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Vehicular Ad-hoc Networks (VANETs) have high requirements for real-time data processing and security of message authentication. In order to solve the computing power asymmetry between vehicles and road side units (RSUs) in VANETs under high-density traffic, accelerate the processing speed of message authentication, and solve the problems of high computational overhead and long message authentication time caused by the use of bilinear pairing encryption technology in similar message-batch-authentication schemes, we propose introducing the concept of edge computing (EC) into VANETs and using idle nodes' resources to assist the RSU in quickly authenticating messages to achieve computing power load balancing under multiple traffic flows. We propose introducing the idea of edge computing (EC) into VANETs and using idle nodes' resources to assist RSUs in quickly authenticating messages. This scheme performs two identity-based message authentications based on the identity signature constructed by elliptic curve cryptography (ECC). One of them is the batch authentication of the vehicle sending messages by the RSU-authenticated vehicles with free resources, as temporary edge computing nodes (TENs), and the other is the authentication of the temporary TEN messages by the fixed-edge-node RSUs. The resources of the TEN are used to reduce the computational burden of RSUs and message authentication time, thereby improving the efficiency of system authentication of messages. We performed a security analysis of the scheme to prove its security properties and compared it with other schemes in terms of performance. The experimental results show that our scheme has a transmission overhead of 2400 bytes when there are four TENs, and the number of verification message requests reaches 20, which outperforms other methods. The gap will be more evident as the numbers of TEN and message verification requests increase. [ABSTRACT FROM AUTHOR]

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2 × 2 , 3 × 3 , and 4 × 4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3 × 3 and 4 × 4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time. [ABSTRACT FROM AUTHOR]

• We propose a new optimal automatic load balancing criterion for parallel iterative applications. • We introduce an algorithm for finding the optimal time at which dynamic load balancing should be triggered. • Our criterion outperforms state-of-the-art load balancing criteria by up to 17.6%, while being, on average, 4.9% faster. • We show that state-of-the-art automatic load balancing criteria are at most 42% slower than the optimum. Parallel iterative applications often suffer from load imbalance, one of the most critical performance degradation factors. Hence, load balancing techniques are used to distribute the workload evenly to maximize performance. A key challenge is to know when to use load balancing techniques. In general, this is done through load balancing criteria, which trigger load balancing based on runtime application data and/or user-defined information. In the first part of this paper, we introduce a novel, automatic load balancing criterion derived from a simple mathematical model. In the second part, we propose a branch-and-bound algorithm to find the load balancing iterations that lead to the optimal application performance. This algorithm finds the optimal load balancing scenario in polynomial time while, to the best of our knowledge, it has never been addressed in less than an exponential time. Finally, we compare the performance of the scenarios produced by state-of-the-art load balancing criteria relative to the optimal load balancing scenario in synthetic benchmarks and parallel N-body simulations. In the synthetic benchmarks, we observe that the proposed criterion outperforms the other automatic criteria. In the numerical experiments, we show that our new criterion is, on average, 4.9% faster than state-of-the-art load balancing criteria and can outperform them by up to 17.6%. Moreover, we see in the numerical study that the state-of-the-art automatic criteria are at worst 26.43% slower than the optimum and at best 10% slower. [ABSTRACT FROM AUTHOR]

With the rapid increase of compute intensive tasks and workload, the need of the hour is to manage the total workload due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in federated cloudlet environment, load balancing is the main challenge. Load balancing means to divide the total workload between all the member nodes to get the maximum benefits from the federated environment. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible to the mobile devices in the vicinity. Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various member cloudlets. Different tasks consume different amount of energy for their execution that results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat, the more performance degradation, so it becomes very vital to keep the energy consumption as minimum as possible. To address this problem, this paper proposes a novel scheduling strategy that will distribute the total load between all the member nodes in a federated cloudlet environment for load balancing. The proposed methodology not only considers the energy level of the system on which the task has to be executed but also considers the energy required by the tasks in queue. The proposed methodology is tested in a Cloudlet Federation environment and the results show an improved load balancing in terms of energy consumption. [ABSTRACT FROM AUTHOR]

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities. [ABSTRACT FROM AUTHOR]

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, "available" and "free." To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm's performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm's performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed "component-based throttled load balancing algorithm" is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. [ABSTRACT FROM AUTHOR]

