Load Balancing Model Research Articles

Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm

As the number of sensing devices rises, traffic on the cloud servers is boosting day by day. When a device connected to the IoTwants access to data, cloud computing encourages the pairing of fog & cloud nodes to provide that information. One of the key needs in a fog-based cloud system, is efficient job scheduling to decrease the data delay and improve the QoS (Quality of Service). The researchers have used a variety of strategies to maintain the QoS criteria. However, because of the increased service delay caused by the busty traffic, job scheduling is impacted which leads to the unbalanced load on the fog environment. The proposed work uses a novel model which curates the features and working style of Genetic algorithm and the optimization algorithm with the load balancing scheduling on the fog nodes. The performance of the proposed hybrid model is contrasted with the other well-known algorithms in contrast to the fundamental benchmark optimization test functions. The proposed work displays better results in sustaining the task scheduling process when compared to the existing algorithms, which include Round Robin (RR) method, Hybrid RR, Hybrid Threshold based and Hybrid Predictive Based models, which ensures the efficacy of the proposed load balancing model to improve the quality of service in fog environment.

Load balancing techniques in cloud platform: A systematic study

In the current scenario, researchers have made a new invention and added to the computing paradigm every next second. Cloud computing is one of the most demanding, practical, accessible and extended technologies based on ‘pay as per use model’ and works on virtualisation via internet. Data sharing and accessing have become easy by properly organising various resources, such as storage, servers, development tools, software, etc, in cloud. Handling these resources has faced many challenges, such as cost management, system performance, migration, load imbalance, reliability and privacy etc. Load imbalance is one of the most important factors which are solved by load balancing techniques. This paper introduced the detailed classification of load balancing methods and techniques that are taken as a solution to overcome such problems and also helps future researchers. Also given is a proposed model for load balancing and some comparative studies of the heuristics methods based on platforms and simulator tools.

A Parallel Intelligence-Driven Resource Scheduling Scheme for Digital Twins-Based Intelligent Vehicular Systems

Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.

An Ant-colony Based Model for Load Balancing in Fog Environments

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available resources.We provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field.

A dynamic load balancing model coupled with DAC and ISAT for a stochastic turbulent combustion model

Due to the composition-dependent stiffness of chemistry, simulations of reactive turbulent flows may present computational load imbalance among parallel processes when spatial decomposition is used for parallelisation, causing high CPU idle time and waste of computational resources. To increase computational efficiency, a dynamic load balancing (DLB) model is proposed to redistribute computational load among computing cores. The DLB model exploits a decomposition in the mixture fraction space with two dynamic adjusting decomposition strategies to realise load redistribution. The DLB model is suitable for the integration of chemistry on stochastic particles in hybrid Eulerian/Lagrangian turbulent combustion models in which the Eulerian field is conventionally decomposed statically in physical space in a way that balances the computational load for the solution of the Navier-Stokes equation but which does not generally lead to balanced load for the computation of the composition fields. Here it is tested using an OpenFOAM-based platform, mmcFoam, which is a comprehensive object-orientated C++ library for stochastic turbulent combustion modelling. Apart from direct integration (DI) for chemistry, the DLB model is also coupled with dynamic adaptive chemistry (DAC) and in situ adaptive tabulation (ISAT), which allows for extra speedup. The performance of the coupled models is validated and assessed for two laboratory flame conditions that exhibit different levels of computational load imbalance. Overall, the DLB model effectively balances the computational load distribution and increases the effective usage of computing power, shortening the simulation wall time required. Moreover, a strong scaling test is carried out using up to 512 cores. Although all approaches have sub-ideal scalability, the scalability of each with DLB is significantly better than without DLB. While DLB-ISAT has relatively poor scalability compared to the DI- and DAC-based methods, DLB-ISAT still ranks the fastest among the algorithms in all scaling trials.

Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region.Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals’ data warehouses and regional infection data as recorded in the Netherlands.The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

A mixed-integer convex approximation for optimal load redistribution in bipolar DC networks with multiple constant power terminals

This paper proposes a mixed-integer convex model for optimal load-balancing in bipolar DC networks while considering multiple constant power terminals. The proposed convex model combines the Branch and Cut method with interior point optimization to solve the problem of optimal load balancing in bipolar DC networks. Additionally, the proposed convex model guarantees that global optimum of the problem is found, which ensures minimal power losses in the bipolar DC distribution grid branches, as the total monopolar load consumption has been balanced at the substation's terminals. In addition, an optimal load balancing improves the voltage profiles due to current redistribution between the positive and negative poles. Numerical results in the 21- and 85-bus test feeders and a comparison with three metaheuristic techniques show the effectiveness of the proposed convex model in reducing the total grid imbalance while minimizing the power losses and improving the voltage profiles.

