Load Balancing Techniques Research Articles

Improved round-robin rule learning for optimal load balancing in distributed cloud systems

A newly emerging technology that distributes virtualised computer resources across the internet is referred to as 'cloud computing' and is gaining popularity. These clouds' ability to evenly distribute the load is vital. Load balancing in cloud computing distributes dynamic workloads so no database server is overcrowded or underloaded (LB). As a result, a dynamic load balancing technique in the cloud may contribute to improved service stability and resource utilisation. In this study, we model a load balancing system that balances the cloud's available resources using a rule-based round-robin method. Cloud parameters are used to calculate and allocate resources using a rule-based round-robin technique. The study analyses cloud metrics to determine cloud loads and resources. The cloud sim tool is used to allocate resources, and its effectiveness is tested against a variety of performance measures such as overhead, migration time, and throughput rate. Simulation results show that rule-based round-robin improves cloud performance. When compared to existing algorithms, the proposed methodology improves task performance by about 4%.

A Smart Architecture Leveraging Fog Computing Fusion and Ensemble Learning for Prediction of Gestational Diabetes

Gestational diabetes (GD) is a growing global concern, underscoring the need for early detection and effective management to prevent adverse health consequences. This paper presents an innovative and reliable architecture to predict gestational diabetes in pregnant women. While reducing the frequency of doctor visits by sending the necessary data via Internet of Things (IoT) technology and receiving the prediction results via a mobile application in real time. The proposed architecture is a fusion of fog computing hardware with ensemble machine learning to enable low-latency, energy-efficient solutions for data processing, and cloud computing. The GD_Fog architecture leverages fused fog computing and load balancing techniques to reduce latency, power consumption, Network bandwidth consumption, and response time, and cloud computing is used based on the concept of use on demand for more reliability while harnessing the power of group learning to improve prediction accuracy. In addition, GD_Fog can be configured for different operating modes to ensure optimal quality of service and prediction accuracy in various fog calculation scenarios, which meet different user requirements. Through extensive testing using real-world data from pregnant women, the framework shows promising results, outperforming the latest methods in accuracy and efficiency. Where the percentage of improvement in prediction accuracy was approximately 6.5% when using ensemble learning, and the improvement in energy use, amounted to approximately 87.01% when using fused fog computing instead of cloud computing. These results confirm the potential of the proposed structure as an invaluable tool for the early detection and effective management of gestational diabetes.

Dynamic Load Balancing Techniques in the IoT: A Review

The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.

Permutation based load balancing technique for long term evolution advanced heterogeneous networks

<span lang="EN-US">Traffic congestion has been one of the major performance limiting factors of heterogeneous networks (HetNets). There have been several load balancing schemes put up to solve this by balancing load among base stations (BSs), but they appear to be unfeasible due to the complexity required and other unsatisfactory performance aspects. Cell range extension (CRE) has been a promising technique to overcome this challenge. In this paper, a permutation based CRE technique is proposed to find the best possible formation of bias for BSs to achieve load balance among BSs. In comparison to the baseline scheme, results depict that the suggested method attains superior performance in terms of network load balancing and average throughput. The complexity of the suggested algorithm is considerably reduced in comparison to the proposed permutation based CRE method it is further modified from.</span>

A Review on Load Balancing Algorithms in Cloud Computing

Cloud computing is a new standard for large-scale distributed and parallel computing that is still in its infancy. At a predetermined time, provides common resources, information, software packages, and other resources in accordance with client requirements. As cloud computing becomes more popular and more people become interested in assistive computing, better and quicker service is necessary. It is necessary to enhance the management of the good load balancing solutions that are already accessible. Recently, numerous clients from all over the world have been seeking various services at a quick rate, negatively impacting cloud computing performance. The various load balancing techniques used in cloud computing are effective in customizing demand by identifying the appropriate virtual machines. In this review, a comparative study will be done between many load balancing algorithms in different cloud computing, as many studies have been done on this important topic. Important research will also be analyzed and defined, and analyzes will be written for each research that talks about dynamic and non-dynamic load balancing techniques and algorithms. Many details will also be mentioned from the researcher's point of view as a summary of each research.

GWO-Based Simulated Annealing Approach for Load Balancing in Cloud for Hosting Container as a Service

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan.

Congestion Avoidance using Traffic Load Balancing (CATLB) in Computer Networks

Congestion in computer networks is one of the most crucial subjects in the world of networks since it has a significant influence on the network and the quality of service and decreased efficiency of it, which frequently results in service interruptions, In order to preserve the continuity of data flow in the network, it is necessary to design approaches and processes to avoid congestion or minimize its effects. Congestion is avoided at two levels, knot and link, by using different techniques, including close loop or open loop. This paper proposed a new method (CATLB) to avoid congestion by using the traffic load balancing technique, CATLB is based on the popular algorithm Round Robin in the process of distributing packets coming to a particular node on the links connected to it, which leads to the same destination to avoid congestion on one of the links and out of service and thus leads to more load on the other links until the network collapses. Method has been simulated using OMNET++ and got around 15% improvement in the performance from the most popular similar algorithms in this field ECMP and TinyFlow.

