Node Load Research Articles

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost.

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.

Identification of Key Nodes in a Power Grid Based on Modified PageRank Algorithm

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid.

RESEARCH OF A COMPLEX LOAD WITH AN INDUCTION MOTOR

The article is devoted to the investigation of the operation of an induction motor in transient regimes as part of a complex load. Simulation of several modes of operation of the specified load node was performed: starting the engine on the fan load with the load node running, the engine running out when the load node was disconnected and then restarting the power supply. Improvement of modeling methods and development of an appropriate methodology for calculating transients in complex load nodes with nonlinear elements is an urgent task.

A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

SummaryIn underwater wireless sensor networks (UWSNs), most of the mobile sink data gathering scheme assumes that sensor nodes are static. Also, very limited set of works aim to eliminate the data redundancy during data gathering. Moreover, the data collection latency, error rate and load have to be considered in addition to the buffer space and energy level of the nodes. Here, a mobile sink data gathering technique is designed where rendezvous points (RPs) and sojourn times are selected. The RPs, where the mobile sinks stop to gather the data, are selected based on the node density, current traffic load and energy level of nodes. The proposed scheme, that is, adaptive distributed mobile sink data gathering (ADMSDG), eliminates data redundancy even by considering mobility of all the nodes including sink node. Here, multihop data gathering is also considered to address the energy hole problem near the sink based on similarity index. To examine the result, proposed scheme and other state‐of‐the‐art schemes are implemented in NS2.30 having the same environment. Obtained results show that when proposed scheme is experimented with respect to number of nodes, the proposed ADMSDG technique achieves 18% high data collection accuracy with 5% reduced data collection delay and 5% less energy consumption. Similarly, when proposed scheme is experimented with respect to transmission range, the proposed ADMSDG technique achieves 18% high data collection accuracy with 12% reduced data collection delay and 8% less energy consumption.

COMPLEX MODELING OF TECHNICAL CONDITION AND REGIMES OF VOLTAGE REGULATION OF CONTROL DEVICES UNDER LOAD OF POWER TRANSFORMERS

The scientific work considers issues (tasks) of the technical condition assessment and voltage regulation in power system load nodes using power transformers on-load tap-changer devices by fuzzy modeling methods. A linguistic model of TC class determining and decision-making on the operation strategy for OLTC devices is presented. A fuzzy algorithm for voltage reg-ulation in load nodes by OLTC devices is proposed, taking into account resource constraints. A voltage control modes test study under changing the nodal load was conducted for the consumers power supply system of the distribution network

Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

Routing optimization on power packet dispatching system based on energy loss minimization

Power packet dispatching system has been proposed for smart power management in the form of discretized packet. In this paper, we discuss the routing optimization of power packets on the network of power routers. We propose a cost metric for the power packet delivery by circuit analysis of the router network. Using the metric, we formulate the optimization problem as a general shortest path problem from a source node to a load node. The result of numerical simulations shows that the proposed algorithm can allocate distributed power sources to load demands and identify the optimal path for the power delivery.

Dynamic Zoning and Collaborative Control of Autonomous Region of an Active Distribution Network Based on Synchronous Measurement Information

With the continuous access of distributed power to the active distribution network, the voltage overrun at key load nodes is easy to occur, which will affect the safe operation of the active distribution network. Therefore, the dynamic partition and collaborative control method of autonomous region of active distribution network based on synchronous measurement information are studied, so as to rationally dynamically partition the active distribution network, effectively coordinate the voltage control of each partition, and ensure the safe operation of distribution network. Synchronous phasor measuring device is used to collect node voltage related information of distribution network. Based on the collected information and the eigenroots of the electrical distance matrix, the number of larger eigenroots, namely the optimal dynamic partition number, is determined. Ward distance clustering algorithm is used to complete the dynamic partition of distribution network. After the completion of dynamic partition, for the nodes with the most serious voltage overlimit in each partition, the collaborative control of the voltage in each partition is completed according to the eventdriven mode. The experimental results show that the proposed method can be used to dynamically partition the autonomous regions of active distribution network and ensure that the reactive power reserve margin of each region is higher than the minimum standard. This method can effectively control the voltage of each node, and the control time is short and the active power loss is less.

Research on evaluation of minimum backbone grid of transmission network based on differentiation planning

The essence of the differential planning of transmission network is to evaluate the minimum backbone grid of transmission network, and the evaluation of the backbone grid of transmitting minimum network includes the evaluation of important nodes and the evaluation of important lines. In this paper, the important node evaluation of the transmission network takes into account the structural fragile nodes and the important load nodes, and the important line evaluation is based on the line number, which is considered synthetically from the point of view of the structural fragile line and the important load line. The minimum backbone grid of transmission network is constructed by the structure backbone grid and the important load backbone grid.

