Load Balancing Algorithm Research Articles

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

Dynamically Balancing Load with Overload Control for Microservices

The microservices architecture simplifies application development by breaking monolithic applications into manageable microservices. However, this distributed microservice “service mesh” leads to new challenges due to the more complex application topology. Particularly, each service component scales up and down independently creating load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this article, we propose a self-managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes. BLOC uses overload control approaches to provide feedback to the load balancers. We show that this performs significantly better in solving the incast problem in microservice architectures. A critical component of BLOC is the dynamic capacity estimation algorithm. We show that a well-tuned capacity estimate can outperform even join-the-shortest-queue, a nearly optimal algorithm, while a reasonable dynamic estimate still outperforms Least Connection, a distributed implementation of join-the-shortest-queue. Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 –4 times and the tail, 99th percentile, latency by 2 times.

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).

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Ransomware Mitigation for Secure and Smart Cloud Manufacturing Using GeniLeaf Decision Tree with Load Balancing

Background: Cyber-attacks related to ransomware have increased significantly in cloud manufacturing industries over the last decade, causing considerable disruptions to organizations. This type of information may also include personal details, patent rights, bank account details, etc. This type of malware requires new and better mitigation methods. Objective: The primary objective of this study was to provide an algorithm to execute experiments using genetic algorithms for load balancing with decision trees and mitigate ransomware. Methods: Hybrid analysis and machine learning techniques were used in this study to identify ransomware. Since a wide range of samples impacted by ransomware share most of the characteristics, it may be possible to use this study to detect current and future malware variants in industries. Results: In patented industrial technology, ransomware mitigation plays a crucial role based on the analysis of various papers and patents-to-science references. In this study, a machine learning mitigation algorithm, GeniLeaf Decision Tree (GLDT), was applied to a featured dataset using a genetic algorithm with a decision tree Conclusion: Machine learning and load balancing are used to gain insight into ransomware behavior. By using the proposed approach to mitigate ransomware and spoofing patterns, a high level of accuracy is achieved. Using GeniLeaf Decision Trees to mitigate ransomware is a significant innovation.

Battle Royale deep reinforcement learning algorithm for effective load balancing in cloud computing

Battle Royale deep reinforcement learning algorithm for effective load balancing in cloud computing

Dynamic Load Balancing of Multi-GPU Parallelization for VULCANO VE-U7 Corium Spreading Analysis Using SOPHIA

The SOPHIA code is a graphical processing unit (GPU)–based smoothed particle hydrodynamics (SPH) framework for fundamental nuclear thermal-hydraulic and safety applications developed by Seoul National University. Focusing on particle-based SPH methods, the SOPHIA code provides capabilities for computational fluid dynamics–level analysis in addressing complex multiphase and multiphysics issues related to severe accidents and reactor safety. Since the SPH method requires high computational cost due to the nature of the particle-based method, the SOPHIA code was parallelized with multiple GPUs. However, using multiple GPUs may cause load imbalance as particles migrate between GPUs, which degrades performance. In this study, we developed a dynamic load-balancing algorithm to increase computational efficiency by reducing the load imbalance. Additionally, a novel cumulative time comparison approach is proposed as a criterion for adjusting boundaries between GPUs, ensuring low load imbalance. To evaluate the performance of the algorithm, three-dimensional dam-break simulations were conducted. The results demonstrated enhanced computational efficiency and equitable load distribution among GPUs compared to using fixed subdomains. Additionally, the developed algorithm was utilized to analyze the VULCANO VE-U7 experiment, and the results were well aligned with the experimental data. Therefore, the practical applicability of the proposed dynamic load-balancing algorithm in nuclear safety analyses has been confirmed.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Towards fine-grained load balancing with dynamical flowlet timeout in datacenter networks

In modern datacenter networks (DCNs), load balancing mechanisms are widely deployed to enhance link utilization and alleviate congestion. Recently, a large number of load balancing algorithms have been proposed to spread traffic among the multiple parallel paths. The existing solutions make rerouting decisions for all flows once they experience congestion on a path. They are unable to distinguish between the flows that really need to be rerouted and the flows that potentially have negative effects due to rerouting, resulting in frequently ineffective rerouting. Fine-grained rerouting will also cause severe packet reordering, especially in asymmetric topology scenarios. To address the above issues, we present a fine-grained traffic-differentiated load balancing (TDLB) mechanism, which aims to distinguish flows that are necessarily to be rerouted and reroute traffic in fine-grained without packet reodering. Specifically, TDLB distinguishes the traffic that must be rerouted through the host pair information in the packet header, and selects an optimal path for rerouting. To prevent severe packet reodering caused by excessive path delay differences, TDLB dynamically adjusts the flowlet timeout to segment the traffic and select the optimal path for rerouting. The NS-2 simulation results show that TDLB effectively reduces tail latency and average flow completion time (FCT) for short flows by up to 49% and 46%, respectively, compared to the state-of-the-art load balancing schemes.

