Load Balancing Method Research Articles

SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks

SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to identify under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, makespan, reliability, resource utilization, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

Method of load balancing for computer cluster of data processing center

The article presents a description of the load balancing method for a computing cluster of a data processing center (DPC), which is based on a probabilistic approach to proactive forecasting of packet traffic states, formed on the basis of the results of its statistical, nonlinear and spectral analysis. The fractal properties of network traffic are the rationale for the possibility of prediction, allow with a fairly high probability to predict the appearance of bursts and drops in its activity at certain time intervals, identify periods of possible overload of servers and network equipment, and make it possible to develop methods for effective planning and distribution of tasks within the data center, ensuring a statistically uniform loading its functional elements. The spectral analysis of the time series is carried out according to the normalized deviations of the actual levels from the smoothed ones. The absence of significant peaks in the spectral estimates indicates the absence of periodic fluctuations. It is shown that the summation of cycles of different periods of the dynamics of the time series, based on the use of the most significant harmonics of the spectrum, determines the moments of occurrence of subsequent anomalies in its development. The process of identifying significant harmonics of the spectrum is based on the study of its spectral power density using the Fourier transform. The developed method is able to provide a solution to the problem of efficient planning and distribution of tasks in a data center computing cluster in order to optimize the use of resources, speed up task execution time and reduce application processing costs.

Система управления выравниванием нагрузки в многодвигательном электроприводе скребкового конвейера

Скребковый конвейер относится к основным средствам доставки угля от очистного комбайна до транспортных средств конвейерного штрека при подземной разработке угольных месторождений. В мощных скребковых конвейерах, в условиях ограничения оборудования по габаритам, используется многодвигательный электропривод, включающий от двух до четырех электродвигателей. Дисбаланс нагрузки между двигателями в таком электроприводе является основной проблемой. С целью решения этой задачи проанализированы существующие способы выравнивания нагрузки в многодвигательном электроприводе и предложена стратегия частотно-регулируемого привода с векторным управлением нескольких двигателей, основанная на принципе «ведущий-ведомый». При этом главный двигатель управляется с помощью регулятора скорости, а ведомый - с использованием стратегии заданного крутящего момента. Данная схема управления реализована в среде MatLab/Simulink. Результаты моделирования показали, что режим управления по принципу «ведущий-ведомый» может решить проблему выравнивания нагрузки скребкового конвейера во всех режимах работы, тем самым увеличив срок его службы, снизив износ оборудования, а значит, повысив эффективность производства шахты.

Load Balancing Framework for Cross-Region Tasks in Cloud Computing

Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and, can balance the traffic load by service providers, resulting in seamless and uninterrupted services. Different types of algorithms and techniques are available that can manage the load balancing in the cloud services. In this paper, a newly proposed method for load balancing in cloud computing at the database level is introduced. The database cloud services are frequently employed by companies of all sizes, for application development and business process. Load balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource utilization. Load balancing is the process of distributing the load on various nodes to ensure that no single node is overloaded. To avoid the nodes from being overloaded, the load balancer divides an equal amount of computing time to all nodes. The results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

Sequential Load Balancing for Link Aggregation Enabled Heterogeneous LiFi WiFi Network

Light fidelity (LiFi) is one of the promising communication technology for 6G internet of everything (IoE), however, it requires a line-of-sight (LoS) link; in contrast, WiFi can support moderate data rates even in the absence of LoS. As the electromagnetic spectrums of LiFi and WiFi do not overlap, these technologies can be used for concurrent communication, thus, resulting into a link aggregation (LA) enabled heterogeneous LiFi WiFi networks (HLWN). However, for the most efficient utilization of a LA enabled HLWN, proper load balancing is essential. Therefore, in this paper, we propose a novel sequential load balancing method with reinforcement learning (RL)-based access point (AP) assignment followed by optimum resource allocation for LA enabled HLWN. It is observed that the proposed method outperforms the baseline received signal strength (RSS) scheme by around 37% and 56% in terms of average data rate and user satisfaction, respectively. Furthermore, the proposed method performance closely matches to the exhaustive search with the added advantage of reasonably low complexity. Additionally, the robustness of the proposed method is proven by considering two different user’s mobility models in this work.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

Recursive redundancy zoning for highly available microservices architecture

This paper aims to advance high availability in microservices by a novel approach called recursive redundancy zoning (RRZ). This research adopted an integrated approach to high availability by enhancing the chain oriented load balancing method. It adopts a combination of redundancy nesting and containerisation to ensure early fault detection and isolation. An empirical and concrete insight is provided in this research that indicated that the RRZ approach provides the best performance in terms of response time, load balancing and scalability. Comparison of this novel approach with existing approaches shows an improvement in availability. Since the experimental tests and evaluations carried out was based on a simulated environment, further research is ongoing to evaluate the performance of the RRZ approach in enterprise cloud environment. This research with further refinements could be a standard for design of mission critical information systems that require a high level of stability under unreliable load conditions.

