Load Balancing Control Research Articles

Retraction Note: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

Retraction Note: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

Enhancing Software‐Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q‐Learning Approach

ABSTRACTThe Software‐Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi‐controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in‐packet request‐response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration‐in and migration‐out domains by selecting a subset of switches. The architecture utilizes online Q‐learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real‐world applications.

An innovative NSGA-II-based Byzantine Fault Tolerant solution for software defined network environments

Byzantine fault tolerance (BFT) of the control plane in Software Defined Networking (SDN) is achieved by mapping each switch to 3f+1 number of controllers, where f represents the number of faulty controllers that can be tolerated at a time. A BFT approach protects the data plane from any potential malicious activity at the control plane by detecting the inconsistency among the response messages from multiple controllers. To compute the optimal mapping of switches to the controller, the existing literature does not consider some important parameters. This paper proposes a novel approach, named NBFT-SDN, that extends an artificial intelligence algorithm (i.e. NSGA-II) to solve a new formulated multi-objective optimization problem associated with this mapping. NBFT-SDN considers the very important parameters link reliability and link load along with switch-to-controller minimum delay, switch-to-controllers maximum reliability, controller-to-controller minimum delay, minimum link load, minimum hop count, and controller load balancing when mapping the switches to the controllers in optimum manner. The performance of our proposed approach is evaluated in comparison to a state-of-art approach using real network traces with network topologies of diverse sizes. Our proposed approach NBFT-SDN show improved network performance in terms of reliability, delay, hop count, load balancing and link load.

Load-aware switch migration for controller load balancing in edge–cloud architectures

As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load balancing. Unfortunately, most of the existing mechanisms only consider the switch with the highest flow request rate as the migration object in the process of switch selection, and ignore the migration cost generated in the switch migration activity, such as the update cost of flow request message and the deployment cost of migration rule, which may increase the controller load. Additionally, most of them choose the controller with light load as the target controller to associate with the switch to be migrated, without considering whether the target controller is overloaded after the switch migration, which leads to the low load balancing performance of the controller. In view of the above problems, this paper proposes a Load-Aware Switch Migration (LASM) mechanism in edge–cloud architectures. The LASM mechanism models and analyses the cost metrics affecting switch migration and selects lower-cost switches from overloaded controller-controlled domain networks for migration activities. Besides, the LASM mechanism models switch migration based on the 0-1 knapsack problem and avoids overloading the target controllers through a greedy policy to achieving optimal migration activities. The experimental results show that the proposed LASM mechanism improves controller load balancing performance by an average of 34.3%, eliminates migration costs by 30.2%, and reduces response times by an average of 39.3%, respectively, compared to existing solutions.

Green energy solution for grid integrated solar PV system for water storage towers

In India, water storage towers are present almost everywhere, especially across small towns to rural areas. The storage tanks either utilize diesel powered or utility grid fed induction motor (IM) water pumps. The large spaces available with water storage towers can be utilized to plant solar PV panel for large storage towers water pumping operation, hence, making a switch from diesel or grid run storage towers to solar operated storage towers. This paper presents a robust control of grid integrated solar PV-based water pump system with efficient and smooth transition between standalone and grid interfaced modes. A smart DC link voltage control is presented for distributed generation system (DGS), which provides full control on DC link voltage for both modes of operation at all dynamic load conditions. An enhanced phase-locked loop (EPLL) control is utilized for grid source converter (GSC) in grid control operation, making it flexible to different grid scenarios and power quality. It works for harmonics suppression, load balancing and grid side converter control. An enhanced phase-locked loop (EPLL) is implemented for synchronization control algorithm, which provides an accurate estimation of voltage angle even during frequency ramps and exhibiting superior performance particularly in terms of robustness against DC offsets. The primary requirement for smooth operation of grid-tied solar PV system is effective power management between different sources. The issues of power management and power quality, are discussed in this work, and solutions are presented. The implemented control algorithms are tested successfully for dynamic solar insolation, varying load conditions, unbalance in load, sudden grid outage condition. The system appropriateness is validated by experimental results.