The symmetric wireless network has been expected to be a revolutionary technology for mobile communications. Due to the limited resources of the microbase stations in the wireless network, the way to jointly optimize resource allocation, traffic throughput, latency, and other key performances is a hot research issue. In this paper, we introduce a joint optimization algorithm for improving the performance and balancing the traffic load of the wireless network. For the optimal traffic routing scheme, we transfer the problem to a mixed mathematical programming model. The model contains multiple traffic constraints and a single joint objective; the objective of the joint optimization are data transmission latency, energy consumption of wireless microbase stations, and throughput of links. Moreover, in order to approximately solve the optimization problem, we propose an efficient heuristic traffic transmission and migration scheme with load balancing, called an adaptive data traffic control scheme. The main idea of the proposed scheme is to split the traffic of overloaded microbase stations and links in the symmetric wireless network, so as to achieve load balancing and reduce the energy consumption of microbase stations. At last, the evaluations and simulations verify the proposed algorithm can efficiently optimize the energy allocation of microbase stations, and the network lifetime is increased to 210 rounds. Meanwhile, the network latency is reduced to 2–3 ms, and the network throughput is increased to 1000 Mb in our simulation environment. The constructed traffic control system for the traffic engineering-based wireless network in this paper can serve the intelligent system in the future. [ABSTRACT FROM AUTHOR]

Edge cloud is used to handle latency-sensitive services. However, due to the large number of concurrent requests for edge intensive tasks, the resource allocation strategy affects the stability of nodes. In addition to an adaptive resource allocation model based on chaotic hierarchical gene replication (CRPSO model), the concept of chaotic replication ratio is proposed. This study is divided into two parts. The first is to verify the algorithm verification of the simulation platform. By comparison, it is found that CRPSO reduces the CPU and bandwidth utilization by 43.7% and 62.7% on average, respectively, and the memory usage is also lower than other algorithms. Thereafter, we compared the CRPSO algorithm with the Kubernetes clustering algorithm. Experiments showed that the fitness of the CRPSO model is 33.7% higher than that of the comparison algorithm on average. The algorithm is superior to the cluster scheduling algorithm in terms of CPU utilization and memory utilization. Furthermore, the total variance of the two resources involved in this model improved significantly, reaching 69.8% on average. In addition, CRPSO also has great advantages in other aspects of CPU and memory. It is indicated that the model in this study is suitable for the scenario of edge large-scale requests. [ABSTRACT FROM AUTHOR]

Cloud computing provides computing resources on demand of users without their direct management. In this cloud paradigm, scheduling the tasks and allocating the resources become major aspect for cloud infrastructure as a service (IaaS). There are more existing algorithms and techniques suggested for task allocation problem. Still there is challenging research on efficient scheduling. To address this issue, many researches are in progress and all of them having their own drawbacks. In this paper, we are proposing a queue-based scheduling strategy with load balancing called as IQSLB and an extended IQSLB also proposed for dealing with critical situations. The proposed strategy calculates the placement value of the tasks in queue with the current status of the virtual machine (VM) in cluster and reshuffles the task accordingly. The extended IQSLB deals with handling deadlock situation where VM cannot adopt task for execution and task will be reshuffled with another task in another queue. The proposed strategy is compared with few existing systems, and the performance evaluation proves that IQSLB schedules tasks more efficiently than other systems. Our proposed IQSLB takes 75 s to allocate 1000 tasks by using 55 virtual machines which is much lesser than existing techniques. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high energy consumption caused by node overload in complex network flow, a simulation load separation control algorithm based on complex network flow is proposed. According to the characteristics of complex network flow, combined with the characteristics of traffic in the network, a tree combined classifier is designed to discretize the complex network, analyze the micro dynamics of nodes in the network, and simulate the load division of nodes in complex network flow by dividing simulation load, evaluating node bandwidth, transferring overload nodes, and controlling the tree combined classifier. The experimental results show that the designed discrete control algorithm has the advantages of low cost, good load balancing, low energy consumption, and good simulation load discrete control performance. [ABSTRACT FROM AUTHOR]

