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Summary: Flying 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. [ABSTRACT FROM AUTHOR]

Ensuring optimal load balancing is imperative for maintaining reliability and upholding quality of service as specified in service-level agreements (SLAs) for cloud computing providers. This research addresses the most common shortcomings of existing state-of-the-art methods, which often lack responsiveness and struggle to adapt to exponentially increasing demand, especially in the era of the internet of things (IoT). The proposed hybrid approach surpasses current literature approaches in performance metrics such as makespan, response time, number of cloudlet migrations, and SLA violations. It operates on two levels, initially employing a k-means clustering algorithm to group servers within each datacenter based on similar utilization rates. Subsequently, a round-robin method allocates task groups sequentially to non-overloaded clusters, and within each cluster, a genetic algorithm optimally assigns tasks to servers. This multilayered approach facilitates hot-deployment and scalability in operational cloud environments while promoting strong interoperability and decoupling of core mechanisms missions. Simulation experiments conducted on CloudSim Plus validate the superiority of our method, positioning it as a robust solution for enhancing load balancing and tasks scheduling in cloud environments, especially in the face of rapidly increasing IoT-related demands. [ABSTRACT FROM AUTHOR]

This work presents a performance-oriented study of a heterogeneous application developed with Intel OneAPI to solve two well-known diffusion problems: heat diffusion and image denoising. We have explored CPU+iGPU and CPU+FPGA schemes, applying dynamic load balancing and conducting experiments on Intel DevCloud. The results demonstrate that the CPU+iGPU scheme outperforms the execution times achieved by the fastest device when the problem is sufficiently computationally demanding. We also found that the performance of the CPU+FPGA scheme is heavily affected by bandwidth limitations and specific strategies to manage memory efficiently are required. Moreover, it was demonstrated that dynamic workload balancing is crucial due to possible performance fluctuations in any of the implicated devices. In conclusion, Intel OneAPI provides a helpful tool for multi-platform development using a unique high-level language, DPC++. However, developing specific code for each platform is necessary to achieve optimal performance. [ABSTRACT FROM AUTHOR]