An advanced ensemble load balancing approach for fog computing applications

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

QQAR: A Q-learning-based QoS-aware routing for IoMT-enabled wireless body area networks for smart healthcare

Recently, the Internet of Medical Things (IoMT) has gained broad acclaim in the academic and industrial sectors due to its use in integrating medical devices and healthcare systems. Wireless body area networks (WBANs) are a promising technology in smart healthcare for real-time patient monitoring and health data analysis by medical professionals. Efficient data transmission is essential in ensuring reliable and timely delivery of healthcare facilities. Healthcare services require reliable, delay-sensitive, energy- and congestion-aware communication, which makes it challenging to design a routing protocol. To address quality of services (QoS) in IoMT-enabled WBANs for healthcare, we propose Q-learning-based quality of services (QoS) aware routing (QQAR). Firstly, we used two-hop-based link reliability estimation to ensure link reliability from sources to sink nodes. Routing decisions using two-hop neighbor information guarantees successful packet delivery. Secondly, we differentiated the packets based on traffic priority, which assures the delivery of critical packets even in a congested network. Particularly, considering two-hop node velocity with energy balancing enhanced the guarantee of timely packet delivery before deadlines. In addition, the multi-sink approach enhanced network reliability. Finally, we utilized Q-learning to select suitable neighbor nodes for packet forwarding in a decentralized fashion. A multi-sink load balance and congestion control mechanism adjusts the routing decisions in the QQAR to avoid congestion. Our simulation and comparison results showed that QQAR outperforms the existing routing protocols across various performance metrics under distinct network conditions.

Optimization Method of RTOS System Delay Scheduling Based on Multi-Core Processor

In order to improve the task execution efficiency of multi-core processor, this paper studies the system delay scheduling optimization method of real-time operating system based on multi-core processor. Firstly, the core structure analysis model of multi-core processor data fusion and data acquisition in RTOS system is established, and then the task information flow of delay scheduling in RTOS system is constructed by using the correlation dimension feature fusion method. Based on the power domain non orthogonal multiple access control method, the data fusion state factor of multi-core processor is calculated for the initial scheduling node, and multi-core processor is used to control load balancing in the delay balancing scheduling process of RTOS system, The delay scheduling and balance control of real-time operating system are realized by multi-core processor. Simulation results show that the algorithm has low bit error rate and low task overhead in RTOS system, and can improve the task execution efficiency of multi-core processor.

Research on load balance control of power systems based on distributed energy storage technology

We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies’ Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks.

Carbon emission reduction capability assessment based on synergistic optimization control of electric vehicle V2G and multiple types power supply

With the increasing proliferation of electric vehicles (EVs) and the integration of renewable energy sources into the power system, significant challenges have arisen for power system load balancing and stability control. The carbon emission reduction capability of power systems through coordinated optimization control of electric vehicles and power sources has garnered considerable attention. This paper evaluates the impact of Vehicle-to-Grid (V2G) and power source coordinated optimization control on the carbon emission reduction capability of power systems, proposing a technique that takes into account the dynamic characteristics of electric vehicles, power sources, and carbon emissions. This approach integrates V2G technology, renewable energy generation, and load demand into a framework aimed at optimizing power system operations and minimizing carbon emissions. A case study using real load data from a city assesses the impact of EV V2G and power source coordinated optimization control on carbon reduction. The results indicate that by 2025, peak-valley regulation will reach 1211,000 MW, with a carbon reduction of 138,783.799 tons, corresponding to a total reduction ratio of 1.101%. This research can provide valuable insights for policymakers, power system operators, and researchers.

Balancing of Low-Voltage Supply Network with a Smart Utility Controller Leveraging Distributed Customer Energy Sources

In South Africa, there has been a rapid adoption of solar power, particularly inverter-based solar sources, in low-voltage (LV) networks due to factors such as load shedding, rising electricity costs and greenhouse gas emissions reduction. In residential LV networks, the alignment between solar supply and energy demand is less precise, necessitating larger battery storage systems to effectively utilize solar energy. Residential areas experience peak energy demand in the morning and evening when solar irradiance is limited. As a result, substantial energy storage is important to fully utilize the potential of solar energy. However, increasing inverter-based, customer-generated power creates an imbalance in the utility supply. This is because utility LV supply transformers have three phases, while individual customers have single-phase connections and no load balancing control mechanism. This supply imbalance adversely affects the overall power quality, causing energy losses, damage to devices and other issues. To address these problems, the paper proposes a smart control approach to minimize power imbalances within utility LV supply transformers. The controller uses customer battery storage in residential areas to balance the utility transformer phases. A laboratory model was built to simulate a three-phase low-voltage network with single-phase customers, both with and without a smart controller. The results show that closely monitoring and controlling individual inverters through a central controller can significantly improve the balance of the supply network.

Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

AbstractWith the successful implementation of Software‐Defined Networking (SDN) in data center networking, the way forward for its deployment in the ISP/Telco network is becoming prominent. Small and medium‐sized networks may easily adopt SDN. The research on SDN deployment and implementation for a large‐scale network is continuing. This paper properly presents the current research status of Controller Placement Problem (CPP) and Multi‐CPP (MCPP) over SDN with their specific challenges and provides a comprehensive review of the major performance metrics, that is, latency, and controller load balancing techniques. This survey highlights the use of network partitioning‐based CPP and clustering approaches and their benefits in the context of SDN deployment. Moreover, this paper highlights the importance of implementing SDN and SDN security issues into ISP/Telco networks. Finally, we provide some key areas of ongoing research and discuss the future research direction regarding the various SDN‐based Controller Placement (CP) issues in the next‐generation IP and advanced networking technologies.

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

The deployment of distributed multi-controllers for Software-Defined Networking (SDN) architecture is an emerging solution to improve network scalability and management. However, the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience. Thus, we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures. This fault-tolerance strategy has been applied to distributed SDN control architecture, which allows each switch to migrate to next controller to enhance network performance. In this paper, the Reliable and Dynamic Mapping-based Controller Placement (RDMCP) problem in distributed architecture is framed as an optimization problem to improve the system reliability, quality, and availability. By considering the bound constraints, a heuristic state-of-the-art Controller Placement Problem (CPP) algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers. The algorithm identifies the optimal controller location, minimum number of controllers, and the expected assignment costs after failure at the lowest effective cost. A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms. The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

An Enhanced Lightweight Security Gateway Protocol for the Edge Layer

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined.

Optimization of Load-Balancing Strategy by Self-Powered Sensor and Digital Twins in Software-Defined Networks

This work is aimed at smart sensors in the context of the boom in intelligent manufacturing under artificial intelligence (AI). First, an intelligent industrial system is innovatively constructed based on AI and Digital Twins technology. Its sensors use self-powered sensors based on solar energy. Secondly, layered architecture and four modules of the balancing system are built based on the Software Defined Network with programmability and flexibility, and a controller load balancing strategy is proposed based on the layered architecture. Besides, the link load balancing algorithm is optimized based on Dijkstra’s algorithm (This algorithm is the leading AI algorithm used here). Finally, simulation experiments are carried out on different controllers and algorithms. The results show that the AI system has a high control accuracy. Controller 5 experiences a surge in load at 45s, exceeding the load threshold. Controllers 1 and 5 are selected for load balancing. Migration can be done quickly when load balancing is completed within 5 seconds. Control Transfer Load Balance has the longest response time when the load value reaches 20% to 70%. The load policy has the shortest response time, indicating the best effect. In addition, the load balancing algorithm proposed here fully considers the link conditions in the link load comparison and achieves an excellent effect. The load rate increase affects the network delay’s continuous increase. Moreover, the highest network delay of the algorithm reported here is 0.25ms; that of the Yen algorithm is 0.31ms; that of Dijkstra’s algorithm is 0.43ms, which is 83.3% lower than that of the unoptimized algorithm. In the 10%~100% load rate range, the packet loss rate of the proposed algorithm is lower than that of the other two algorithms. Load balancing strategies outperform other standard algorithms in terms of network performance. This study provides some reference for the development of self-powered sensors in the field of industrial manufacturing.

Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN

Clustering, an unsupervised machine learning technique, plays a crucial role in partitioning unlabeled data into meaningful groups. K-means, known for its simplicity, has gained popularity as a clustering method. However, both K-means and the LAC algorithm, which utilize learning automata, are sensitive to the selection of initial points. To overcome this limitation, we propose an enhanced LAC algorithm based on the K-Harmonic means approach. We evaluate its performance on seven datasets and demonstrate its superiority over other representative algorithms. Moreover, we tailor this algorithm to address the controller placement problem in software-defined networks, a critical field in this context. To optimize relevant parameters such as switch–controller delay, intercontroller delay, and load balancing, we leverage learning automata. In our comparative analysis conducted in Python, we benchmark our algorithm against spectral, K-means, and LAC algorithms on four different network topologies. The results unequivocally show that our proposed algorithm outperforms the others, achieving a significant improvement ranging from 3 to 11 percent. This research contributes to the advancement of clustering techniques and their practical application in software-defined networks.