Load-Balancing Method for LEO Satellite Edge-Computing Networks Based on the Maximum Flow of Virtual Links

With the increasing number of satellites in orbit, traditional scheduling methods can no longer satisfy the increasing data demands of users. The timeliness of remote sensing images with large data volumes is poor in the backhaul process through low-earth-orbit (LEO) satellite networks. To address the above problems, we propose an edge-computing load-balancing method for LEO satellite networks based on the maximum flow of virtual links. First, the minimum rectangle composed of computing nodes is determined by the source and destination nodes of the transmission task under the configuration of the 2D-Torus topology of LEO satellite networks. Second, edge computing virtual links are established between computing nodes and users. Third, the Ford-Fulkerson algorithm is used to obtain the maximum flow of the topology with virtual links. Finally, a strategy is generated for computing and transmission resource allocation. The simulation results show that the proposed method can optimize the total capacity of the multi-node information backhaul in the remote sensing scenario of LEO satellite networks. The effectiveness of the proposed algorithm is verified in several special scenarios.

Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes.

An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters.

This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.

High-performance Shallow Water Model for Use on Massively Parallel and Heterogeneous Computing Systems

This paper presents the shallow water model, formulated from the ocean general circulation sigma model INMOM (Institute of Numerical Mathematics Ocean Model). The shallow water model is based on software architecture, which separates the physics-related code from parallel implementation features, thereby simplifying the model’s support and development. As an improvement of the two-dimensional domain decomposition method, we present the blocked-based decomposition proposing load-balanced and cache-friendly calculations on CPUs. We propose various hybrid parallel programming patterns in the shallow water model for effective calculation on massively parallel and heterogeneous computing systems and evaluate their scaling performances on the Lomonosov-2 supercomputer. We demonstrate that performance per a single grid point on GPUs dramatically decreases for small grid sizes starting from 219 points per node, while performance on CPUs scales up to 217 well. Although, calculations on GPUs outperform calculations on CPUs by a factor of 4.7 at 30 nodes using 60 GPUs and 360 CPU cores at 6100 x 4460 grid size. We demonstrate that overlapping kernel execution with data transfers on GPUs increases performance by 28%. Furthermore, we demonstrate the advantage of using the load-balancing method in the Azov Sea model on CPUs and GPUs.

Research on load balancing of parallel component programs based on quantum particle swarm optimization algorithm

Parallel component applications are often deployed on heterogeneous clusters. Load balancing is very important for their performance requirement. Existing load balancing methods have high performance cost and poor balance effect. Based on the analysis of structures of parallel component applications, we established the mathematical model of load balancing for parallel components on heterogeneous clusters. We use the quantum particle swarm optimization algorithm to search the optimal solution of the proposed mathematical model and determine the best load balancing scheme. Comparing with the methods based on real-time detection and other swarm intelligence optimization algorithms, our method has lower balance cost, less number of iterations and better performance.

Phase reassignment for load balance in low-voltage distribution networks

Load balancing is one of the most widely used techniques to reduce losses and improve service quality in low-voltage networks. Many methods have been proposed, including the customer phase reassignment, which has multiple advantages. However, its application represents a real challenge if the data availability is limited (only a few variables from some nodes are measured). This paper presents a new method for load balancing on a three-phase feeder by switching the phase connection of single-phase customers. It is based on Deterministic Crowding, a variant of Genetic Algorithms with few and easily adjustable parameters. In contrast to related works, we establish an imbalance target value and minimize the number of customers required to switch. The proposed method requires the phase current profiles from only a percentage of the customers, reducing the number of smart meters necessary and the related investment. The method was validated using real data, analyzing different scenarios with different percentages of measured customers compared on the basis of imbalance index, neutral current, and transformer lifespan reduction. Results show a significant improvement in load balancing with few changes, even when only 30% of customers are measured.