Improving fog resource utilization with a dynamic round-robin load balancing approach

In fog computing, load balancing is an important research problem. It focuses on efficiently assigning tasks to fog nodes and minimizing delay in real-time applications. The traditional round-robin algorithm assigns tasks in a rotating manner among fog nodes, but it can send tasks to the cloud too early, leading to increased delays. To solve this problem, this paper introduces an improved round-robin algorithm that takes a dynamic approach to balancing the use of fog resources. The new model aims to improve load balancing in fog computing by distributing tasks more evenly among fog nodes, reducing dependence on cloud computing, and making better use of fog resources. The improved algorithm helps fog computing systems run more efficiently, reduces delays in real-time applications, and lowers the costs associated with cloud use. The results show that the proposed load balancing algorithm is key to optimizing fog resource use, improving system efficiency, and reducing task completion times in distributed computing systems.

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

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

An asymptotically optimal algorithm for generating bin cardinalities

In the balls-into-bins setting, n balls are thrown uniformly at random into n bins. The naïve way to generate the final load vector takes Θ(n) time. However, it is well-known that this load vector has with high probability bin cardinalities of size Θ(lognloglogn). Here, we present an algorithm in the RAM model that generates the bin cardinalities of the final load vector in the optimal Θ(lognloglogn) time in expectation and with high probability.Further, the algorithm that we present is still optimal for any m∈[n,nlogn] balls and can also be used as a building block to efficiently simulate more involved load balancing algorithms. In particular, for the Two-Choice algorithm, which samples two bins in each step and allocates to the least-loaded of the two, we obtain roughly a quadratic speed-up over the naïve simulation.

Dynamic Load Balancing and Service Prioritization Algorithm for Cloud Computing Environments

Bridge infrastructure plays a critical role in ensuring the safe and efficient transportation of goods and people. This research paper investigates the strength characteristics of bridges with a focus on understanding the factors influencing their structural integrity and resilience. The study employs a comprehensive methodology that includes field inspections, laboratory testing, and advanced computational modeling techniques to assess the performance of various bridge types under different loading conditions. Findings reveal the complex interplay between design parameters, material properties, and environmental factors in determining bridge strength and durability. Moreover, the research highlights the importance of proactive maintenance strategies and innovative engineering solutions to enhance the resilience of bridge infrastructure against aging, deterioration, and extreme events. The insights gained from this study have significant implications for bridge design, maintenance practices, and public safety, ultimately contributing to the sustainability and reliability of transportation networks.

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments

Load balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories.

Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing

Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing

Solar powered high gain DC-DC converter with FPGA controller for portable devices

A solar-powered converter design is necessary for portable devices with increased gain and reduced ripple in overall operation. This research presents a photovoltaic-powered Field Programmable Gate Array (FPGA) based on increased gain multiple output converter design and analysis. The dynamic panel input makes it difficult to operate the load. To control and make the input static to satisfy the demand, perturb and observe with a load balancing algorithm technique are used. The converter gained output is high, and batteries are used to balance the loads when solar irradiance is low. Simulation results were carried out with the presented converter circuit that can be operated with a 12 V supply as input from a renewable power source. The output gain ratio of 14 to a minimum of 40 % duty cycle operating at a switching frequency of 20 kHz has been achieved. The proposed methodology is transferred to hardware by connecting with the reconfigurable FPGA Spartan-6 development board to manage the dynamic load control system's complex logic and high processing ability. The overall performance is compared with various pre-existing systems, tabulations are made with the analysis, and experimental results are also illustrated at the end.