In recent years, the use of edge computing to solve the problem of limited resources in the IoV has attracted more and more attention. Vehicles can upload tasks to the edge servers within their radio range for computing cooperation and offloading. However, due to shared resources among edge servers, the uneven distribution of vehicles may lead to many problems such as uneven tasks distribution, unbalanced load, and low computing efficiency on different edge servers. On the other hand, most of the existing works are proposed to address the load unbalance issue of edge servers with the help of remote cloud or vehicle cloud, but still leading to resource wasting problems especially for edge servers with a light load. To address this issue, a task transfer scheme has been proposed among different edge servers in this paper. First, we designed a partitioned and hierarchical software-defined network architecture for IoV. After that, a load balancing model is proposed under this network architecture, in which the global controller can manage and schedule the local tasks from each edge server. Next, we proved that the task allocation problem for our load balancing model under the proposed network architecture is an NP-hard problem. To solve this problem, a Deep Q-Network (DQN) model is proposed to minimize the mean square deviation of loads among different edge servers. Numerical results show that our proposed load balancing model can significantly improve the resource utilization of edge servers as well as reduce the computational latency of vehicular tasks. [ABSTRACT FROM AUTHOR]

Effectively balancing traffic in datacenter networks is a crucial operational goal. Most existing load balancing approaches are handcrafted to the structure of the network and/or network workloads. Thus, new load balancing strategies are required if the underlying network conditions change, e.g., due to hard or grey failures, network topology evolution, or workload shifts. While we can theoretically derive the optimal load balancing strategy by solving an optimization problem given certain traffic and topology conditions, these problems take too much time to solve and makes the derived solution stale to deploy. In this paper, we describe a load balancing scheme Learned Load Balancing (LLB), which is a general approach to finding an optimal load balancing strategy for a given network topology and workload, and is fast enough in practice to deploy the inferred strategies. LLB uses deep supervised learning techniques to learn how to handle different traffic patterns and topology changes, and adapts to any failures in the underlying network. LLB leverages emerging trends in network telemetry, programmable switching, and "smart" NICs. Our experiments show that LLB performs well under failures and can be expanded to more complex, multi-layered network topologies. We also prototype neural network inference on smartNICs to demonstrate the workability of LLB. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

We developed a technology that quickly shares information found by a connected vehicle with other vehicles. For example, when a connected vehicle finds an obstacle on the road, the technology can transmit information about it quickly to other vehicles. This quick notification is achieved by offloading part of the processing of the collected obstacle information to network-edge nodes and transmitting interim results to vehicles near the obstacle. However, in urban areas, the delay in notification may become large because a large number of vehicles connect to a small number of network-edge nodes; thus, the volume of data received from these vehicles may overwhelm the computing resources of these nodes. To solve this problem, we also developed a technology that dynamically selects which computer will execute any particular notification processing on the basis of the states of the connected vehicles. Using this technology, obstacle information can be transmitted quickly even when a large number of vehicles are connected to a small number of network-edge nodes. [ABSTRACT FROM AUTHOR]

Dynamic maps are being constructed to support automated driving and advanced navigation. They combine high-precision map information with traffic-related information, such as that about traffic controls and jams. We at NTT believe that detecting lane-specific traffic jams, such as those caused by a queue of vehicles waiting to enter the parking lot of a commercial facility or by vehicles parked on the street, and providing information on these jams will enable unprecedentedly advanced navigation. This article describes a technology for detecting lane-specific traffic jams on the basis of information that can be collected from connected vehicles. [ABSTRACT FROM AUTHOR]

As the most popular anonymous communication system, Tor provides anonymous protection for users by sending their messages through a series of relays. Due to the use of the bandwidth-weighted path selection algorithm, many more users choose routers with high bandwidth as relays. This will cause the utilization of high bandwidth routers to be much higher than that of low bandwidth routers, which will bring congestion risk. The Quality of Service (QoS) is difficult to guarantee for users who need delay-sensitive services such as web browsing and instant messaging. To reduce the average load of routers and improve the network throughput, we propose a circuit construction method with multiple parallel middle relays and conduct a dynamic load allocation method. The experiment demonstrates that our proposed method can provide better load balancing. Compared with other multipath anonymous communication networks, our proposed method can provide better anonymity. [ABSTRACT FROM AUTHOR]

The article presents a study which assessed the effectiveness of a modified live load model in conducting a reliability analysis of typical highway bridges in Manitoba and comply with the American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LFRD), the Modified HSS-25. The analysis was conducted in three types of supported structures, including precast prestressed concrete box girder and steel girder.