To perform a set of tasks in a robotic network cloud system as fast as possible, it is recommended to use a scheduling approach that minimizes the makespan. The makespan is defined as the time between the start of the first scheduled task and the completion of all scheduled tasks. Load balancing is a technique to distribute incoming tasks across processing units in a way that the resource utilization is optimized and the makespan is minimized. Robotic network cloud systems can be conceptualized as graphs, with nodes representing hardware with independent computing power and edges representing data transmissions between the nodes. The initial scheduler assigns a set of newly arrived tasks to the processing units capable of performing them. To reduce the response time we can replicate some of the tasks and assign them to different processing units. This results in some tasks becoming redundant. Assigning redundant tasks refers to determining which processing unit should receive the replicated tasks. Load balancing for redundant allocation can be viewed as assigning tasks to multiple processing units with different resource sizes so that the load is evenly distributed among the units. We propose a technique for load balancing, the ordered balancing algorithm, to minimize the makespan in the redundant allocation and scheduling problem. We prove theoretically the correctness of the proposed algorithm and illustrate with simulations, using R version 4.0.3, the obtained results that outperform other recent load balancing proposals. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose an image processing device based on load balancing algorithm, which can realize efficient and accurate data processing in physical fitness detection. The characteristics of optical sensor image processing equipment and the demand of physical fitness detection are studied and analyzed. Then, according to the principle and strategy of load balancing, a load balancing algorithm suitable for iot optical sensor image processing equipment is designed. The algorithm can automatically adjust the task allocation according to the load of the device and ensure the load balance of each device. Finally, the experiment is carried out in the actual scene, and the comparison with the traditional method is analyzed. The experimental results show that the iot optical sensor image processing equipment based on load balancing algorithm has excellent performance in the physical fitness detection. It can effectively reduce the load pressure of the equipment and improve the speed and accuracy of data processing. The application potential of the equipment is huge, and it can play an important role in sports training, health monitoring and other fields. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have emerged as a one of the most fascinating areas of lookup in the past few years principally due to its massive quantity of workable applications. However limited energy, node deployment strategy, routing techniques has considerable impact on the overall performance of network. This paper proposes sustainable honey bee ratel based hybrid wireless sensor network (SHHWSN) layering framework for improving load balancing and scalability issues in network. In SHHWSN tessellated hexagonal shape clusters are formed to cover entire geographical region without any gaps. Hybrid routing schema constrained with redundant data identification policy used in this, seeks to optimize energy utilization. The proposed SHHWSN was evaluated and validated by simulation, and the results were compared with earlier schematics where mostly one sided stochastic node deployments were followed. The experimental results show that SHHWSN has superior performance in terms of network alive nodes, delay, energy and throughput. The proposed SHHWSN obtained values of 50 nodes is 3, 0.061, 0.14, 0.89, 100 nodes is 3,0.037, 0.19, 0.551, 150 nodes is 13, 0.081, 0.23, 0.415, and 200 nodes is 23, 1.01, 0.25, 0.356, which is significantly more effective than current techniques. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm has emerged to connect the number of devices using the Internet resulting in the deployment of smart cities. Cloud computing has been applied to execute the computational demands of IoT devices by collecting data from the physical environment of a smart city. However, cloud computing could not become a proper choice for latency-sensitive applications because of remote cloud data centers. To overcome this challenge, fog computing has emerged to deal with the inherent limitations of cloud computing environment through provision of computing to the edge of a network. However, resource allocation of IoT service requests among fog nodes is considered as an NP-hard problem, which should be addressed in the fog computing environment. In this paper, an efficient optimization approach based on improved binary particle swarm optimization (IBPSO) algorithm has been provided for resource allocation of IoT requests in the hybrid fog–cloud computing environment. The proposed method aims to reduce the service request latency with ensuring load balancing among fog nodes. The performance of the proposed algorithm has been compared by the binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO)-based, and ranked-based resource allocation methods in terms of latency, missed deadline requests, run time, and load balancing. The results show that the proposed algorithm outperformed with an average of around 11 % , 22 % , 21 % , and 22 % percent in the IBPSO-based method rather than the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods, respectively. Moreover, the resource allocation based on IBPSO achieved around 11 % , 28 % , 27 % , and 25 % decline in total latency compared to the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods. Furthermore, the run time of the proposed algorithm could enhance by 45 % , 9 % , and 8 % compared to the BGA-based, BPSO-based, and BGWO-based resource allocation methods. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, load balancing technology in cloud services has become a critical component in ensuring service quality and system stability. Traditional load balancing methods, often relying on static parameters and preset rules, face challenges in flexibly responding to the dynamic changes in cloud service demands. Recent studies have begun to explore the application of machine learning algorithms to optimize load balancing, aiming to enhance the system's adaptive adjustment capabilities. This study proposes an innovative approach, applying the solution of the heat conduction equation to the optimization of cloud service load balancing issues. It simulates and analyzes the dynamic changes in load distribution, proposing corresponding optimization strategies. The first part of this research focuses on designing a model that integrates genetic algorithms and neural networks to solve the inverse problem of the twodimensional nonlinear heat conduction equation, namely, the accurate prediction of thermal physical parameters. By simulating the heat conduction process, this model can reflect the dynamic distribution characteristics of server loads and guide the adjustment strategy of weights. Furthermore, an adaptive dynamic load balancing strategy algorithm is proposed. By optimizing the existing engine x (Nginx) weighted least connections algorithm, an efficient adaptive algorithm is designed and implemented. This algorithm adjusts server weights dynamically based on real-time load data, enabling cloud services to respond more flexibly and efficiently to different service requests. The findings of this research not only enhance the processing capability and resource utilization rate of cloud services but also provide more scientific and precise theoretical support for load balancing through the introduction of new algorithmic models. Additionally, the proposed adaptive dynamic load balancing strategy algorithm has demonstrated good performance in practical deployment, offering new perspectives and technical paths for the research and practice of cloud service load balancing. [ABSTRACT FROM AUTHOR]