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

Cluster system load balancing model with consideration of hardware characteristics of server hardware

Objectives. To upgrade and complement the existing load balancing model in multi-server systems, taking into account the hardware characteristics of the server equipment, as well as the most loaded components of the server equipment in the video conferencing service cluster in educational processes and distance education.Methods. The existing mathematical model of load balancing as a mass exchange system is considered, when significant changes are introduced: penalties for equipment downtime and penalties for waiting in a queue will depend on the load on the server hardware components in the cluster architecture of video conferencing service.Results. Formulas are given for calculating the total performance of a cluster of n servers with the maximum and minimum load of server hardware components in a videoconferencing system cluster.Conclusion. A modeling complex has been developed to test the mathematical model on a system of up to n < 10 servers in a cluster of a videoconferencing system. Based on the results of calculations of the modeling complex, it was concluded that it is necessary to upgrate the existing algorithm for balancing the load on the selected BigBlueButton video conferencing service.

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

SummaryLoad balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.

Mean Field and Refined Mean Field Approximations for Heterogeneous Systems

Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as n interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of n homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds or contents with different popularities. We define an interaction model that allows obtaining asymptotic convergence results for stochastic systems with heterogeneous object behavior and show that the error of the mean field approximation is of order O(1/n). More importantly, we show how to adapt the refined mean field approximation, developed by the authors of Gast et al. 2019, and show that the error of this approximation is reduced to O(1/n2). To illustrate the applicability of our result, we present two examples. The first addresses a list-based cache replacement model, RANDOM(m), which is an extension of the RANDOM policy. The second is a heterogeneous supermarket model. These examples show that the proposed approximations are computationally tractable and very accurate. For moderate system sizes (n ≈ 30) the refined mean field approximation tends to be more accurate than simulations for any reasonable simulation time.

Rmf tool - A library to Compute (Refined) Mean Field Approximation(s)

Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as systems of interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers, for which mean field approximation gives very accurate estimates of the transient or steady-state behaviors. In a series of recent papers [9, 7], a new and more accurate approximation, called the refined mean field approximation is presented. Yet, computing this new approximation can be cumbersome. The purpose of this paper is to present a tool, called rmf tool, that takes the description of a mean field model, and can numerically compute its mean field approximations and refinement.

Distribution-free chance-constrained load balance model for the operation planning of hydrothermal power systems coupled with multiple renewable energy sources

We are interested in the potential risk of short-term load imbalance in hydro-thermal power systems under high integration of renewable energy sources, including wind, solar, and biomass. Toward this end, the short-term load balance across the system is formulated as a joint chance constraint. Due to non-convexity issues and the lack of information about the joint probability distribution of the renewable energy resources, we analyze a distribution-free robust relaxation and a p-efficiency approximation of the joint chance constraint. Our proposal is embedded within the widespread SDDP algorithm, and compared with the popular CVaR scheme on a simplified version of the Colombian power system.

Load Balancing and Parallel Computation Model for Performance and Accuracy over the Cluster of Nodes

Cloud computing can be online based network engineering which contributed with a rapid advancement at the progress of communication technological innovation by supplying assistance to clients of assorted conditions with aid from online computing sources. It's terms of hardware and software apps together side software growth testing and platforms applications because tools. Large-scale heterogeneous distributed computing surroundings give the assurance of usage of a huge quantity of computing tools in a comparatively low price. As a way to lessen the software development and setup onto such complicated surroundings, high speed parallel programming languages exist which have to be encouraged by complex operating techniques. There are numerous advantages for consumers in terms of cost and flexibility that come with Cloud computing anticipated uptake. Building on well-established research in Internet solutions, networks and utility computing, virtualization et cetera Service-Oriented Architectures and the Internet of Services (IoS) have implications for a wide range of technological issues such as parallel computing and load balancing as well as high availability and scalability. Effective load balancing methods are essential to solving these issues. Adaptive task load model is the name of the method we suggest in our article for balancing the workload (ATLM). We developed an adaptive parallel distributed computing paradigm as a result of this (ADPM). While still maintaining the model's integrity, ADPM employs a more flexible synchronization approach to cut down on the amount of time synchronous operations use. As well as the ATLM load balancing technique, which solves the straggler issue caused by the performance disparity between nodes, ADPM also applies it to ensure model correctness. The results indicate that combining ADPM and ATLM improves training efficiency without compromising model correctness.