Research on differentiated scheduling method of electric vehicle considering random probability

On the basis of the charging load of a single electric vehicle, in order to reduce the variance of the equivalent load curve and alleviate the peak plus peak caused by electric vehicles, a local electric vehicle charging cooperative scheduling model with the distribution network node as the optimization unit is set up. The model is composed of node agent and electric vehicle agent. The electric vehicle agent is based on the node load obtained. Rate information, combining with its own state, using random probability charging algorithm to make the decision to start charging or stop charging, and use IEEE-33 node system as an example to use MATLAB software for the designed electricity. The algorithm is verified by dynamic vehicle charging cooperative scheduling strategy. The results show that the proposed algorithm can effectively load the load curve and reduce the variance of load curve.

The use of Analytical Hierarchy Process in sensor-based networks for security-aware congestion control

<abstract><p>Network congestion may occur naturally or intentionally caused by selfish nodes. Existing congestion control techniques designed by researchers for sensor-based networks have primarily focused on natural modes of congestion occurrence and ignored malevolent nodes' potential for purposeful congestion-like scenario creation. In light of this fact, a security attack-resistant congestion control method that takes into account both possible sources of congestion in sensor nodes has been developed. So firstly, a trust-based technique has been developed to get rid of selfish nodes' intentional attempts to cause congestion. After the elimination of malicious nodes, a congestion avoidance method has been applied which tries to prevent the natural way of congestion occurrence. For this purpose, we have applied a multi-criteria decision-making method as there are many factors responsible for congestion occurrence. The remaining energy, node potential value, node load factor, and traffic burst rate have been considered as decision factors. Simulation results show that our Security Aware Congestion Control technique using the AHP method (SACC-AHP) outperforms the existing relevant techniques LEACH, TCEER, TASRP, CARA and SACC in terms of energy efficiency, security, packet delivery ratio and network lifetime.</p></abstract>

DESIGNING THE STRUCTURE OF HINGED FASTENING OF THE MULTILAYER COMPOSITE BOARD TO A VEHICLE TRAILER BODY

Steel sides of dump tractor trailers used in the transportation of bulk and semi-liquid agricultural goods are subject to active abrasive wear and corrosion. The article proposes replacing the existing design of the sides, instead of steel sheets to use multilayer composite plates, which will increase the reliability and service life of the machine as a whole. (Research purpose) The research purpose is developing a design and methodology for calculating the hinge fastening of a composite side to the agricultural vehicle. (Materials and methods) The article analyses the existing metalcomposite compounds. When using multilayer sandwich-structured composites, all layers do not adhere to the metal base. We have developed a design of a pivotally movable side support of the trailer, which provides a tight connection due to the introduction of a metal steel element into the prepreg, consisting of a plate with studs and an axle welded to it. Additional tightening of the support due to threaded connections made it possible to connect a multilayer composite with metal plates. (Results and discussion). The article presents the equation of node loading to determine the maximum force acting on the fastener. The calculation of the most loaded part, the fastener, was carried out according to the permissible stresses of the cut of its cylindrical part. (Conclusions) The expediency of using power structures of vehicles of the agro-industrial complex exposed to abrasive wear and corrosion made of polymer composite materials is confirmed. A new design of a detachable swivel joint of a multilayer composite side with a metal frame was created.

Microservice Deployment in Edge Computing Based on Deep Q Learning

The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.

Multi-task equilibrium scheduling of Internet of Things: A rough set genetic algorithm

The Internet of Things (IoT) server suffers from numerous business traffic with network bandwidth growth, resulting in downtime. Providing business support without affecting the user experience is the primary problem that IoT companies need to consider and solve in the face of traffic impact. This paper proposes a load balancing scheduling algorithm based on Particle Swarm Optimization Genetic Algorithm (PSO-GA) for IoT clusters. The algorithm uses CPU occupancy rate, memory occupancy rate, network bandwidth occupancy rate, and disk Input and Output (IO) occupancy rate to comprehensively measure the server node load and establish a resource balance model. The fitness function is used to quantify the influence as the basis of weight adjustment. Then, the Particle Swarm Optimization (PSO) algorithm uses the disturbance factor and contraction operator. The optimized algorithm is used to calculate the optimal solution of the fitness function and obtain the optimal weight. Finally, the PSO-GA algorithm is simulated, tested, and compared with the other three load balancing algorithms. As seen from the test results of response delay, throughput, request error rate, and resource utilization, the performance of this algorithm is improved by more than 5% compared with the performance of the traditional method, and the optimization ability is improved obviously. The research content of this paper provides a new way to alleviate the network load, reduce the server overload, congestion, downtime, and other problems, and realize the multi-task balanced scheduling of IoT.