Processing of data gathered from new communication devices, such as Internet of Things (IoT)-based technology, has grown dramatically in the past decade. Resource management plays a vital role in cloud/fog-based platforms' efficiency. Alternatively, a deadline-based workflow scheduling mechanism is an approach to resource management that increases cloud/fog computing efficiency. However, most proposed methods may overload some resources and underload others. Consequently, adopting a proper load-balancing approach has a major impact on optimizing Quality of Service (QoS) and improving customer satisfaction. This paper presents a 4-layer software architecture for analyzing workflows and dynamic resources in cloud/fog/IoT environments to address such a problem. This approach also considers workload and presence prediction of IoT nodes as dynamic resources. Moreover, the 4 + 1 architectural view models represent architecture layers, components, and significant interactions. Architecture components are ultimately proposed to meet quality attributes such as availability, reliability, performance, and scalability. The proposed architecture evaluation is according to the Architecture Tradeoff Analysis Method (ATAM) as a scenario-based technique. Compared with previous works, various scenarios, and more quality attributes are discussed within this evaluation, in addition to analyzing and predicting workload and the presence prediction of dynamic resources. [ABSTRACT FROM AUTHOR]

Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won't be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine's (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987. [ABSTRACT FROM AUTHOR]

The increasing wafer yield and shrinking size pose challenges for real-time defect inspection using a single computer. To address this, we propose enhancing real-time performance by increasing computing devices. However, uneven load distribution due to device performance or defects variations can reduce inspection efficiency. We introduce computer cluster load balancing to solve this, utilizing a load-balancing model and an objective function. We propose the adaptive discrete quantum particle swarm optimization algorithm (ADQPSO) for efficient load balancing and implement the adaptive dynamic smooth weighted round-robin algorithm based on ADQPSO. Experimental results demonstrate that our algorithm achieves the fastest execution speed and up to a 24% improvement in performance. Our approach significantly improves real-time performance and efficiency in wafer surface defect inspection. [ABSTRACT FROM AUTHOR]

Load balancing in cloud computing refers to dividing computing characteristics and workloads. Distributing resources among servers, networks, or computers enables enterprises to manage workload demands. This paper proposes a novel load-balancing method based on the Two-Level Particle Swarm Optimization (TLPSO). The proposed TLPSO-based load-balancing method can effectively solve the problem of dynamic load-balancing in cloud computing, as it can quickly and accurately adjust the computing resource distribution in order to optimize the system performance. The upper level aims to improve the population's diversity and escape from the local optimum. The lower level enhances the rate of population convergence to the global optimum while obtaining feasible solutions. Moreover, the lower level optimizes the solution search process by increasing the convergence speed and improving the quality of solutions. According to the simulation results, TLPSO beats other methods regarding resource utilization, makespan, and average waiting time. [ABSTRACT FROM AUTHOR]

Time series reconstruction of astronomical catalogues is an important part of data archiving and a basis for time-domain astronomical analysis in the era of time-domain astronomy. As the field of view and sampling frequency of various time-domain telescopes increase, the amount of data to be processed becomes larger and larger. How to optimize the spatial and temporal efficiency of this process with the aid of computer technology becomes a hot issue. To address the problem of spatial efficiency, in this paper, we propose a time series data compression algorithm based on the negative database and dynamic programming, and on this basis, we design a multi-level storage and access query architecture for hot data and non-hot data, which greatly compresses the storage space of data while ensuring the query efficiency. To address the issue of time efficiency, this paper proposes a spatio-temporal data partitioning and layout algorithm suitable for distributed architecture, whose nested round-robin strategy has a wide range of load balancing effects on different spatial locations, temporal locations, and different ranges of temporal data queries, which can effectively ensure the execution efficiency of the distributed system. Experimental results show that the proposed optimization algorithm can keep the system at a low load skewness level of about 4% and save about 83% of storage space. [ABSTRACT FROM AUTHOR]