The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm

In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect.

Stability and Optimization of Speculative Queueing Networks

We provide a queueing-theoretic framework for job replication schemes based on the principle “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">replicate a job as soon as the system detects it as a straggler</i> ”. This is called job <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speculation</i> . Recent works have analyzed replication on arrival, which we refer to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">replication</i> . Replication is motivated by its implementation in Google’s BigTable. However, systems such as Apache Spark and Hadoop MapReduce implement speculative job execution. The performance and optimization of speculative job execution is not well understood. To this end, we propose a queueing network model for load balancing where each server can speculate on the execution time of a job. Specifically, each job is initially assigned to a single server by a frontend dispatcher. Then, when its execution begins, the server sets a timeout. If the job completes before the timeout, it leaves the network, otherwise the job is terminated and relaunched or resumed at another server where it will complete. We provide a necessary and sufficient condition for the stability of speculative queueing networks with heterogeneous servers, general job sizes and scheduling disciplines. We find that speculation can increase the stability region of the network when compared with standard load balancing models and replication schemes. We provide general conditions under which timeouts increase the size of the stability region and derive a formula for the optimal speculation time, i.e., the timeout that minimizes the load induced through speculation. We compare speculation with redundant- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and redundant-to-idle-queue- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> rules under an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S\&amp; X$ </tex-math></inline-formula> model. For light loaded systems, redundancy schemes provide better response times. However, for moderate to heavy loadings, redundancy schemes can lose capacity and have markedly worse response times when compared with the proposed speculative scheme.

Load Balancing Based on Many-objective Particle Swarm Optimization Algorithm with Support Vector Regression in Fog Computing

With the development in computing technologies, fog computing is developing as public and robust computing, which complements cloud computing to provide services, computation, and storage on the edge network. The future growth and support of 5G access networks additional advance the viability and implementation of fog networks and widen the scope of devices that can participate and serve in IoT communication. However, scaling fog computing, the number of end-users increases. Hence, the workload between fog nodes needs to be distributed efficiently. Otherwise, some of the nodes will be overloaded, and others will be under-loaded. Consequently, one of the critical factors for managing resources in fog computing efficiently and avoiding overloaded or under-loaded is load balancing. Therefore, load balancing between these resources is a challenge in fog computing. There are different techniques to balance the load, such as optimization algorithms or machine learning. This paper proposes a load balancing model in fog computing based on a many-objective particle swarm optimization (PSO) algorithm with support vector regression (SVR). The proposed load balancing model considered four metrics to optimize them while distributing the load: response time, energy consumption, resource utilization, and throughput. Besides, It combines SVR with PSO to improve PSO performance. The proposed model has been simulated and tested to evaluate the performance from different aspects. The experiments show that the proposed model efficiently balances the load with optimizing the four metrics. In addition, it improves the performance of PSO, which is used to balance the load.

Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables

This work aims to identify techniques leading to a highly available request processing service by using the natural decentralization and the dispersion power of the hash function involved in a Distributed Hash Table (DHT). High availability is present mainly in systems that: scale well, are balanced and are fault tolerant. These are essential features of the Distributed Hash Tables (DHTs), which have been used mainly for storage purposes. The novelty of this paper’s approach is essentially based on hash functions and decentralized Distributed Hash Tables (DHTs), which lead to highly available data solutions, which a main building block to obtain an improved platform that offers high availability for processing clients’ requests. It is achieved by using a database constructed also on a DHT, which gives high availability to its data. Further, the model requires no changes in the interface, that the request processing service already has towards its clients. Subsequently, the DHT layer is added, for the service to run on top of it, and also a load balancing front end, in order to make it highly available, towards its clients. The paper shows, via experimental validation, the good qualities of the new request processing service, by arguing its improved scalability, load balancing and fault tolerance model.

Discrete-event simulation of load distribution between server stations

The simulation model of the computational load balancing in a server complex using a balancing server is proposed. The server load balancing model was developed using MATLAB/Simulink/SimEvents/Stateflow tools. The SimEvents based model makes it possible to simulate the server complex as a queuing system and to take into account the sporadic occurrence of requests. The Stateflow based event-driven model simulates the system with a variable time delay in data transmission channels and simulates state transitions for servers. This discrete-event simulation model allows evaluating the possibility of rational use of computational resources and reducing the time of service. In addition, the model allows to investigate the effectiveness of load balancing systems at the stage of their development.