Load Balancing Algorithm Research Articles

A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications

Critical healthcare application tasks require a real-time response because it affects patients’ life. Fog computing is the best solution to get a fast response and less energy consumption in healthcare. However, current solutions face difficulties in scheduling the tasks to the correct computing devices based on their priorities and capacity to meet the tasks’ deadlines and resource limitations with minimal latency. Furthermore, challenges of load balancing and prioritization are raised when dealing with inadequate computing resources and telecommunication networks while obtaining the best scheduling of emergency healthcare tasks. In this study, a fog computing resource management (FRM) model is proposed, which the proposed model has three main solutions. Firstly, resource availability is calculated according to the average execution time of each task. Secondly, load balancing is enhanced by proposing a hybrid approach that combines the multi-agent load balancing algorithm and the throttled load balancing algorithm. Thirdly, task scheduling is done based on priority, resource availability, and load balancing. The results have been acquired using the iFogSim toolkit. Two datasets are used in this study, the blood pressure dataset was acquired from the UTeM clinic, and the ECG dataset was acquired from the University of California at Irvine. Both datasets are integrated to enlarge the attributes and get accurate results. The results demonstrate the effectiveness of managing resources and optimizing task scheduling and balancing in a fog computing environment. In comparison with other research studies, the FRM model outperforms delay by 55%, response time by 72%, cost by 72%, and energy consumption by 70%.

Dynamic Weighted Round Robin Approach in Software-Defined Networks Using Pox Controller

Load balancing is important in solving over-load traffic problems in the network. Therefore, it has been among the first appealing applications in Software Defined Networking (SDN) networks. Numerous SDN-based load-balancing approaches have been recommended to enhance the performance of SDN networks. However, network control could be more manageable in large networks with hundreds of switches and routers. The SDN is a unique way of building, controlling, and developing networks to modify this unpleasant situation. The major concept of SDN contains logically centralizing network management in an SDN controller, which manages and observes the behaviour of the network. Numerous load-balancing approaches are known, such as Round Robin (RR), random policy, Weighted randomized policy (WRP), etc. Every load-balancing policy approach has some benefits and detriments. This paper developed an advanced load-balancing algorithm, a dynamic weighted round-robin (DWRR), and ran it on the top of the SDN controller. Then we calculate the result of our proposed load-balancing approach by comparing it with the current round-robin (RR) and weighted round-robin (WRR) approaches. Mininet tool is utilized for the investigation, and the controller utilized as the control plane is named the POX controller.

Load Balancing Algorithms with the application of Machine Learning: A Review

Cloud computing is the provision of computing services over the web. Cloud Computing's load-balancing algorithms are implemented in static, dynamic, and centralized environments. The article analyzes and outlines various load-balancing techniques in the cloud computing architecture and discusses the pros and cons of different load-balancing algorithms. The research also primarily fixates on exploring machine learning models implemented in LB techniques. The most popular algorithms in the articles reviewed include Statistical Regression, Random Forest Classifier Artificial Neural Networks(RF), Convolutional Neural Networks(CNNs), and Regenerating Memory Neural Networks. long-term (LSTM-RNN). LB specifications have been defined through performance metrics such as throughput, latency, travel time, fault tolerance, and power savings.

An Intelligent Server load balancing based on Multi-criteria decision-making in SDN

In an environment of rising internet usage, it is difficult to manage network traffic while maintaining a high quality of service. In highly trafficked networks, load balancers are crucial for ensuring the quality of service. Although different approaches to load-balancing have been proposed in traditional networks, some of them require manual reconfiguration of the device to accommodate new services due to a lack of programmability. These problems can be solved through the use of software-defined networks. This research paper presents a dynamic load-balancing algorithm for software-defined networks based on server response time and content mapping. The proposed technique dispatches requests to servers based on real-time server loads. This technique comprises three different modules, such as a request classification module, a server monitoring module, and an optimized dynamic load-balancing module using content-based routing. There are a variety of robust mathematical tools to address complex problems that have multiple objectives. Multi-Criteria Decision-Making is one of them. The performance of the proposed scheme has been validated by applying the Weighted Sum Method of the multi-criteria decision-making technique. The proposed method Server load balancing based on Multi-criteria Decision Making[SDLB-MCDM] is compared with different load-balancing schemes such as round robin, random, load-balancing scheme based on server response time [LBBSRT], and An SDN-aided mechanism for web load- balancing based on server statistics [SD-WLB]. The experimental results of SDLB-MCDM show a significant improvement of 58% when weights are equal and 50% when unequal weights are assigned to various QoS parameters in comparison with the ROUND ROBIN, RANDOM, LBBSRT and SD-WLB techniques.