Load imbalance severely affects cluster performance, and the polarization of resources due to load skewing leads to further worsening of system throughput and latency problems. The proliferation of tasks to be processed in the big data era leads to more severe load skewing. How to cope with the surge of skewed data stream in the context of big data is a new challenge now. In this paper, we propose a coordinated load balancing strategy on skewed data streams (referred to as St-Stream), which is a two-tier hierarchical system for handling data streams. The proposed strategy is characterized by performing a migration pairing strategy for resources at the task allocation stage by cutting and moving out the tasks of high-load nodes in a hierarchical manner, and the moved-out operators are placed in the routing table, and the routing table operators are moved out to these nodes sequentially according to the tasks required by low-load nodes. We further design a two-tier coordination scheme for the resource allocation problem, which can adjust the skewed load from within the nodes and then dynamically restore the balance between the nodes. We implemented St-Stream on Apache Storm, which achieves a 21% coordination in processing CPU utilization, a 17.6% reduction in latency, and a 0.3 improvement in load balance recovery compared to the baseline design. Our experimental results demonstrate that the proposed load balancing strategy better balances the cluster load and improves the performance of the stream processing system. [ABSTRACT FROM AUTHOR]

Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics. [ABSTRACT FROM AUTHOR]

These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]

In view of the problems that current load balancing algorithm affects system efficiency under low load and poor distribution efficiency under high load, based on Nginx server, a dynamic and static load balancing algorithm based on improved genetic algorithm is proposed in this study. The algorithm chooses to use server performance parameters based on CPU performance, memory performance, disk I/O and network bandwidth as server node performance evaluation indexes and static weighted polling algorithm weights under low load, and designs a dynamic load balancing algorithm under high load based on the change of node performance utilization rate as a percentage of the cluster average load utilization rate by introducing operation conversion thresholds and dynamic. By introducing the improved genetic algorithm of operation transition threshold and dynamic triangular function operation probability as the threshold calculation method, the transformation of static algorithm dominant area into dynamic algorithm dominant area is calculated. This study designs comparison experiments to verify that the proposed algorithm has better load balancing effect when compared with weighted polling algorithm, probabilistic meritocracy algorithm and dnfs_conn algorithm in the experimental environment, and has about 15% improvement in the values of average response time and actual concurrent connections when compared with dnfs_conn algorithm. [ABSTRACT FROM AUTHOR]

The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural sector is witnessing improvements in its crop yields and overall efficiency. This paper presents the design and performance analysis of a machine learning (ML) model for agricultural applications involving acoustic sensing. This model is integrated into an efficient Artificial Intelligence of Things (AIoT) platform tailored for agriculture. The model is then used in the design of a communication network architecture and for determining the distribution of the computing load between edge devices and the cloud. The study focuses on the design, analysis, and optimization of AI deployment for reliable classification models in agricultural applications. Both the architectural level and hardware implementation are taken into consideration when designing the radio module and computing unit. Additionally, the study encompasses the design and performance analysis of the hardware used to implement the sensor node specifically developed for sound classification in agricultural applications. The novelty of this work lies in the optimization of the integrated sensor node, which combines the proposed ML model and wireless network, resulting in an agricultural-specific AIoT platform. This co-design enables significant improvements in the performance and efficiency for acoustic and ambient sensing applications. [ABSTRACT FROM AUTHOR]

Cloud computing is the computation of the future in the present situation. Cloud computing makes resources available to clients on demand. Uneven task assignment to processors causes imbalance, i.e., some processors are loaded while others are loading control. Load balancing's purpose is to transparently shift the load from an overloaded process to a process that is loaded. The Critical issue in cloud computing is load balancing. Workloads must be transferred between nodes in the cloud network to achieve high performance, a short response time, and a resource utilization ratio. In the following parts, we will discuss loadbalancing approaches and the suggested work of our load-balancing system. Utilizing the Cloudsim simulator, performance is examined. Simulation results show that the suggested method evenly balances the workload among all accessible virtual machines without under or overusing them. [ABSTRACT FROM AUTHOR]

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method. [ABSTRACT FROM AUTHOR]

Summary: Recently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively. [ABSTRACT FROM AUTHOR]

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than that in terrestrial over-the-air wireless sensor networks. To achieve reliable and energy-efficient data gathering in dynamic WUSNs, this article proposes an optimal transmission policy, where path loss of sensory data transmission, energy constraint, and network load balancing are the factors to be considered. Specifically, we capture the effect of underground environments on wireless communications, and evaluate path probability, energy consumption, and load balancing factor with respect to reliability and efficiency of transmission paths. The transmission topology can be reduced to a multi-objective and multi-constrained optimization problem and solved through an improved maximum flow minimum cost algorithm. By using reinforcement learning, we derive an adaptive transmission policy for underground sensors to efficiently use their energy and avoid transmitting sensory data in unreliable paths under a dynamic environment. Through simulations and comparison upon publicly available real data, our technique achieves more reliable wireless communication with significant reduction of packet loss, and enables more energy-efficient data gathering than other techniques, especially when soil moisture varies frequently. [ABSTRACT FROM AUTHOR]

