic algorithms draw concept from herbal approaches, which includes the foraging behavior of ants or the hunting strategies of predators. These algorithms excel at rapidly finding super answers in situations where conventional optimization algorithms fail to get great results. Various strategies have been proposed to attain paintings equilibrium in cloud computing. Evidence has shown that these techniques can enhance work balance in cloud computing.

Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms

Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.

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.

Implementing Load Balancing in Cloud Computing with MTB

Implementing Load Balancing in Cloud Computing with MTB

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.

Review of Techniques and Algorithms of Load Balancing in Cloud Computing

Review of Techniques and Algorithms of Load Balancing in Cloud Computing

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.

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