This paper proposes a new normalized Huber adaptive control algorithm for shunt compensation. A 5-level cascaded multilevel inverter (CHB-MLI) is controlled as a shunt active power filter (SAPF) unit for a medium voltage distribution system. The proposed algorithm is designed, modelled, and tested to control the SAPF unit under normal and distorted grid conditions. It is employed to extract the fundamental active power component of load current. This extracted effective component is used to generate source reference currents by multiplying with the unit vector template. Furthermore, the switching of CHB-MLI is performed using phase-shifted PWM scheme. The algorithm helps to mitigate the reactive power and unwanted harmonics generated by the nonlinear load. A single-phase scale-down prototype model is developed for experimental investigation and testing purpose in the laboratory. The associated voltage and current sensors and control circuits are controlled by the dSpace 1104 digital microprocessor. Simulation and experimental results show good performance of the proposed algorithm in steady-state and dynamic load conditions. A fair performance comparison is also done with other conventional adaptive algorithms. [ABSTRACT FROM AUTHOR]

Different experiment has been advertised that the processor work load distributing equitably with the processors of a distributed system decidedly enhance framework execution and improves system management. Fuzzy logic has been implemented in numerous areas of industry and science to manage susceptibility. Proposed work with the intent of load balancing has been focused on using fuzzy logic to interpret processor's load and task execution length. This work introduces a new dynamic fuzzy-based load balancing algorithm for homogeneous dispersed frameworks. The proposed techniques use fuzzy logic to manage improper data load i.e., overloaded and under loaded, deciding on load distribution choices and preserve general framework strength. For accurately evaluating the load status of a host, proposed algorithm uses CPU utilization, CPU queue length and distance upon its present load as linguistic inputs while framing fuzzy set. Method proposes Hybrid Genetic Algorithm (HGA) that is blended with stochastic development process in order to designate and schedule real-time tasks with priority requirements. The work randomly generates the tasks using random wheel approach, once the tasks are generated then encoding tasks to chromosome is carried out. Height of each task is obtained through DAG and according to the root node, the height of each taskis updated in the chromosome. Proposed fuzzylogicbased load balancing and hybrid genetic algorithm based task scheduling (FLLBHGATS) algorithm has been evaluated with similar existing methods in order to prove its efficiency. The results prove that FLLBHGATS performs better than other techniques as far as the solution quality. [ABSTRACT FROM AUTHOR]

As a key enabler of the access traffic steering, switching and splitting (ATSSS) feature, multipath transport can leverage the simultaneous use of several network paths and support seamless failover to improve both communication throughput and resilience. Therefore, a traffic scheduling strategy is necessary to determine the best network path combination that may improve the performance of multipath transport. To address this need, we developed a multiservice-type based transmission (MSTT) traffic scheduling optimization strategy, which involves three steps. First, the user equipment (UE) selects the number of data stream transmission paths, considering the service utility function, and either transmits all data streams via the 3GPP network or sends two streams, one via the 3GPP network and the other via the non-3GPP network. Second, the proposed method is used to select the transmission path for each data stream based on load balancing. Finally, an algorithm for optimizing traffic scheduling is formulated by applying the convex optimization problem to maximize the effective network capacity under a Delay Quality of Service (DQoS) constraint. The proposed traffic scheduling strategy is validated through simulation experiments. The results indicate that user satisfaction and effective capacity realized are always better than when using the always-best-connected and fixed-ratio power-allocation algorithms. [ABSTRACT FROM AUTHOR]

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s. [ABSTRACT FROM AUTHOR]

The central network architecture takes the information content as the processing object and can better meet the user access needs. However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture. Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network. Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system. By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms. In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given. Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness. Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds. [ABSTRACT FROM AUTHOR]

Summary: The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption). [ABSTRACT FROM AUTHOR]

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree. [ABSTRACT FROM AUTHOR]

Cloud Computing is A Remarkable technology in the IT field Various services are provided by the cloud computing over the internet. According to the usage computing resource services IT charges in the cloud Yahoo, Amazon, Google are some of the cloud used organisations which are having number of users. Cloud Computing is an internet based computing approach. So there is a sharing of applications resources and software in between the computing devices. Mentioning of loads in cloud environment is most challenging issue. Balancing of float in the main intention in making of visual machine balance which not be overload or underload. An efficient and its dynamic load balancing weighted Round Robin algorithm for virtual machine in cloud computing is presented in this paper. Task or uses the modified algorithm of inspired Honey Bee behaviour and for balancing the workload on virtual machine uses the modified Round Robin Algorithm in the cloud system. By using this energy system performance is improved minimum completion time can be achieved and better resources are utilized. The efficiency of this load balancing method is better than the previous methods. [ABSTRACT FROM AUTHOR]

