Load Balancing Algorithm Research Articles

Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.

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.

Local Scheduling in KubeEdge-Based Edge Computing Environment.

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as network management between cloud and edge nodes. However, the resulting cloud-based systems are subject to several limitations. In this study, we evaluated the performance of KubeEdge in terms of the computational resource distribution and delay between edge nodes. We found that forwarding traffic between edge nodes degrades the throughput of clusters and causes service delay in edge computing environments. Based on these results, we proposed a local scheduling scheme that handles user traffic locally at each edge node. The performance evaluation results revealed that local scheduling outperforms the existing load-balancing algorithm in the edge computing environment.

Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.

Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections

5G wireless networks have as one of the main characteristics the large-scale deployment of small cells (microcells, picocells, etc.), which is expected to bring several advantages in what concerns the high speed and low latency connectivity of the users. This large-scale deployment of small cells also raises several technical challenges, provisioning the backhaul connectivity being one of them. The paper considers the situations when small cells are deployed temporarily or are deployed in a vehicle transporting many passengers, situations when the traditional wired or wireless backhaul solutions could be too costly to be used. The paper proposes, as an alternative solution, the use as backhaul connections of the wireless links set up in the macro cells which cover the location of the small cell. The paper proposes several Game Theory (GT)-based Load-Balancing (LB) algorithms for distributing the traffic of the small cell users over the macro cell links. The proposed LB algorithms are evaluated by computer simulations and are compared with “classical” LB algorithms considered as references. The performed computer simulations show that the auction-based algorithms have the best performance in terms of delay suffered by the transmitted data packets, while the selfish routing type algorithm has weaker performance, even behaving poorly than some of the reference non-GT-based algorithms. The paper also considers the situation when several small cell APs are deployed in a limited area or a vehicle and the user groups that attach to different APs should be identified. The paper proposes two GT-based user clustering algorithms, and the performance of these algorithms are evaluated by computer simulations. These simulations show that even simple clustering algorithms could improve the distribution of the traffic over the neighbor small cell APs and reduce the delay experienced by the data packets in the transmission system.

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

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

Soft Computing Techniques to Analyze the Load Balancing in Cloud Environment

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.

Classification of Request-Based Mobility Load Balancing in Fog Computing

Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request. The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests. LBA does the operation based on these classifications. The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs. The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance, which was attained by (90.8%). Using LBA, several metrics may be examined, including Response Time (RT), delay (d), Energy Consumption (EC), and latency. Through the on-demand provisioning of necessary resources to IoT users, our suggested LBA assures effective resource usage.

Resource Scheduling and Load Balancing Algorithms in Cloud Computing

A technology called cloud computing makes it possible to offer computing resources including storage, software, and services over the internet. Users can access these resources whenever they need them and just pay for what they actually use. There is a strict need to maintain track of all the resources which are being used by machines to perform certain activities such as scheduling of tasks. Resource scheduling and load balancing are important techniques used in modern computing systems to improve performance and resource utilization. Resource scheduling algorithms are used to allocate resources such as CPU, memory, and storage to different tasks or processes. Load balancing algorithms, on the other hand, distribute incoming traffic or workloads across multiple servers or resources to ensure that no single resource becomes overloaded. These techniques are essential for maintaining the stability and efficiency of large-scale systems, such as cloud computing environments, web services, and distributed systems. There are various kinds of load-balancing algorithms that differ in terms of their complexity, flexibility, and performance. Selecting the right algorithm for a specific system depends on the system's specific requirements and constraints. Overall, resource scheduling and load-balancing algorithms play a crucial role in ensuring the smooth operation of modern computing systems and are an active area of research and development.

A dynamic approach for selecting load balancing algorithm in cloud

The collection of interconnected computers that constitutes more than one united computing resource is known as the Cloud. In recent years, the advancement of cloud computing has facilitated the rapid arrangement of interconnected data centers that are geographically dispersed, offering high-quality and dependable services. Scalable traffic management has recently been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, reducing latency during multidimensional resource allocation still remains a challenge. Hence, there is a need for efficient resource scheduling to ensure load optimization in the cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The goal of the proposed system is to select the required load balancing algorithm to enhance resource utilization. Simulations were conducted to evaluate effectiveness using the Cloudsim simulator in cloud data centers, and the results show that the proposed method achieves better performance in terms of the average success rate, resource scheduling efficiency, and response time. The dynamic nature of cloud environments requires constant adaptation in resource allocation strategies. This necessitates the development of algorithms capable of handling diverse workloads efficiently. Additionally, the increasing complexity of applications and services hosted on the cloud demands a comprehensive approach that considers not only load balancing but also the intricacies of resource utilization. Furthermore, the proposed algorithm focuses on predictive analytics to anticipate fluctuations in demand and adjust resource allocation preemptively. By incorporating machine learning techniques, the system can adapt to changing patterns, ensuring optimal performance even in unpredictable scenarios. This holistic approach addresses the evolving challenges in cloud computing, providing a robust foundation for reliable and efficient service provisioning.