Multimedia Information Time Balance Management in Mobile Cloud Environment Supported By Case Study

Mobile cloud computing is used to define and determine computing services with a structure model. The data and resource of any service will be retrieved from cloud computing through internet service, some tools, and user interface (web-based or application). Mobile Cloud Computing (MCC) is a hybrid of cloud computing and mobile computing. Multimedia Information is the core of Mobile Cloud information because of the sizable information of multimedia particularly video streaming. Mobile Cloud mostly handles and processes that information. MCC is one of the business expressions with the real environment in the IT world. The concept of the MCC is still in the beginner stage of advancement. So, the handle of the innovation in a careful way especially in the bearing of future research should be provide. In this paper, an algorithm is throttled load balancing for mobile clouds has been presented within an example of Multimedia information. The results has shown that the load balancing of cloud computing environment. In this scenario, load balancing techniques in mobile cloud computing can be employed and can successfully manage time through the cloud.

Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing

The number of client applications on the fog computing layer is increasing due to advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant role in reducing latency and enhancing resource usage for IoT users’ tasks. Along with its various benefits, fog computing also faces several challenges, including challenges related to resource overloading, security, node placement, scheduling, and energy consumption. In fog computing, load balancing is a difficult challenge due to the increased number of IoT devices and requests, which requires an equal load distribution throughout all available resources. In this study, we proposed a secure and energy-aware fog computing architecture, and we implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment. A deep belief network (DBN)-based intrusion detection method was also implemented as part of the proposed techniques to reduce workload communication delays in the fog layer. The simulation findings showed that the proposed technique provided an efficient method of load balancing in a fog environment, minimizing the average response time, average energy consumption, and communication delay by 15%, 23%, and 10%, respectively, as compared with other existing techniques.

Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks

Designing an intelligent self-organizing network (SON) architecture is challenging for future wireless networks. To meet the needs of SON, the reactive self-organizing model of the traditional network needs to be transformed into an active and interactive one. Due to the user mobility and small coverage of cells in ultra-dense networks (UDNs), the network load usually becomes unbalanced, leading to deteriorated network performance, such as low throughput, radio link failure, and poor user experience. Therefore, the technique of mobility load balancing (MLB) is critical to ensuring a seamless user experience among cells. This letter proposes an active and interactive MLB strategy for UDNs, which transforms the original reactive MLB into a forward-aware and active one. In particular, user mobility is first predicted based on the Bayesian additive regression tree (BART). Then, with the mobility predictions, the joint mobility robust optimization and MLB problem subject to users’ rate constraint is solved via safe reinforcement learning. The pertaining simulation results show that the proposed method can improve the network performance and realize intelligent mobile management for future UDNs.

A Survey on Load Balancing, Routing, and Congestion in SDN

High traffic could result in load imbalance or network congestion, which degrades the network’s performance and efficiency. Thus, it is crucial to adopt efficient routing and load balancing models to face these challenging issues. Additionally, when investigating a new approach, it is essential to consider the most important metrics to evaluate this potential approach precisely. This paper presents an intensive analysis of recently available SDN-based load balancing and routing techniques. Furthermore, the features and issues of each technique are stated. Moreover, the most important metrics that should be evaluated are statically analyzed. Also, a brief survey of available network congestion solutions is shown. Additionally, taxonomies of available load balancing, routing techniques, and congestion solutions are presented. Finally, we shed light on the trends, promising techniques, and future directions’ suggestions that could be utilized further in research. Investigating SDN-based research published by well-known academic publishers in the last six years shows that enhancing network performance and AI-based approaches are the highest investigated topics with 28% and 27%, respectively, of the total investigated issues. Other topics took lower percentages. As far as we know, this study is the first work that jointly surveys and categorizes all existing approaches in the field of decreasing delay and congestion in SDN-based networks

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

AbstractCloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.

A Cloud-Based Container Microservices: A Review on Load-Balancing and Auto-Scaling Issues

Microservices are being used by businesses to split monolithic software into a set of small services whose instances run independently in containers. Load balancing and auto-scaling are important cloud features for cloud-based container microservices because they control the number of resources available. The current issues concerning load balancing and auto-scaling techniques in Cloud-based container microservices were investigated in this paper. Server overloaded, service failure and traffic spikes were the key challenges faced during the microservices communication phase, making it difficult to provide better Quality of Service (QoS) to users. The aim is to critically investigate the addressed issues related to Load balancing and Auto-scaling in Cloud-based Container Microservices (CBCM) in order to enhance performance for better QoS to the users.

