Load Balancing Model Research Articles

Fuzzy-Genetic Model for Load Balancing and Machine Utilization in Job Shop Scheduling

Fuzzy-Genetic Model for Load Balancing and Machine Utilization in Job Shop Scheduling

Weight factor and priority-based virtual machine load balancing model for cloud computing

Weight factor and priority-based virtual machine load balancing model for cloud computing

Graph neural networks based queuing model for optimal load balancing in mobile ad hoc network

SummaryThis paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs.

A chemistry load balancing model for OpenFOAM

Efficient simulation tools are crucial for studying complex systems such as reacting flows where computational costs of computing chemical reaction rates can vastly exceed the costs for the integration of the convective and diffusive transport terms. Load imbalance in parallel computing poses a significant challenge for massively parallel reacting flow simulations. In response, a novel load balancing library has been developed to enhance OpenFOAM's solver performance in parallel environments. This library seamlessly integrates with OpenFOAM, offering ease of use and applicability to any OpenFOAM reacting solver incorporating finite-rate chemistry. In addition, it supports the standard and the dynamic adaptive chemistry model (TDAC) of OpenFOAM.The newly developed load-balanced standard and TDAC models address significant load imbalances by exchanging information between processes via MPI calls and tracking ODE solution times on a cell level. The TDAC model introduces dual tables on each core and enables immediate addition of computed solutions, enhancing computational efficiency. Validation on various test cases, including simulations on the HLRS Hawk supercomputer with up to 8000 cores, confirms identical results compared to the original unbalanced models, with notable speed-up factors of up to 6 for the standard and 5 for the TDAC model. Despite non-linear scaling at lower cell count per processor, load-balanced models consistently outperform unbalanced counterparts, making them the preferred choice for reacting flow simulations in OpenFOAM.

A dynamic reconfiguration model and method for load balancing in the snow-shaped distribution network

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original non-convex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration.

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

DBlock-RLB: An energy efficient framework for intelligent routing and trading based load balancing in SDWSN environment

Wireless Sensor Networks (WSNs) is an emerging field that enables them to work in complex scenarios with enough flexibility. The WSN systems are improved in terms of network flexibility by integrating Software Defined Network (SDN) to obtain SDWSN environment. However, security and energy efficiency are the major issues in the SDWSN environment which leads to low network lifetime. To resolve the existing issues, we proposed DAG-based BLOCKchain-Routing and Load Balancing Model (DBLOCK-RLB). The proposed approach performs routing and load balancing in the SDWSN environment in an energy efficient manner with high security. The SDWSN environment is composed of four major planes as data plane, switch plane, control plane, and application plane. The sensor nodes in the data plane are authenticated priory to ensure legitimacy and reduce unwanted network traffics by adopting Camellia Encryption Algorithm (CAE) which provides secret key to the user by considering several metrics. The authenticated sensor nodes are clustered to reduce energy consumption using Adaptive Threshold based Network Partitioning in which the circular SDWSN environment is split into equal parts and optimal Cluster Head selection is done by considering clustering metrics. From the clustered network, the CH is responsible for intelligent routing. For intelligent routing, we utilize Dual Agent-Twin Delayed Deep Deterministic Policy Gradient (DA-TD3) in which one agent for optimal forwarder selection and another agent for optimal route selection. After routing, the load balancing is done to reduce the network overhead and scalability issues. The proposed work performs trading based load balancing by utilizing Stackelberg Game Model in which all the local controllers are considered as followers and global controller is considered as leaders. All the transactions are stored in the DAG blockchain to reduce the scalability and Proof-of Authentication (PoAH) used to verify the transactions. The proposed work is simulated using NS-3.26 simulation tool and compared with several existing works by validation metrics like throughput at 2.55(mbps), latency at 27.1(ms), packet delivery ratio at 91.5(%), network Lifetime at 546.5(s), and energy consumption at 14.5 (J). The results show that the proposed work outperforms better than the existing works.

Combined optimization strategy: CUBW for load balancing in software defined network

Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.

Exploration of Energy Meter Data Security and Fast Response Technology in Spot Power Trading

Abstract With the advancement of smart grid construction, higher requirements have been put forward for energy meter data, which need to realize real-time, accurate, efficient, safe, and economical data transmission. In this paper, an intelligent collection of energy meter data is designed to monitor energy meter data. The PCA algorithm is utilized to downscale the energy meter data, and the LMR algorithm is combined to monitor abnormal data from the meter. Based on the smart contract in blockchain technology, a parallel Pedersen commitment algorithm based on privacy protection is designed, a hybrid signature algorithm is created to ensure the secure transmission of energy meter data and a cluster load balancing model for energy meter data is also designed in combination with Hopfield neural network. The PCA-LMR algorithm identifies 95 anomalies of the data in 12 months and under the encryption of a smart contract. The packet loss rate of energy meter data is maintained at about 2%, and after optimizing the cluster load capacity of energy meter data using the Hopfield neural network, the cluster load variance value is about 1.84 in 20 h. Modern technology can improve the secure transmission of energy meter data and enhance its rapid response ability to ensure the economic benefits of the power system.

Load balancing model for cloud environment using swarm intelligence technique

A distributed system with a shared resource pool offers cloud computing services. According to the provider’s policy, customers can enjoy continuous access to these resources. Every time a job is transferred to the cloud to be carried out, the environment must be appropriately planned. A sufficient number of virtual machines (VM) must be accessible on the backend to do this. As a result, the scheduling method determines how well the system functions. An intelligent scheduling algorithm distributes the jobs among all VMs to balance the overall workload. This problem falls into the category of NP-Hard problems and is regarded as a load balancing problem. With spider monkey optimization, we have implemented a fresh strategy for more dependable and efficient load balancing in cloud environments. The suggested optimization strategy aims to boost performance by choosing the least-loaded VM to distribute the workloads. The simulation results clearly show that the proposed algorithm performs better regarding load balancing, reaction time, make span and resource utilization. The experimental results outperform the available approaches.

Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Multi-objective cloud load-balancing with hybrid optimization

In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center; CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing; CSP, Cloud service providers; CSSA, Chaotic Squirrel Search Algorithm; DA, Dragonfly Algorithm; ED, Euclidean Distance; EDA-GA, Estimation Of Distribution Algorithm And GA; FF, FireFly algorithm; GA, Genetic Algorithm; HHO, Harris Hawk Optimization; IaaS, Infrastructure-as-a-Service; MGWO, Modified Mean Grey Wolf Optimization Algorithm; MMHHO, Mantaray modified multi-objective Harris Hawk optimization; MRFO, Manta Ray Forging Optimization; PaaS, Platform-as-a-Service; PM, Physical Machine; PSO, Particle Swarm Optimization; SaaS, Software-as-a-Service; SAW, Sample additive weighting; SLA-LB, Service Level Agreement-Based Load Balancing; TBTS, Threshold-Based Task Scheduling Algorithm; TS, Task Scheduling

Optimal load balancing in cloud: Introduction to hybrid optimization algorithm

One of the leading-edge technologies that have made it possible to pay as you go and distribute appropriate resources on demand is cloud computing. The process of optimizing the resource on cloud virtual machines (VMs) is called load balancing. The main goal is to evenly distribute tasks and resources among all machines to ensure that no node is overloaded or inactive. However, the load balancing methods currently used in cloud environments are ineffective in lowering the amount of time needed to respond to requests. This work intends to propose a novel optimal load balancing (LB) method in the cloud. For this a new Intercrossed Chimp and Bald Eagle Algorithm (IC&BA) algorithm is introduced that combines the algorithms like bald eagle search (BES) optimization and chimp optimization (CA) algorithm. Thereby, the proposed load balancing model works under the consideration of (i) Energy Consumption (ii) execution cost (iii) migration cost (iv) Make span and (v) load balancing parameters like response time, Turnaround time.

Fault tolerant and load balancing model for software defined networking controllers

Previous years have seen increased interest in a new network paradigm, Software defined networking (SDN). The basic idea behind this new concept consists of removing the smart parts of the connectivity components and moving them to a seul control point known as a controller. This centralized network view makes the network maintenance and management easier and facilitates the creation of new services. Despite many advantages of SDN, the concentration of network intelligence in a single controller raises serious challenges that impact SDN scalability, performance, and fault tolerance. One of the main problems in SDN is controller failure. In this article, we develop a fault tolerant model called fault-tolerant load balancing (FTLBC) for SDN controllers. To reduce the cascading failure problem, the proposed model requires the load of the failing controller to be shared among other controllers. In the case of a controller failure, The FTLBC model concentrates on distributing the load among the remaining controllers based on the load of the orphans' switches and the load of the remaining controllers.

Load-Balancing Target Channel Selection for Spectrum Handoff in Cognitive Radio Networks

In this study, a new probabilistic approach is proposed to balance the traffic loads of secondary users (SUs) among channels. Furthermore, an adaptive load-balancing model is formulated to meet the time-varying radio environment. We employ the overall system time of SUs to evaluate the latency performance of the proposed approach. Besides, we derive the optimal channel selection probability to minimize the overall system time. Simulation results clearly verify the validity of our approach by comparing it with other alternative channel selection strategies.

Load-aware dynamic controller placement based on deep reinforcement learning in SDN-enabled mobile cloud-edge computing networks

Software-defined networks (SDNs) can improve network resource utilization and optimize the performance of mobile cloud-edge computing networks (MECCNs) through unified and flexible network management. However, network traffic in MECCNs can change over time and space, which affects the performance of the control plane in an SDN. Further, an MECCN may require the temporary addition of network access points, further reducing the network management capabilities of the control plane. To ensure that the control plane can handle the constant changes in network traffic, adapt to dynamic changes in network access points, and provide continuous and efficient network management functions, this study focuses on the dynamic controller placement problem in SDN-enabled MECCNs. We study the deployment of a two-layer control plane and accordingly construct the corresponding delay, load balancing, and control reliability models. Next, we construct a joint optimization problem considering the developed delay, load balancing, and control reliability, and solve this problem using an algorithm based on the deep deterministic policy gradient algorithm. The experimental results demonstrate that the proposed algorithm outperforms other algorithms on a variable-node network.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Proactive and dynamic load balancing model for workload spike detection in cloud

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.

Construction of energy load balance model for interchange tunnel structure based on semi supervised learning model

BackgroundWith the current development of chemical energy and process engineering optimization, urbanization construction has been further promoted, and traffic tunnels have gradually increased, especially in the case of insufficient ground space, overpass tunnels have become an important traffic carrier. The structural energy load of interchange tunnel is an important factor. MethodBased on the semi supervised learning model, the energy load problem of interchange tunnel is modeled. Through the optimization of the structure and design requirements of the interchange tunnel, the energy problem in the structure was optimized. FindingsThrough the experiment of the monitoring effect, structure and data information under the semi supervised learning, it was found that the model based on the semi supervised learning model could well solve the settlement problem of the interchange tunnel. Compared with the actual measured values, it was found that the detection accuracy of the model reached 98.43%, and the data information processing efficiency index reached 0.832, It has high accuracy and processing efficiency. It can quickly adapt to the construction characteristics of the interchange tunnel, improve the test effect, and thus promote the smooth and effective development of the interchange tunnel construction project.