Distributed Algorithms for Resource Allocation and Load Balancing

Modern computing is increasingly using distributed systems, and improving their load distribution and resource allocation is essential for obtaining optimal performance. Distributed algorithms for resource allocation and load balancing employ a variety of techniques, including heuristic-based, optimization-based, and machine learning-based ones. In this work, we present a review of distributed load-balancing and resource-allocation approaches. We explore the difficulties in developing efficient algorithms and emphasise the need of meticulously analysing and contrasting various algorithms in light of the requirements of a certain system and workload. Additionally, based on the concept of particle swarm optimisation, we present a distributed method for load balancing and resource allocation in cloud computing environments. Our suggested method tries to reduce the average task waiting time while simultaneously maintaining some semblance of resource parity among nodes. By putting our technique through its paces in a simulated cloud computing environment and examining the outcomes, we compare it against cutting-edge algorithms. Our research demonstrates that our suggested technique has the potential to greatly improve system performance by reducing the typical amount of task waiting time and ensuring that the load is distributed evenly among nodes. This shows how particle swarm optimisation may be used to create efficient distributed load-balancing and resource-allocation algorithms.

Cost-Effective Scheduling and Load Balancing Algorithms in Cloud Computing Using Learning Automata

Cloud computing is a distributed computing model in which access is based on demand. A cloud computing environment includes a wide variety of resource suppliers and consumers. Hence, efficient and effective methods for task scheduling and load balancing are required. This paper presents a new approach to task scheduling and load balancing in the cloud computing environment with an emphasis on the cost-efficiency of task execution through resources. The proposed algorithms are based on the fair distribution of jobs between machines, which will prevent the unconventional increase in the price of a machine and the unemployment of other machines. The two parameters Total Cost and Final Cost are designed to achieve the mentioned goal. Applying these two parameters will create a fair basis for job scheduling and load balancing. To implement the proposed approach, learning automata are used as an effective and efficient technique in reinforcement learning. Finally, to show the effectiveness of the proposed algorithms we conducted simulations using CloudSim toolkit and compared proposed algorithms with other existing algorithms like BCO, PES, CJS, PPO and MCT. The proposed algorithms can balance the Final Cost and Total Cost of machines. Also, the proposed algorithms outperform best existing algorithms in terms of efficiency and imbalance degree.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloud-based healthcare systems are discussed in detail.

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

According to the advances in users’ service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources. Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload. A load balancing algorithm is developed to serve users’ requests to improve the solution of workload problems with an efficient distribution. The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability of CC. Then, the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance. Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and (low resources and large number) of IoT.

A Cloud and IoT-enabled Workload-aware Healthcare Framework using Ant Colony Optimization Algorithm

In recent years, smart cities have gained in popularity due to their potential to improve the quality of life for urban residents. In many smart city services, particularly those in the field of smart healthcare, big healthcare data is analyzed, processed, and shared in real time. Products and services related to healthcare are essential to the industry's current state, which increases its viability for all parties involved. With the increasing popularity of cloud-based services, it is imperative to develop new approaches for discovering and selecting these services. This paper follows a two-stage process. The first step involves designing and implementing an Internet-enabled healthcare system incorporating wearable devices. A new load-balancing algorithm is presented in the second stage, based on Ant Colony Optimization (ACO). ACO distributes tasks across virtual machines to maximize resource utilization and minimize makespan time. In terms of both makespan time and processing time, the proposed method appears to be more efficient than previous approaches based on statistical analysis.

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

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

Improved Whale Social Optimization Algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing

Improved Whale Social Optimization Algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing

Improved whale social optimisation algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing

Improved whale social optimisation algorithm and deep fuzzy clustering for optimal and QoS-aware load balancing in cloud computing

Improved Cat Swarm Optimization Algorithm for Load Balancing in the Cloud Computing Environment

Improved Cat Swarm Optimization Algorithm for Load Balancing in the Cloud Computing Environment

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.