With the increase in the number of vehicles and intelligent applications in the IoV, computing intensive tasks have proliferated, and the traditional architecture is difficult to meet user needs. To solve the problems of insufficient and uneven allocation of computing resources in the IoV, insatiable application delay requirements, and high task energy consumption costs, this paper combined MEC and SDN to design an efficient task offloading scheme in the network of vehicles architecture from macro station to MEC server to vehicle, and proposed an improved low-complexity non-dominated sorting genetic algorithm to optimize the task offloading cost and the load balancing rate of MEC server. The experimental simulation results show that the proposed scheme compared with random offloading, NO-MEC offloading, NO-I offloading, traditional NSGA,NSGA-Ⅱ off loading, GA offloading, Q-learning offloading, DQN offloading, has lower offloading cost, better load balancing rate, and approximately the highest system utility, which brings better network services to vehicle users in the IoV. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic. [ABSTRACT FROM AUTHOR]

Stream processing systems are widely used to process large amounts of data generated by applications in real time due to their advantages in latency and throughput. In most streaming applications, the system requires a comprehensive analysis of data from multiple data sources, so stream joins are the basis of stream processing systems. Similar to other big data problems, stream joins suffer from load imbalance, where a few nodes responsible for handling most of the load can become bottlenecks, thereby increasing latency and reducing throughput. Therefore, how to obtain a good load-balancing effect with low overhead is a critical issue in designing stream join systems. To solve this problem, we propose an adaptive non-migrating load-balancing method, which is mainly oriented to the stream window join problem. Considering that the completeness of the stream join results during the splitting of state to multiple downstream instances can be guaranteed by replicating the input tuples into multiple replicas and sending them to those downstream instances, our method can control the replication and forwarding of input tuples by setting up routing tables, and then when the system becomes unbalanced, our method can change the load distribution of the system by directly changing the partitioning of the tuples arriving later instead of state migration, and thus achieving load balancing with very low overhead. Based on our method, we develop a distributed stream window join system, NM-Join, which is built on Flink. We theoretically analyze the completeness and effectiveness of our method and provide extensive experimental evaluations of NM-Join in terms of load-balancing effect, latency, and throughput. Experimental results show that our method is able to perform load balancing with very low additional overhead, and thus outperforms existing load-balancing methods in terms of latency and throughput. [ABSTRACT FROM AUTHOR]

Quantum key distribution (QKD) has great potential in ensuring data security. Deploying QKD-related devices in existing optical fiber networks is a cost-effective way to practically implement QKD. However, QKD optical networks (QKDON) have a low quantum key generation rate and limited wavelength channels for data transmission. The simultaneous arrival of multiple QKD services may also lead to wavelength conflicts in QKDON. Therefore, we propose a resource-adaptive routing scheme (RAWC) with wavelength conflicts to achieve load balancing and efficient utilization of network resources. Focusing on the impact of link load and resource competition, this scheme dynamically adjusts the link weights and introduces the wavelength conflict degree. Simulation results indicate that the RAWC algorithm is an effective approach to solving the wavelength conflict problem. Compared with the benchmark algorithms, the RAWC algorithm can improve service request success rate (SR) by up to 30%. [ABSTRACT FROM AUTHOR]

Cloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively. [ABSTRACT FROM AUTHOR]