This study is to explore the smart city information (SCI) processing technology based on the Internet of Things (IoT) and cloud computing, promoting the construction of smart cities in the direction of effective sharing and interconnection. In this study, a SCI system is constructed based on the information islands in the smart construction of various fields in smart cities. The smart environment monitoring, smart transportation, and smart epidemic prevention at the application layer of the SCI system are designed separately. A multi-objective optimization algorithm for cloud computing virtual machine resource allocation method (CC-VMRA method) is proposed, and the application of the IoT and cloud computing technology in the smart city information system is further analysed and simulated for the performance verification. The results show that the multi-objective optimization algorithm in the CC-VMRA method can greatly reduce the number of physical servers in the SCI system (less than 20), and the variance is not higher than 0.0024, which can enable the server cluster to achieve better load balancing effects. In addition, the packet loss rate of the Zigbee protocol used by the IoT gateway in the SCI system is far below the 0.1% indicator, and the delay is less than 10 ms. Therefore, the SCI system constructed by this study shows low latency and high utilization rate, which can provide experimental reference for the later construction of smart city. [ABSTRACT FROM AUTHOR]

Traditional load balancing algorithms can't meet the demand of high concurrent requests and quick calling of public network digital trunk. This paper proposed a dynamic load balancing algorithm based on load feedback for distributed digital trunk to achieve load balancing and increase user capacity. Firstly, this paper established monitoring method and monitoring indexes for static and dynamic load of participating MCPTT server. Then, it used weighted round robin algorithm to allocate participating MCPTT server to users and got complex load parameters by dealing user requests. Finally, it updated the weight of participating MCPTT server based on load feedback to dynamically adjust server load. Simulation results show that the proposed algorithm has better load balancing effect, smaller load balancing degree and lower response delay compared with traditional algorithms and other dynamic feedback algorithms. [ABSTRACT FROM AUTHOR]

Balancing network traffic among base stations poses a primary challenge for mobile operators because of the escalating demand for enhanced data speeds in large-scale 5G radio applications. Within cellular networks, traffic flow prediction constitutes a pivotal issue in numerous applications, such as resource allocation, load balancing, and network slicing. In this paper, traffic prediction based load balancing framework with reinforcement learning is proposed to optimize neighbor cell relational parameters that can better balance traffic within a defined geographical cluster. Spatial–temporal-event cross attention graph convolution neural network (STECA-GCN) is put forward to predict the precise traffic flow. The model takes event dimension features into account, while also incorporating direct cross-fusion among diverse features. Concurrently, we have developed a strategy based on deep reinforcement learning to facilitate dynamic load balancing decisions. Simulation results show that our proposed load balancing framework can improve overall system performance. In particular, the combination of loading rate and energy efficiency can achieve a 12% improvement. The load balancing of the base station can better deal with social emergencies. [Display omitted] • Cross-fusion of different features and adding event domain features can enhance the precision of traffic forecasting. • Incorporating traffic prediction prior to the implementation of load balancing facilitates proactive resource planning. • Clustering to obtain load balancing cells and deep reinforcement learning for dynamic load balancing. • Simulation findings demonstrate that the methodologies yield enhancements in loading rates and energy efficiency. [ABSTRACT FROM AUTHOR]

With the advancement of the 5G networks, edge computing (EC) assisted Internet of Things (IoT) based applications demand real-time computation and high-volume data-intensive services. Due to the heterogeneity and limited resources of the edge nodes (ENs), and dynamic resource demand of the IoT applications, it is challenging to place the IoT services into the available ENs by ensuring performance measurements on quality of services (QoS). In this paper, a novel quantum-inspired particle swarm optimization-based service placement (QPSO-SP) algorithm is proposed for EC environment. The QPSO-SP is intended to achieve desired service placement while optimizing throughput, energy consumption, delay, and computation load of the system. Quantum particle (QP) is designed to represent a complete solution for IoT service placement in an EC environment. Decoding of the QP is done by using a novel double-hashing technique. The fitness function uses throughput, delay, energy consumption, and load balancing parameters. Extensive simulation is performed and comparison is done with the standard existing algorithms. The parametric study, Taguchi method is conducted. The statistical analysis, ANOVA, followed by Friedman test is also done. The simulation results indicate that the proposed QPSO-SP outperforms existing works in terms of energy consumption, delay, throughput, and load balancing. • A Quantum-Inspired PSO is proposed to address IoT service placement. • Quantum particle is designed to represent complete IoT service placement solution. • The decoding of quantum particle is done using a novel double-hashing technique. • Fitness function considers throughput, energy consumption, delay and load balancing. • Simulation shows efficacy of the proposed work with statistical analysis. [ABSTRACT FROM AUTHOR]