BGP-Multipath Routing in the Internet

BGP-Multipath (BGP-M) is a multipath routing technique for load balancing. Distinct from other techniques deployed at a router inside an Autonomous System (AS), BGP-M is deployed at a border router that has installed multiple inter-domain border links to a neighbor AS. It uses the equal-cost multi-path (ECMP) function of a border router to share traffic to a destination prefix on different border links. Despite recent research interests in multipath routing, there is little study on BGP-M. Here we provide the first measurement and a comprehensive analysis of BGP-M routing in the Internet. We extracted information on BGP-M from query data collected from Looking Glass (LG) servers. We revealed that BGP-M has already been extensively deployed and used in the Internet. A particular example is Hurricane Electric (AS6939), a Tier-1 network operator, which has implemented >1,000 cases of BGP-M at 69 of its border routers to prefixes in 611 of its neighbor ASes, including many hyper-giant ASes and large content providers, on both IPv4 and IPv6 Internet. We examined the distribution and operation of BGP-M. We also ran traceroute using RIPE Atlas to infer the routing paths, the schemes of traffic allocation, and the delay on border links. This study provided the state-of-the-art knowledge on BGP-M with novel insights into the unique features and the distinct advantages of BGP-M as an effective and readily available technique for load balancing.

In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets

<span lang="EN-US">Load <span lang="EN-US">balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.</span></span>

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

SUMMARYLoad balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

Optimal load balancing and assessment of existing load balancing criteria

Parallel iterative applications often suffer from load imbalance, one of the most critical performance degradation factors. Hence, load balancing techniques are used to distribute the workload evenly to maximize performance. A key challenge is to know when to use load balancing techniques. In general, this is done through load balancing criteria, which trigger load balancing based on runtime application data and/or user-defined information. In the first part of this paper, we introduce a novel, automatic load balancing criterion derived from a simple mathematical model. In the second part, we propose a branch-and-bound algorithm to find the load balancing iterations that lead to the optimal application performance. This algorithm finds the optimal load balancing scenario in polynomial time while, to the best of our knowledge, it has never been addressed in less than an exponential time. Finally, we compare the performance of the scenarios produced by state-of-the-art load balancing criteria relative to the optimal load balancing scenario in synthetic benchmarks and parallel N-body simulations. In the synthetic benchmarks, we observe that the proposed criterion outperforms the other automatic criteria. In the numerical experiments, we show that our new criterion is, on average, 4.9% faster than state-of-the-art load balancing criteria and can outperform them by up to 17.6%. Moreover, we see in the numerical study that the state-of-the-art automatic criteria are at worst 26.43% slower than the optimum and at best 10% slower.

Load balancing technique based on hybrid resource utilization in cloud computing

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.

Machine Learning Load Balancing Techniques in Cloud Computing: A Review

Load balancing (LB) is the process of distributing the workload fairly across the servers within the cloud environment or within distributed computing resources. Workload includes processor load, network traffic and storage burden. LB’s main goal is to spread the computational burden across the cloud servers to ensure optimal utilization of the server resources. Cloud computing (CC) is a rapidly growing field of computing that provides computing resources as a product over the internet. This paper focuses on the issues within Cloud Load Balancing (LB) that have attracted research interest. The paper also mainly focused on uncovering machine learning models used in LB techniques. The most common algorithms in the reviewed papers included Linear Regression, Random Forest classifier (RF) Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long-Short Term Memory- Recurrent Neural Network (LSTM -RNN). The criteria for LB technique was identified through performance metrics like throughput, response time, migration time, fault tolerance and power saving. The paper adjourns by identifying research gaps found in the reviewed literature.

Accelerating Reads With In-Network Consistency-Aware Load Balancing

We present FLAIR, a novel approach for accelerating read operations in leader-based consensus protocols. FLAIR leverages the capabilities of the new generation of programmable switches to serve reads from follower replicas without compromising consistency. The core of the new approach is a packet-processing pipeline that can track client requests and system replies, identify consistent replicas, and at line speed, forward read requests to replicas that can serve the read without sacrificing linearizability. An additional benefit of FLAIR is that it facilitates devising novel consistency-aware load balancing techniques. Following the new approach, we designed FlairKV, a key-value store atop Raft. FlairKV implements the processing pipeline using the P4 programming language. We evaluate the benefits of the proposed approach and compare it to previous approaches using a cluster with a Barefoot Tofino switch. Our evaluation indicates that, compared to state-of-the-art alternatives, the proposed approach can bring significant performance gains: up to 42% higher throughput and 35–97% lower latency for most workloads. Furthermore, our evaluation shows that our novel load balancing techniques can cope with heterogeneous load and hardware to achieve higher performance, and that FLAIR can scale to support large data sets and clusters.