Adaptive cell selection algorithm for balancing cell loads in 5G heterogeneous networks

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm’s performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms.

Enhanced network load balancing technique for efficient performance in software defined network.

Hash collisions and redirection of loads are major limitations for recent Hash IP algorithms. To overcome this, we propose a new Hash IP algorithm dubbed HDW in network load balancing (NLB) to increase network's efficiency, availability and scalability. We achieve the new Hash IP load balancing algorithm via a constructive merger with weighted scheduler (WS) technique and dynamic switching of routing path (DSP). This helps to reduce delays, jitters and additionally assures some level of security owing to the hashing process. Our findings after comprehensive simulations and performance evaluation depicts that our proposed HDW algorithm is relatively efficient as against other related load balancing algorithms for software defined network.

Distributed load balancing algorithm considering QoS for next generation multi-RAT HetNets

In this paper, we propose a distributed load balancing algorithm for multi-radio access technology (multi-RAT) heterogeneous networks (HetNets). The random deployment of small cells and the user mobility make the network load distribution uneven, which degrades overall network capacity and the QoS for users. Furthermore, the disparate capabilities of multiple RATs such as the different propagation delays in terrestrial and non-terrestrial RATs affect the latency requirements. To balance the network load in multi-RAT HetNets, we propose a distributed game-theoretic algorithm considering QoS for users. To that end, a cost function is defined to reflect the cell load status, required data rates, and delay constraints. In the proposed algorithm, overloaded cells iteratively minimize user cost in a distributed manner by associating a user with a less-loaded cell based on its cost. We provide analysis to show that minimizing each user cost in the game theory setting achieves a balanced load distribution among cells. We show that the proposed algorithm brings each cell load to a balanced state in a finite number of iterations. Via simulation, we show that the proposed algorithm achieves superior performance in terms of even load distribution, network throughput, and the number of users with satisfactory QoS.

An improved non-local awareness of congestion and load balanced algorithm for the communication of on chip 2D mesh-based network

Due to advancements in multi-core design technology, IC (Integrated Circuits) designers have expanded the single chip multi-core design. A privileged way of communication effectively between these multi-cores is a Network on-chip (NoC). Design of an effective routing algorithm capable of routing data to non-congested paths is the most notable research challenge in NoC, by retrieving congestion information of non-local nodes. This research proposed an improved congestion-aware load balancing routing algorithm. Non-local or distant links congestion awareness is done by propagating congestion information via data packets. By counting number of hops from the source node, in the quadrant of the destination node, an intermediate node has been defined, and after the calculation of the least congested route to the intermediate node, this route is also stored in the data packet for source routing. Furthermore, for load balancing network is partitioned into two areas called high congested area (HCA) and low congested area (LCA). For load balancing, from HCA a node in LCA is selected as output for data packets. Comparison of the proposed algorithm is done in the form of average latency, average throughput, power consumption, and scalability analysis under synthetic traffic patterns. Under simulation experiments, it is shown improvement in an average latency and throughput of the proposed algorithm is 31.28% and 5.28% respectively, than existing.

$$\tt F_{K-means}RA$$: Fuzzy K-Means Clustering Routing Algorithm for Load Balancing in Wireless Sensor Networks

$$\tt F_{K-means}RA$$: Fuzzy K-Means Clustering Routing Algorithm for Load Balancing in Wireless Sensor Networks

A Multi-Agent-Based VM Migration for Dynamic Load Balancing in Cloud Computing Cloud Environment

Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.

Load balancing in 5G heterogeneous networks based on automatic weight function

Load balancing is a major challenge in heterogeneous networks (HetNets) consisting of 5G and 6G ultra-dense small cells with long-term evaluation advanced (LTE-A) networks. A key factor in achieving efficient load balancing during user mobility is creating appropriate optimisation for handover control parameters (HCP). This paper proposes a coordinated load balancing algorithm for LTE-A/fifth generation (5G) HetNets. The algorithm automatically optimises HCP settings for a given user based on three bounded functions (the signal-to-interference-plus-noise ratio (SINR) of the user equipment (UE), the number of physical resource blocks (PRBs) per UE and the UE’s speed) as well as their automatic weight levels. A two-step target cell determination strategy is implemented according to the cell load level and RSRP criteria, ensuring that users are handed over to low-loaded target cells. A new HO procedure that considers the pilot signal power is also proposed, which includes the number of PRBs per UE and the RSRP. Cells with freer PRBs are prioritised in user association to provide load balance and enhanced throughput. The proposed load balancing algorithm is compared with five other load balancing algorithms selected from the literature. The simulation results reveal that under various mobile speed scenarios, the proposed load balancing scheme enhances network performance in terms of load level, throughput, spectral efficiency and call dropping ratio (CDR).

Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing

In the modern IT industry, cloud computing is a cutting-edge technology. Since it faces various challenges, the most significant problem of cloud computing is load balancing, which degrades the performance of the computing resources. In earlier research studies, the management of the workload to address all resource allocation challenges that caused by the participation of a large number of users has received important attention. When several people are attempting to access a given web application at once, managing all of those users becomes exceedingly difficult. One of the elements affecting the performance stability of cloud computing is load balancing. This article evaluates and discusses load balancing, the drawbacks of the numerous methods that have been suggested to distribute load among nodes, and the variables that are taken into account when determining the best load balancing algorithm.

A reinforcement learning-based sleep scheduling algorithm for compressive data gathering in wireless sensor networks

Compressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG.

A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.

Load Balancing Algorithms in Fog Computing

Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. The fog system extends storing and computing to the edge of the network, which can remarkably solve the problem of service computing in delay-sensitive applications besides enabling location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of service parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved by load balancing. In recent years, some research in load balancing algorithms in fog networks has been carried out, but there is no systematic study to consolidate these works. This article investigates the load-balancing algorithms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid algorithms. Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing algorithms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these algorithms are discussed.

Joint Routing and Scheduling With Cyclic Queuing and Forwarding for Time-Sensitive Networks

Various Industrial Internet of Things (IIoT) devices and their compelling applications have witnessed exponential growth over the past few years. Compared to the consumer internet, the industrial IoT often faces tough communication latency boundaries and low packet loss. Recently, time-sensitive networks (TSN) technique has been considered the most promising solution to tackle these issues, providing deterministic communication guarantees for time-sensitive applications. In TSN, to achieve bounded low latency, it introduces the Cyclic Queuing and Forwarding (CQF) mechanism to shape and regulate the transmission selection to provide the protected transmission window for time-triggered (TT) flows. Currently, a considerable amount of literature has explored traffic scheduling with the CQF mechanism in TSN. However, these methods ignore the impact of routing policy on scheduling optimization. In this paper, we design two algorithms to tackle the problem of joint routing and scheduling optimization with the CQF mechanism for TSN. We propose a TT flow sorting algorithm (JDPS) to measure the scheduling priority weight of each TT flow, which uses the normalization method to calculate the weight based on the flow's deadline, period, and size. Then, we propose a joint routing and scheduling optimization with load balance algorithm (JRS-LB) to improve the scheduling success radio and the network resource utilization, which jointly considers the injection time slot offset, scheduling, and routing of TT traffic. The evaluation results mainly focus on the scheduling success ratio, execution time, and resource utilization for the proposed algorithm compared to the Tabu and Naive algorithms. Extensive simulations indicate that the proposed JDPS and JRS-LB algorithms can improve the scheduling success radio compared with the other algorithms.

Intelligent Segment Routing: Toward Load Balancing with Limited Control Overheads

Segment routing has been a novel architecture for traffic engineering in recent years. However, segment routing brings control overheads, i.e., additional packets headers should be inserted. The overheads can greatly reduce the forwarding efficiency for a large network, when segment headers become too long. To achieve the best of two targets, we propose the intelligent routing scheme for traffic engineering (IRTE), which can achieve load balancing with limited control overheads. To achieve optimal performance, we first formulate the problem as a mapping problem that maps different flows to key diversion points. Second, we prove the problem is nondeterministic polynomial (NP)-hard by reducing it to a k-dense subgraph problem. To solve this problem, we develop an ant colony optimization algorithm as improved ant colony optimization (IACO), which is widely used in network optimization problems. We also design the load balancing algorithm with diversion routing (LBA-DR), and analyze its theoretical performance. Finally, we evaluate the IRTE in different real-world topologies, and the results show that the IRTE outperforms traditional algorithms, e.g., the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.

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.

Correction to: FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Consumption in Cloud Networks

Correction to: FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Consumption in Cloud Networks

A taxonomy of load balancing algorithms and approaches in fog computing: a survey

A taxonomy of load balancing algorithms and approaches in fog computing: a survey