This paper considers the locally and globally optimal solutions of the global optimisation problem whose objective function is a max‐plus vector‐valued function and constraint function is a real affine function. The formulas about global optimisation in the existing work are further explained and simplified. The necessary and sufficient conditions for the existence and uniqueness of locally optimal solutions of a single objective are established, which are then used to deduce these of globally optimal solutions. Furthermore, the general formulas of locally and globally optimal solutions are presented, respectively. The local optimisation is then applied to solve the load distribution problem of distributed systems with no globally optimal solution, and the optimal allocation scheme is proposed to complete the overall task at the earliest time. The proposed method is constructive, and the obtained results are illustrated by the numerical examples. [ABSTRACT FROM AUTHOR]

Summary: In Fifth Generation (5G) Heterogeneous Mobile Networks (HetNets), deploying dense small cell networks makes user association more challenging. The process of collecting cell load information from the User Equipments (UEs) and broadcasting the feedback message involves significant overhead and time complexity. Moreover, the UEs may not know the optimum cell to reselect, satisfying its data rate requirements. In order to overcome these drawbacks, in this paper, we propose to design an Hierarchical and Hybrid Cell Load Balancing (HHCLB) technique using Selective Handoff. In this technique, the UEs of each cell are grouped into clusters depending on their proximity distance. Each cluster contains a cluster controller (CC) which is in charge of determining the intra‐cell load and redirecting the cell‐reselection request of a UE. If the data rate of any UE in a cluster becomes less than its required rate, then the cell reselection process is performed. By simulation results, it is shown that load balancing can be done proactively (implicitly) by the CCs when the load is unbalanced or can be done on demand (explicitly) when a UE send a request for cell reselection. In the case of Macro cells, HHCLB attains 71% higher throughput for low load scenario and 59% higher throughput for high load scenario. Similarly, in the case of Femto cells, HHCLB attains 19% higher throughput for low load scenario and 27% higher throughput for high load scenario. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]

Data center networks typically adopt multi-rooted tree topologies to provide high bisection bandwidth. Various fine-grained load balancing schemes have been proposed to split flows across multiple paths. However, data center networks suffer from many uncertainties such as highly dynamic traffic. These uncertainties easily make network become asymmetric, resulting in significant packet reordering. Unfortunately, existing solutions passively deal with packet reordering based on a threshold and hardly adapt to asymmetric networks because of lacking the explicit reordering feedback. These solutions either fail to quickly respond to packet loss or cause unnecessary fast retransmission, which reduces link utilization and increases flow completion time. In this paper, we propose a fine-grained load balancing scheme RMC to eliminate the impact of packet reordering and handle uncertainties in asymmetric networks. To avoid unnecessary fast retransmission, the switch proactively identifies reordered packet according to local queue length and global path latency. Furthermore, we employ a coding technique with redundancy optimization to reduce long-tailed flow completion time under network asymmetry. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that RMC effectively avoids unnecessary fast retransmission under different network scenarios and reduces flow completion time by up to 72% compared with state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Cloud computing is a new technology, providing different online resources and services to users. Load balancing has become an interesting research area in this field. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to guarantee a service level along dimensions such as performance, availability and reliability. In this work, we propose a Model-based approach to study and analyze centralized and distributed dynamic load balancing protocols in the Cloud. The formal verification of different properties of the studied protocols has been performed automatically and a performance analysis allowing their comparison is also provided. In this paper, we show how aspects of performance, resource consumption, and reliability of the Cloud can be formally modeled, verified and analyzed using a component-based architecture. [ABSTRACT FROM AUTHOR]

With the expansion in the use of IoT, increasing the efficiency of these networks has become even more significant. Objects need reliable communications at suitable speed to be able to reach the expected performance. In a heterogeneous network of IoT, the objects can include users and related devices. Despite the hybrid Li-Fi and Wi-Fi networks, IoT needs were somewhat met. However, the efficiency of these networks depends on the choice of access points at the network level. In this study, a new algorithm was proposed based on the access point selection model, considering the strategic behavior of the objects and users. In this algorithm, inspired by access point selection model, a new adaptive algorithm was selected by choosing the access point by Markov game to enhance the load balancing and efficiency in IoT networks based on Wi-Fi and Li-Fi combination. According to the simulation results, it is seen that the proposed method could greatly increase the efficiency of IoT network and better distributes network load between access points with different technology. Overall network throughput was estimated at an average of at least 10% compared to the load-balancing method with fuzzy logic approach proposed in before research. [ABSTRACT FROM AUTHOR]

