Load Balancing Techniques Research Articles

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

Ruzicka Indexive Throttled Deep Neural Learning for Resource-Efficient Load Balancing in a Cloud Environment

Cloud Computing (CC) is a prominent technology that permits users as well as organizations to access services based on their requirements. This computing method presents storage, deployment platforms, as well as suitable access to web services over the internet. Load balancing is a crucial factor for optimizing computing and storage. It aims to dispense workload across every virtual machine in a reasonable manner. Several load balancing techniques have been conventionally developed and are available in the literature. However, achieving efficient load balancing with minimal makespan and improved throughput remains a challenging issue. To enhance load balancing efficiency, a novel technique called Ruzicka Indexive Throttle Load Balanced Deep Neural Learning (RITLBDNL) is designed. The primary objective of RITLBDNL is to enhance throughput and minimize the makespan in the cloud. In the RITLBDNL technique, a deep neural learning model contains one input layer, two hidden layers, as well as one output layer to enhance load balancing performance. In the input layer, the number of cloud user tasks is collected and sent to hidden layer 1. In that layer, the load balancer in the cloud server analyzes the virtual machine resource status depending on energy, bandwidth, memory, and CPU using the Ruzicka Similarity Index. Then, it is classified VMs as overloaded, less loaded, or balanced. The analysis results are then transmitted to hidden layer 2, where Throttled Load Balancing is performed to dispense the workload of weighty loaded virtual machines to minimum loaded ones. The cloud server efficiently balances the workload between the virtual machines in higher throughput and lower response time and makespan for handling a huge number of incoming tasks. To evaluate experiments, the proposed technique is compared with other existing load balancing methods. The result shows that the proposed RITLBDNL provides better performance of higher load balancing efficiency of 7%, throughput of 46% lesser makespan of 41%, and response time of 28% than compared to conventional methods.

Scalable Parking Management Using Cloud Infrastructure and DevOps

This project presents a scalable parking management system built using PHP and SQL, integrated with cloud infrastructure and DevOps methodologies. The system dynamically scales to handle high traffic using cloud auto-scaling and load balancing techniques, ensuring reliable performance. By leveraging containerization, continuous integration, and monitoring tools, the project automates deployments and ensures minimal downtime. The goal is to provide an efficient and resilient solution to parking management challenges, enabling seamless user experience during peak times while maintaining system integrity and availability.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

CBWO: A novel multi-objective load balancing technique for cloud computing

In cloud computing systems, the growing demand for diverse applications has led to challenges in resource allocation and workload distribution, resulting in increased energy consumption and computational costs. To address these challenges, we propose a novel load-balancing method, namely CBWO, that integrates Chaos theory with the Black Widow Optimization algorithm. Our approach is designed to optimize cloud computing environments by improving energy efficiency and resource utilization. We employ CloudSim for simulations, evaluating key performance metrics such as energy consumption, resource utilization, makespan, task completion time, and imbalance degree. The experimental results demonstrate the superiority of our method, achieving average improvements of 67.28% in makespan and 29.03% in energy consumption compared to existing solutions.

A systematic literature review for load balancing and task scheduling techniques in cloud computing

Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques.

K-medoid clustering containerized allocation algorithm for cloud computing environment

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%.

Leveraging AI and Machine Learning for Performance Optimization in Web Applications

The rapid evolution of web technologies has placed an unprecedented demand on web applications to deliver high performance, scalability, and responsiveness. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools that can significantly optimize the performance of web applications. This paper explores the integration of AI and ML techniques in enhancing various aspects of web application performance, including load balancing, caching, resource management, and user experience personalization. The deployment of AI-driven algorithms enables real-time monitoring and predictive analytics, allowing for proactive adjustments to prevent bottlenecks and ensure smooth operation under varying loads. One of the primary areas where AI and ML contribute is in load balancing. Traditional load balancing techniques often rely on static or rule-based methods, which may not adapt well to dynamic web environments. AI-powered load balancers, however, can learn from traffic patterns and predict surges in demand, enabling more efficient distribution of traffic across servers. This adaptive approach not only improves response times but also reduces the risk of server overloads, enhancing the overall reliability of web applications.

Stable creation of a single plasma channel and collimated fast electrons using large-scale PIC simulations with new dynamic load-balancing technique

We developed a PIC code using new load balancing technique, in which the lower load processes help the higher load processes. A test calculation indicates more than 10 times faster than that without load balancing. Large scale 3-d calculations indicate the formation of central current whose density is close to critical density, supported by the magnetic field inside the channel.