SHIELD: A Secure Heuristic Integrated Environment for Load Distribution in Rural-AI

The increasing adoption of edge computing in rural areas is leading to a substantial rise in data generation, necessitating the need for development of advanced load balancing algorithms. This is particularly important in applications that utilise existing, though limited, computational and data communication infrastructures. Furthermore, rural communities have growing concerns regarding the privacy, security, and ownership of the data produced within their agricultural fields. Load distribution in rural edge devices can enhance agricultural practices by improving resource usage, decision-making, and addressing network connectivity challenges. Managing resource utilisation in this way also improves economic investments made in managing and deploying edge devices in rural environments. In this work, we propose SHIELD, a security-aware load balancing framework, primarily designed for edge-based systems in rural areas. For handling environments with limited connectivity, SHIELD efficiently manages tasks and computational resources by categorising them into restricted, public and private, shared respectively. It also allocates tasks considering key performance factors such as completion time, resource utilisation, failure rate, and security. The framework is evaluated on a weed detection scenario in precision agriculture, using three federated learning (FL) variants (local model training, global model aggregation, and model prediction) with the ResNet-50 model trained on the DeepWeeds image classification dataset. The proposed framework also integrates encryption and task replication techniques for data confidentiality, integrity, and availability. Experimental results show that SHIELD demonstrates an average of 23% (using Parsl), 29% (using OpenWhisk) improvement in failure rate and 18 s (Parsl), 13 s (OpenWhisk) average improvement in makespan compared to other task allocation approaches, such as secure variants of random, round robin, and least loaded.

The “load balancing” and “adaptive task completion” algorithms implementation on a pharmaceutical sorting conveyor line

The subject matter of the article is advantages and disadvantages of using the "load balancing" and "adaptive task execution" algorithms on the pharmaceutical sorting conveyor. The goal of the work is to analyze the Load Balancing (LB) and Adaptive Task Completion (ATC) sorting algorithms application on pharmaceutical sorting conveyor lines. And also, to consider their advantages and disadvantages in the context of optimizing sorting processes for pharmaceutical products. The following tasks were solved in the article: analysis of the latest research and publications on the research topic; study of the specifics of the application of algorithms for sorting pharmaceutical products on conveyor lines to balance the load on sorting robots; analysis of the Load Balancing (LB) algorithm from the point of view of its application for load balancing on conveyor lines with sorting robots in the field of pharmaceuticals; development of the Load Balancing algorithm for load balancing on conveyor lines with sorting robots in the pharmaceutical field; analysis of the Adaptive Task Completion (ATC) algorithm from the point of view of its application for load balancing on conveyor lines with sorting robots in the field of pharmaceuticals; development of a general Adaptive Task Completion (ATC) algorithm for load balancing on conveyor lines with sorting robots in the pharmaceutical field; highlighting the advantages and disadvantages of the specified algorithms. The following methods were used: simulation modeling methods. The following results were obtained – the advantages and disadvantages of Load Balancing and Adaptive Task Completion algorithms for load balancing on conveyor lines with sorting robots in the pharmaceutical field are formulated; recommendations are given, in which situations, under which conditions, which algorithm should be used. Conclusions. In the context of the pharmaceutical industry, where not only speed but also sorting accuracy is important, Adaptive Task Completion may be a better choice because it takes into account different criteria and dynamically responds to changing conditions. On the other hand, Load Balancing can be effective in environments where sorting speed is paramount, and tasks are homogeneous.

Research on the Architectural Design and Performance Optimization of Large Scale Real-Time Chat Systems

With the rapid development of internet technology, large-scale real-time chat systems have become an essential tool for connecting users worldwide. These systems not only support the core functions of social communication but also serve as key platforms for business, education, and remote collaboration. However, the surge in user numbers and the explosive growth in message volume pose unprecedented technical challenges to real-time chat systems, including high concurrency processing, low latency communication, data consistency assurance, and system scalability. This study aims to explore and implement an efficient, stable, and scalable architecture for large-scale real-time chat systems and propose corresponding performance optimization strategies. The research employs literature review, system analysis, prototype design, and performance evaluation methods. It first analyzes the design limitations and performance bottlenecks of existing real-time chat systems. Based on this analysis, a microservices-based system design scheme is proposed, which enhances system performance and reliability through componentized services, message queues, load balancing, and caching mechanisms. In terms of performance optimization, this paper focuses on key technologies such as load balancing algorithms, asynchronous processing mechanisms of message queues, caching strategies, and data storage optimization. Experimental evaluations have confirmed the significant effects of the proposed scheme in handling high-concurrency requests, reducing system latency, and improving data throughput.