Driven cast-in-situ (DCIS) piles are classified as large displacement piles. However, the use of an oversized driving shoe introduces additional complexities influencing shaft resistance mobilisation, over and above those applicable to preformed displacement piles. Therefore, several design codes restrict the magnitude of shaft resistance in DCIS pile design. In this paper, a series of dynamic load tests was performed on the temporary steel driving tubes during DCIS pile installation at three UK sites. The instrumented piles were subsequently subjected to maintained compression load tests to failure. The mobilised shear stresses inferred from the dynamic tests during driving were two to five times smaller than those on the as-constructed piles during maintained load testing. This was attributed to soil loosening along the tube shaft arising from the oversized base shoe. These radial stress reductions appear to be reversible by the freshly cast concrete fluid pressures, which provide lower-bound estimates of radial total stress inferred from the measured shear stresses during static loading. This recovery in shaft resistance is not recognised in some European design practices, resulting in conservative design lengths. Whilst the shaft resistance of DCIS piles was underpredicted by the dynamic load tests, reasonable estimates of base resistance were obtained. [ABSTRACT FROM AUTHOR]

Software-defined networks have many benefits such as more control over the control plane and reduced operating costs through separating the control plane from the data plane in network equipment. One of the most critical problems in software-defined networks is a controller placement problem, which significantly influences its overall performance. The purpose of this problem is to determine the number of controllers required and how to assign switches. This paper attempts to solve this problem, aiming to reduce the network's operational cost and to improve their survivability and load balancing. Hence, we have tried to divide the network into several subdomains using segmentation. Then, we used multi-criteria decision-making methods to solve the controller placement in each subdomain. For this purpose, we considered criteria such as reliability rate, cost, delay, and processing capacity. To assign switches to controllers, we used the proposed mathematical model to minimize the objective function while observing the defined constraints. Furthermore, according to the proposed architecture, we used distance to the central controller, adjacent neighborhood, and controller memory to select the cluster head controller for each subdomain to communicate with the central controller. Finally, we performed experiments on topologies Uran, SwitchL3, and Sinet of the Internet Topology Zoo to evaluate the proposed method. In these experiments, we compared the results of the proposed method with our work-related methods, including Controller Placement Analytic Hierarchy Process (CPAHP) and Controller Placement Modified Density Peak (CPMDP), in terms of criteria such as cost, survivability, number of controllers, connection failure probability, average delay, and controller load-balancing rate. The results show that the proposed method outperforms CPMDP and CPAHP. Thus, the proposed method has a 24.97% and 19.76% improvement in reducing network implementation costs than CPMDP and CPAHP, respectively. [ABSTRACT FROM AUTHOR]

Summary: Data center networks (DCNs) are facing challenging control problems such as flow scheduling, congestion control, load balancing, and bandwidth allocation when dynamically handling heterogeneous mice and elephant flows. This paper focuses on the load balancing in DCNs as an online decision‐making problem with high complexity (NP‐hard problem). Traditional load balancing approaches are usually the heuristic algorithms relying on the operator's viewpoint and a short‐term knowledge of the traffic conditions and network environment. This paper proposes DeepRLB, a deep reinforcement learning (DRL)‐based load balancing approach for software‐defined networking‐based DCNs which exploits the deep deterministic policy gradient (DDPG) algorithm to adaptively learn the link‐weight values by observing the traffic flow characteristics. The actor and critic neural networks in the DDPG model have been designed based on two learning models: fully connected (DeepRLB‐Full) and convolutional (DeepRLB‐Conv). The output of the model is further used by the controller to determine the forwarding paths for traffic flows accordingly. We evaluated the performance of DeepRLB by comparing it with ECMP as well as a per‐packet load balancing algorithm in three different data center topologies under 30 heterogeneous traffic demand matrices. The obtained results showed that the DeepRLB algorithm outperforms the ECMP with respect to studied load balancing metrics considerably (DeepRLB‐Conv achieved the best performance). As a sample, DeepRLB‐Full and DeepRLB‐Conv were able to reach 19.14% and 24.46% reduction in the total number of over‐utilized and under‐utilized network links compared to ECMP, respectively. [ABSTRACT FROM AUTHOR]