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

The advent of the Big Data era has brought considerable challenges to storing and managing massive data. Moreover, distributed storage systems are critical to the pressure and storage capacity costs. The Ceph cloud storage system only selects data storage nodes based on node storage capacity. This node selection method results in load imbalance and limited storage scenarios in heterogeneous storage systems. Therefore, we add node heterogeneity, network state, and node load as performance weights to the CRUSH algorithm and optimize the performance of the Ceph system by improving load balancing. We designed a cloud storage system model based on Software Defined Network (SDN) technology. This system model can avoid the tedious configuration and significant measurement overhead required to obtain network status in traditional network architecture. Then we propose adaptive read and write optimization algorithms based on SDN technology. The Object Storage Device (OSD) is initially classified based on the Node Heterogeneous Resource Classification Strategy. Then the SDN technology is used to obtain network and load conditions in real-time and an OSD performance prediction model is built to obtain weights for performance impact factors. Finally, a mathematical model is proposed for multi-attribute decision making in conjunction with the OSD state and its prediction model. Furthermore, this model is addressed to optimize read and write performance adaptively. Compared with the original Ceph system, TOPSIS_PA improves the performance of reading operations by 36%; TOPSIS_CW and TOPSIS_PACW algorithms improve the elastic read performance by 23 to 60% and 36 to 85%, and the elastic write performance by 180 to 468% and 188 to 611%, respectively. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) wireless communication networks are expected to support heterogeneous services and decentralized infrastructure with resource-aware smart self-organization for Internet of Things (IoT) applications. Large-scale IoT applications face challenges like load balancing and scalability within the network due to the inherent vulnerability of ad-hoc structures. This paper proposes a multiHop constant-time complexity clustering algorithm (MultiHopFast) for IoT networks to address these challenges. The proposed MultiHopFast algorithm reduces the computing burden from IoT nodes with smart load balancing to ensure IoT network scalability. The algorithm addresses the network load, scalability, and time efficiency challenges. Using neighbourhood heuristics, the MultiHopFast algorithm builds appropriate size (i.e., up to 5 hops) of clusters with participating IoT nodes. Each cluster is associated with a cluster head (CH) (or a coordinator). The MultiHopFast algorithm probabilistically selects CH for each cluster. When compared with state-of-the-art counterparts, MultiHopFast algorithm: (i) operates with constant-time complexity in a large scale network as well as in small-scale networks, (ii) runs without any impact on network scalability, and (iii) creates 12% fewer CHs to save precious resources such as energy. Better use of heuristics and resource-aware self-organization, constant-time computational complexity, and network operation with fewer CHs demonstrate that the performance of the MultiHopFast algorithm surpasses the compared algorithms in the literature. The MultiHopFast algorithm is envisioned as a better candidate to match the standard and expectations set by the 6G wireless communications. [ABSTRACT FROM AUTHOR]

In today's business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods. [ABSTRACT FROM AUTHOR]

Cloud computing improves the performance of software applications by providing on-demand usage, high availability, reliability, and agility. However, during peak traffic conditions the resources in cloud services can become over-utilized, impairing the ability to provide performance levels specified in service-level agreements. Therefore, a load balancing algorithm that provides an efficient and fair allocation of cloud resources while providing high availability to end users is a timely necessity. In this paper, we propose a load balancing scheme to distribute the workload among virtual servers using a modified version of the double Q-learning algorithm. The proposed algorithm is implemented on a load balancing controller and leverages user requests using software defined network technologies. The results reveal a considerable reduction in terms of unsatisfied cloud consumers compared to already existing popular algorithms. In short, this work will serve as a future guide for load balancing implementations in cloud environments that require higher Quality of Service. [ABSTRACT FROM AUTHOR]

The main parameters characterizing the process of deformation of solids are displacements, strain and stress tensors. From the point of view of the strength and reliability of the structure and its elements, researchers and engineers are mainly interested in the distribution of stresses in the objects under study. Unfortunately, all boundary value problems are formulated and solved in solid mechanics mainly with respect to displacements, or an additional stress functions. And the required stresses are calculated from known displacements or stress functions. In this case, the accuracy of stress calculation is strongly affected by the error of numerical differentiation, as well as the approximation order of the boundary conditions. The formulation of boundary value problems directly with respect to stresses or strains allows to increase the accuracy of stress calculation by bypassing the process of numerical differentiation. Therefore, the present work is devoted to the formulation and numerical solution of boundary value problems directly with respect to stresses and strains. Using the well-known Beltrami-Miechell equation, and considering the equilibrium equation as ah additional boundary condition, a boundary value problem (BVP) is formulated directly with respect to stresses. In a similar way, using the strain compatibility condition, the Beltrami-Mitchell type equations for strains are written. The finite difference equations for two-dimensional BVP are constructed and written in convenient a form for the use of iterative method. A number of problems on the equilibrium of a rectangular plate under the action of various loads applied on opposite sides are numerically solved. The reliability of the results is ensured by comparing the numerical results of the 2D elasticity problems in stresses and strains, and with the exact solution, as well as with the known solutions of the plate tension problem under parabolic and uniformly distributed loads. [ABSTRACT FROM AUTHOR]