Low-latency controller load balancing strategy and offloading decision generation algorithm based on lyapunov optimization in SDN mobile edge computing environment

Low-latency controller load balancing strategy and offloading decision generation algorithm based on lyapunov optimization in SDN mobile edge computing environment

Enable the proactively load-balanced control plane for SDN via intelligent switch-to-controller selection strategy

The novelty of the software-defined network(SDN) is to separate the control plane and the data plane for easier manipulation of the network. The distributed control plane is designed to achieve more powerful computation capacity and address the single-point failure problem. However, it also poses a new challenge that how to arrange switch-controller associations effectively. The direct static configuration cannot adapt the time-varying requests from switches well, and it would result in imbalance problems on the control plane and cause long-tail latency. Thus, it is necessary to take proper actions to adjust the switch-to-controller association dynamically. Existing controller-based load balancing methods need to communicate with the switches frequently and incur not only high assumptions of the rare control channel of SDN but also high computation costs. In this paper, we provide a switch-based solution that puts Reinforcement Learning agents on all Switches (RLoS). Instead of setting static rules predefined by operators, RLoS makes each switch actively select the best controller. RLoS treats every switch as an independent agent with its own neural network and parameters. With the carefully designed training algorithm, the agents could choose their preferable controllers via their local information. The results show that even with partial observation, the RLoS still can achieve considerable improvement in the load balance among all controllers compared with those controller-based association benchmarks. Our RLoS could decrease the maximum response latency among controllers by about 5%∼15% under different scenarios on average.

Controller Location and Load Balancing Integrated Solution

The usage of multi-controller SDNs is currently the most efficient approach for constructing the core of communication networks of the fifth and following generations. One of the top priorities in the study of this topic is occupied by the optimisation of the network core construction since it involves relatively high expenses when developing communication networks of the fifth and future generations. Due to the complexity of the problems being tackled, there are currently a number of load balancing algorithms and algorithms for arranging controllers in multicontroller networks that are based on meta-heuristic methods. These algorithms allow for the optimum possible utilisation of controller resources in such networks. However, a comprehensive solution to the load balancing and controller placement issues has yet to be discovered. The answer to such an issue is the focus of this article. The report suggests using network clustering in conjunction with the meta-heuristic chaotic salp swarm technique, which has shown to be effective in prior research on the challenges of creating multi-controller networks, to accomplish this goal. The salp swarm algorithm in the paper is adjusted to take into account the integral solution to the problem of deploying controllers based on clustering of a multi-controller network and load balancing. By contrasting the simulation results with those from the well-known meta-heuristic particle swarm algorithms optimization and the grey wolf GWO, as well as the previous version of the chaotic salp swarm algorithm CSSA, the effectiveness of the proposed solution was evaluated.

Congestion and Energy Aware Multipath Load Balancing Routing for LLNs

The Internet of Things (IoT) is presently in its golden era with its current technological evolution towards digital transformation. Low-power and Lossy Networks (LLNs) form the groundwork for IoT, where the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is designated by Internet Engineering Task Force as the benchmark protocol for routing. Although RPL, with its unique capabilities, has addressed many IoT routing requirements, Load balancing and Congestion control are the outliers. This paper builds on the RPL protocol and proposes a multipath Congestion and Energy Aware RPL (CEARPL) that alleviates the load balancing and congestion concerns associated with RPL and improves the network performance. For congestion avoidance, a Congestion and Energy Aware Objective Function (CEA-OF) is suggested during parent selection that considers multiple metrics like Child Count metric, Estimated Lifetime metric, and Queue Occupancy metric, to equally distribute the traffic in LLNs. The Queue Occupancy metric is used to detect congestion in the network, and a Multipath routing strategy is utilized to mitigate the congestion in the network. A comparison of the performance of CEA-RPL was made against the existing Objective Functions of RPL, OFO, and MRHOF, as well as COM-OF, utilizing Contiki OS 3.0's Cooja emulator. CEA-RPL projected superior results with power consumption lowering by 33%, end-to-end delay decreasing by 30%, queue loss ratio reducing by 49%, and packet receiving rate and network lifetime improving by 7% and 49%, on an average, respectively.