In this article, we introduce an adaptive strictly negative-imaginary (SNI) autopilot for a low-cost quadrotor aerial vehicle, specifically designed to achieve high precision hovering and perform accurate trajectory tracking under time-varying dynamic load (i.e., displacement, velocity, and acceleration). Leveraging the learning ability of an artificial neural network, our adaptive SNI controller is robustly designed to overcome uncertainties in flight environments such as variations in the centre-of-gravity, modeling errors, and unpredictable wind gusts. The efficacy of the proposed adaptive control system is investigated under extensive flight tests in addition to numerous computer simulations and rigorous comparison with other control techniques, namely, fixed-gain SNI, fuzzy-SNI, and conventional PID controllers. We also conduct a stability analysis of the proposed control system using the SNI theorem. [ABSTRACT FROM AUTHOR]

Purpose: Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them. Design/methodology/approach: Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency. Findings: The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm. Research limitations/implications: There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment. Practical implications: The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics. Originality/value: This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic. [ABSTRACT FROM AUTHOR]

In this paper, a bias‐eliminated output error (OE) model identification method is proposed for single‐input‐single‐output sampled systems with integer‐type time delay subject to load disturbance with unknown dynamics. By viewing the load disturbance response as a time‐variant parameter, an iterative least‐squares identification algorithm is established to estimate the rational model parameters together with an integer‐type delay parameter, while the disturbance response could be simultaneously estimated. To overcome the adverse effect of stochastic noise involved with output measurement, an auxiliary model is constructed to predict the noise‐free system response. Moreover, a set of adaptive forgetting factors is introduced to expedite the convergence rate of model parameter estimation and the tracking performance of load disturbance response, respectively. In addition, a monotonically rising profile for evaluating the delay parameter is proposed for implementing the above iterative least‐squares algorithm, in order to avoid the occurrence of multiple local minima of the loss function for model fitting. The asymptotic convergence on estimating the rational model parameters together with the delay parameter is analysed with a proof. An illustrative example is given to demonstrate the effectiveness and advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Applications are evolving in ways that demand geographically distributed resources to co-operate in order to give users better Quality of Service (QoS). There is a plethora of ways to implement such systems, but a Heterogeneous Distributed System such as Jungle Computing System is the one that fits the above scenario the most. Additionally, utilizing the Microservices paradigm results in a dynamic system that can overcome today's needs for reduced costs, heterogeneity, scalability and fault tolerance. Moreover, Load Balancing is a NP-Hard problem and finding the optimal solution becomes harder when the scale is getting larger. The development and simulation of a Heterogeneous System utilizing Microservices is presented in this article. This work aims at studying how existing load-balancing algorithms can reduce operational costs of such a system and introduces a new algorithm (SaMW) that results in decreasing expenses while keeping relatively low the Mean Response Time. [ABSTRACT FROM AUTHOR]

Nowadays, datacenters become more complicated and handle many more users’ requests. Custom protocols are becoming more demanded, and an advanced load balancer to distribute the requests among servers is essential to serve the users quickly and efficiently. P4 introduced a new way to manipulate all packet headers. Therefore, by making use of the P4 ability to decapsulate the transport layer header, a new algorithm of load balancing is proposed. The algorithm has three main parts. First, a TCP/UDP separation is used to separate the flows based on the network layer information about the used protocol in the transport layer. Second, a flow size prediction technique is adopted, which relies on the service port number of the transport layer. Lastly, a probing system is considered to detect and solve the failure of the link and server. The proposed load balancer enhances response time of both resources usage and packet processing of the datacenter. Also, our load balancer improves link failure detection by developing a custom probing protocol. [ABSTRACT FROM AUTHOR]

Fog‐based VANETs (Vehicular Ad hoc NETworks) is a new model with vehicular cloud and fog computing benefits. Fog‐based VANETs consist of a series of mobile nodes that are fully dynamic without any central management. These networks have some limitations due to the use of the mobile power source. There are many challenges in extending mobile‐based networks' life span. Many methods have been suggested to minimize the nodes' energy consumption and extend the network life. Also, some works have been developed for load‐balanced routing. Still, energy‐efficient routing and load balancing in VANETs are challenges. The purpose of this study is to present a suitable method based on energy awareness for load balancing in fog‐based VANETs using a hybrid optimization algorithm (employing ant colony optimization and artificial bee colony (ACO‐ABC)). The simulation results in the Network Simulator 2 (NS2) environment showed that as the number of nodes increased, the consumed amount of energy in the VANET is increased. Also, with the increasing number of tasks, load balancing by the proposed method has been improved. Finally, the simulation experiments showed that the proposed hybrid algorithm (ACO‐ABC) outperforms all other algorithms. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]