Electric Vehicle Battery Charger with IOT System

The accelerating adoption of electric vehicles (EVs) has elicited the requirement for modern and developed charging solutions that promise efficiency, as well reliability. In this paper, we propose an Internet of Things (IoT) enabled DC electric vehicle charger to meet these requirements. The solution offers IoT functionalities for full and remote monitoring, diagnostics roadside assistance as well as charging management adapting to the available power. Making use of top-level sensors and connectivity, the charger system optimizes energy usage by intelligent grid balancing as well as enables dynamic load balancing techniques for installation-wise benefits such that it is possible to implement predictive maintenance. Meanwhile, IoT integration ensures a seamless user experience through mobile apps so that users can check the charging status of their vehicle and set up sessions at different times of the day while receiving important notifications. DC EV chargers are faster at charging than normal Alternating Current (AC) ones. They transform electricity from the grid into a form that can be directly applied in the vehicle battery. This ability translates to highly reduce charging times, which explains why they are well-suited for along highways and in commercial applications or urban settings where fast turnaround is critical. These trends are identified by service providers to improve charger placement and availability for drivers

Leveraging AI and Machine Learning for Performance Optimization in Web Applications

The rapid evolution of web technologies has placed an unprecedented demand on web applications to deliver high performance, scalability, and responsiveness. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools that can significantly optimize the performance of web applications. This paper explores the integration of AI and ML techniques in enhancing various aspects of web application performance, including load balancing, caching, resource management, and user experience personalization. The deployment of AI-driven algorithms enables real-time monitoring and predictive analytics, allowing for proactive adjustments to prevent bottlenecks and ensure smooth operation under varying loads.One of the primary areas where AI and ML contribute is in load balancing. Traditional load balancing techniques often rely on static or rule-based methods, which may not adapt well to dynamic web environments. AI-powered load balancers, however, can learn from traffic patterns and predict surges in demand, enabling more efficient distribution of traffic across servers. This adaptive approach not only improves response times but also reduces the risk of server overloads, enhancing the overall reliability of web applications.

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

Optimizing Cloud Application Performance: A Survey on Load Balancing Techniques

In the contemporary realm of operation in the World Wide Web, the cloud applications have to meet the high availability, operations on dynamic loads and must function at a very high quality. Load balancing is a proper technique to achieve above goals to distribute the loads across the server or to the various resources. This distribution optimizes performance, scalability, reliability and resources to be used, hence making the network to be appealing to bigger organizations to adopt it. The advantages because of load balancing are reduced response time, increased throughput and fault tolerance because the network traffic load is spread evenly among different servers thereby avoiding the occurrence of a bottleneck server. Some of these ways include Round Robin, Least Connection, and Weighted Distribution, help in the process by making it possible for systems to horizontally scale up for more traffic. As cloud services becomes popular, it becomes very important to manage the load since some applications or services may be more resource intensive than others. It distributes the incoming requests following the current loads on various servers and consequently, no server is too busy or idle. Also, load balancers provide for fault tolerance in the sense that traffic will be handled by other healthy servers in the occurrence of failures hence the solutions are highly available and reliable. Load balancing algorithms such from the basic rule-dependent to complex reinforcement learning algorithms are very crucial with respect to allocation of resources, energy management, and stability of the system. These algorithms will be increasingly important as cloud environments develop further in the efficiency of delivering services and the managing of resources needed, benefiting both the economy and the environment. INDEX TERMS: Performance, Scalability, Response Times, Latency, Horizontal Scaling, Load Balancing

Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing

Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing

Hybrid Agent-Based Load-Balancing Approach Used in an IaaS Platform

Cloud computing has received a resounding welcome. It was created following methodical and thorough study in web services, distributed computing, utility computing, and virtualization, and it offers several benefits, including lower costs, less space required, and easier management. These advantages bring in a significant number of new users to the cloud platform every day. In addition, because cloud computing is an Internet-based computing paradigm, it must deal with the issue of overwhelming demands through effective load-balancing. A very small number of studies only focus on load-balancing problems in cloud computing platforms, while the majority of load-balancing research is accessible in many domains, including parallel, distributed, and grid computing. Infrastructure as a Service (IaaS), Software as a Service, and Platform as a Service are the three basic categories under which cloud computing falls. For these models, there are notable differences in the load-balancing techniques used. This work compared the outcome with the current method and presented a hybrid agent-based load-balancing approach for the IaaS platform.

Tarazu: An Adaptive End-to-end I/O Load-balancing Framework for Large-scale Parallel File Systems

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu , an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.

Efficient criticality oriented service brokering policy in cloud datacenters

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

An advanced ensemble load balancing approach for fog computing applications

Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.

Load balancing techniques in cloud computing: A review

Cloud computing provides an easy and flexible accessibility of resources on the Internet. In this case the clients can use the available resources as they need without upgrading their own hardware. Thus, load balancing is considered as one of the most challenging issues related to the cloud computing where multiple tasks (processes) must be run simultaneously on the processing elements. There are different algorithms used for the task’s allocation on those elements. The tasks can be distributed according to different schemes. Some algorithms suggest prioritizing the tasks while some others distribute the balance according to the length of the task. However, there is a large number of the load balancing methods that depends on the Artificial Intelligence techniques. Specifically, the utilization of the meta-heuristic algorithms for the task’s distribution on the virtual machines. The aim of these algorithms is to enhance the cloud system productivity by looking for the optimal distribution of those tasks on the virtual machines. There is a large number of methodologies that is worthy to review and investigate in terms of their efficiency, performance and productivity. The main aim of this work is to make a comprehensive literature review paper that discuss the advancement of this area through the years. The advantages and disadvantages of those methods are investigated in order to highlight the gaps and try to suggest some solutions in future. In addition, the classification is conducted on basis of the parameter coefficients. Besides, a full analysis has been made for the compared methods. The futuristic aspects of the utilization of the currently used load balancing algorithms has also been highlighted.