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path. [ABSTRACT FROM AUTHOR]

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration. • Agents in coalitions progressively balance data center sections. • Supported by a game-theoretical framework, agents cooperate to balance workloads. • Coalitions composed of nearby hosts are used as a basis to balance workloads. • Coalition-aware heuristics consider both the benefit of migrating a VM and its cost. [ABSTRACT FROM AUTHOR]

Summary: Since the development of Wireless Sensor Networks (WSNs), the limited battery of the sensor nodes has been an unavoidable concern. Hence, to keep the WSNs operational for a longer possible duration, the recharging of node's battery through harvesting the ambient energy from surroundings (for an example, solar energy) has been proposed. In this work, we focus not only on utilizing the energy harvesting (EH)‐enabled sensor nodes for routing purposes but also introduce a novel hybrid optimization ROATSA that uses Remora Optimization Algorithm (ROA) and Tunicate Swarm Algorithm (TSA) for energy‐efficient cluster‐based routing. The proposed work is termed as ROA and TSA‐based Energy‐Efficient Cluster‐based Routing for EH‐enabled WSN (ROTEE). Hybrid ROATSA is chosen due to enhanced convergence and exploitation capabilities. To reduce the financial burden on the network, we use only four EH‐enabled nodes and locate them at each periphery of the network, equidistant to each other and the other nodes are 3‐level energy heterogeneous sensor nodes. The selection of cluster head (CH) is optimized through ROATSA by considering profile index of each node by evaluating them at energy, distance, load balancing, node density, the delay involved, and network's average energy. The proposed work ROTEE shows supreme performance against the recently proposed clustering techniques. [ABSTRACT FROM AUTHOR]

In the current internet of things era, various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause cloud core overload and significant network delays. To overcome these overload and delay problems, the mobile edge computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, while using the service, it may also encounter a large amount of popular data being accessed in a short period of time. Without proper handling of replica generation and deployment, even in low-latency environments, it can still kill the benefits of MEC due to increased access time. Although many scholars have proposed related issues for copy replication, there are still.parts that can be improved. To avoid the situation of insufficient availability of replicas, replica replication is performed, but infinite replicas may lead to a significant increase in traffic and waste of resources. And when deploying replicas, it is necessary to avoid placing them on congested nodes and consider how to achieve better load balancing. To improve the above problems, we synthesize the advantages of previous algorithms, make up for the shortcomings, and propose an adaptive replica configuration mechanism to predict the popularity of files and replicate replicas to low-blocking nodes. This method spreads the subsequent access workload by copying the popular file in advance to improve the system's overall performance. [ABSTRACT FROM AUTHOR]

Motivated by the exploding growth of web-based services and the importance of efficiently managing the computational resources of such systems, we introduce and study a theoretical model for load balancing of very large databases such as commercial search engines. Our model is a more realistic version of the well-received balls-into-bins model with an additional constraint that limits the number of servers that carry each piece of the data. This additional constraint is necessary when, on one hand, the data is so large that we can not copy the whole data on each server. On the other hand, the query response time is so limited that we can not ignore the fact that the number of queries for each piece of the data changes over time, and hence we can not simply split the data over different machines. In this paper, we develop an almost optimal load balancing algorithm that works given an estimate of the load of each piece of the data. Our algorithm is almost perfectly robust to wrong estimates, to the extent that even when all of the loads are adversarially chosen the performance of our algorithm is 1 - 1/e, which is provably optimal. Along the way, we develop various techniques for analyzing the balls-into-bins process under certain correlations and build a novel connection with the multiplicative weights update scheme. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks' energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas' priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes' residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms. [ABSTRACT FROM AUTHOR]