A Privacy Preserved, Trust Relationship (PTR) Model for Internet of Vehicles

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks.

An efficient skyline query algorithm in the distributed environment

The skyline operator plays an important role in multiple criteria decision making. The traditional skyline query algorithm has high efficiency in dealing with a small amount of data. However, as the data scale increases, the previous algorithm of skyline query in a centralized environment cannot be applied to new applications. Therefore, in this paper, the distributed skyline query algorithm (DSQ) is proposed to process skyline queries in the distributed environment. DSQ contains two stages. In the first stage, redundant data tuples are pruned based on a spatial index. In the second stage, a scheduling plan based on rotation is used to calculate the global skyline. The rotation scheduling strategy ensures that the load of each data node is balanced. Finally, the effectiveness of DSQ is evaluated through a series of experiments.

SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters

A key feature of distributed stream processing (DSP) systems is the scheduling of operators on clustered computers. In scheduling, the assignment plan of operators to nodes of the cluster, requirements of operators, and the computational power of each worker node must be considered with the goal of finding a tradeoff between the communication latency of operators and the utilization of worker nodes to minimize the overall system response time. To reach this goal is quite challenging, especially in heterogeneous clusters, because there are no accurate estimations about the loads of worker nodes at run time. To address this challenge, we propose a novel stream processing scheduling using ant colony algorithm (SP-Ant). SP-Ant finds the best operator assignment plan considering the inter-node communication latencies of operators by initially collocating highly communicative operators on the same worker nodes using the bin-packing algorithm and iteratively (re-)scheduling only the less communicative operators using the exploration and exploitation phases of the evolutionary ant colony optimization (ACO) algorithm in order to reduce its convergence time. SP-Ant is implemented on the standard Apache Storm. Using several standard benchmark topologies of Storm, it is shown that SP-Ant outperforms the R-Storm and Storm default schedulers by at least 50% in reducing the response time.

Statistical Multimode Accounting in the Problem of Optimal Reactive Load Compensation when Constructing Smart Grids

The article deals with the development prospects for smart grids. An approach to optimizing the modes of electric power systems in terms of reactive power is presented in detail. The solution of this problem considers the multimodality, determination of the integral characteristics of the modes, etc. Analytical modeling of load changes by means of the method of factor analysis allows to reduce the amount of information drastically without a significant loss of the accuracy of the obtained solutions. For this purpose, the actually correlated electrical loads of various nodes of electric power systems are represented in the form of a linear combination of independent random variables, namely, generalized load graphs. It is shown that the inclusion of multimode by orthogonal graphs results in a significant simplification of the solution of multimode problems. The choice of the dependent and independent variables composition when solving the optimization problem with the power consumption modes has a fundamental effect both on the modeling of constraints, the formation of the reduced gradient and the main calculation expressions, and on the speed of the optimization search as a whole

The self-adaptive routing strategy to alleviate packet loss in finite buffer networks

In real communication or transportation systems, loss of agents is very common due to finite storage capacity. We study the traffic dynamics in finite buffer networks and propose a routing strategy motivated by a heuristic algorithm to alleviate packet loss. Under this routing strategy, the traffic capacity is further improved, comparing to the shortest path routing strategy and efficient routing strategy. Then we investigate the effect of this routing strategy on the betweenness of nodes. Through dynamic routing changes, the maximum node betweenness of the network is greatly reduced, and the final betweenness of each node is almost the same. Therefore, the routing strategy proposed in this paper can balance the node load, thereby effectively alleviating packet loss.

Vibration response analysis of a gear-rotor-bearing system considering steady-state temperature

Thermal effect is a crucial factor leading to failure of gear system. However, the lack of comprehensive nonlinear dynamics model limits the further study of thermal effects. The constitutive relation of beam element considering steady-state temperature is reconstructed, and thermal node load is formulated. Considering the influences of thermal expansion and temperature on material properties, a more comprehensive dynamic model of gear-rotor-bearing system is established based on the finite element node method. Nonlinear friction, high-speed gyroscopic effect, thermal-related time-varying meshing stiffness (TVMS) and thermal backlash are included in the model. The effects of temperature and bearing type on the vibration response of gear system are analyzed. The results show that the system motion changes from period to chaos with the temperature increase in part of the speed range. The appropriate backlash could restrain the chaotic motion caused by temperature rise. Moreover, the temperature significantly increases the axial bearing force, and the appropriate bearing could reduce the axial displacement. This research can further understand the influence of temperature on the dynamic response of gear system and guide the design of gear system.