Simulation and Modeling Algorithm for Terminal Container Handling Intelligent Management Based on Internet of Things and Big Data Technology

In order to improve the effect of intelligent terminal container management, this paper improves the Internet of Things and big data technology, analyzes the RFID middleware architecture based on the actual needs of container handling management, and proposes a new method of RFID middleware load balancing. Moreover, this paper combines the Internet of Things technology and big data technology to analyze the terminal container loading and unloading process and build a corresponding intelligent system. After constructing a terminal container handling intelligent management system based on the Internet of Things and big data technology, the performance of the system is verified, and multiple sets of simulation data are used to conduct research. The experimental research results show that the terminal container handling management system based on the Internet of Things and big data constructed in this paper basically meets the actual needs of use.

Load balancing method for KDN-based data center using neural network

The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved.

Research on Remote Upgrade Method of Distributed Load Balancing for Power Monitoring Terminal

With the increasing application of power monitoring terminal and the increasing demand for its function, it is necessary to upgrade the field embedded software remotely.The existing online upgrade schemes generally deal with the terminals one by one, and there are also distributed upgrade schemes. However, due to the large differences in the communication performance of terminals in different places, there is a problem of load imbalance, resulting in the low efficiency of the whole upgrade. Aiming at the efficiency of remote online upgrade of power monitoring terminal, a distributed load balancing remote upgrade method is proposed. The power monitoring terminal is upgraded at the same time through the cloud platform, and the genetic algorithm is used for load balancing processing, so as to improve the efficiency. The practical results show that this remote upgrade method improves the upgrade efficiency of the terminal by comparing a variety of upgrade schemes, compared with no load balancing method, the average time of upgrading a terminal is reduced by 36%, which achieves good application effect in practical application.

ScD of Computer Science. Load balancing method in SDN networks based on ACS with improved dynamic resiliency

The article discusses the topical issue of load balancing in distributed computing systems. The analysis of existing solutions is carried out, tasks, problems and practical significance are determined. An improved balancing method using the checkpoint method and an additional confidence factor is proposed, which made it possible to ensure a uniform load on the controllers, while maintaining an acceptable level of efficiency. An assessment of the performance and comparison of the proposed method with existing methods is carried out, as well as steps for further research are indicated.

A practical probabilistic approach for load balancing in data-scarce LV distribution systems using discrete PSO and 2 m + 1 PEM

Unbalanced distribution of single-phase customers among the three phases is an important factor in causing unbalance in low-voltage (LV) distribution systems. This unbalance phenomenon has some important consequences as higher losses in the distribution system, increasing voltage unbalance, and higher neutral wire current. The key challenge is that these networks only have the monthly reading of customers’ meters which is called data-scarce distribution systems due to the lack of enough information. In this paper, a method is proposed in order to have a balanced distribution of single-phase customers between the phases based on particle swarm optimization (PSO) algorithm to reduce the average-load losses (which is calculated losses in the average load of all of the customers and is an attainable data in real-world data-scarce distribution systems). First, a comparative study is performed between two load balancing methods. In the first method, the transformer neutral wire current is reduced. This method is used generally in electricity distribution companies. In the second method, the balancing is performed in order to reduce the losses in the average-load. Then, a probabilistic approach is proposed in order to manage the uncertainty of single-phase customer's consumption, which is an important issue in low-voltage distribution systems balancing. Numerical studies are performed on a practical system and the results show that the proposed method has higher performance comparing to the first method used by distribution companies. Also, it is observed that by rephasing of some single-phase customers, losses are reduced significantly.

Load Balancing Server and Homomorphic Encryption in Internet of Things

User demand for Internet of Things (IoT) services is ever increasing. The growing user demand can lead to an escalation of server workloads and faces the threat of theft of critical data. Consequently, a system is necessary to balance the server load and is protected with data encryption. In this study, we designed a system to share server workloads using load balancing methods. The load balancing technique uses open-source web server software. The system is equipped with data security using a homomorphic encryption algorithm from AES on the sender's side. The system embeds in an IoT telemedicine apparatus. During testing, we analyze the error requests that arrive at each server for the HTTP GET and POST methods. We also evaluate the speed of data encryption and decryption. The results showed that server load balancing reduces the number of error requests for the GET method by 97%. Meanwhile, the number of error requests for the POST method decreases by 66.75%. Observations reveal that the average homomorphic encryption speed, computation time, and decryption time are 15.66 ms, 764.18 µs, and 362.49 µs, respectively.