Load Balancing Control Research Articles

Design an Improved Hybrid Routing Protocol Strategy to Minimize Delay & Overhead for MANET

Mobile adhoc networks (MANET) are a self-coordinated wireless network that is worked without perpetual foundation and base station endorsement. In MANETs (Mobile Ad Hoc Networks) every single node activity goes about as the information source and communicator router. It notices its useful neighbours by making themselves by passing the nodes that got terminated of that correspondence range. In this paper, we designed the IHRP routing protocol with the help of combining the AODV, AOMDV and OLSR routing protocol with the network situation based routing adaptation. In the previous analysis, we identify AODV is more compatible for dynamic environment (where node motion is higher).The AOMDV routing protocol is useful for network load balancing and congestion control. OLSR is compatible for stable network which use as maximum link stability base routing. While we are designing the IHRP routing protocol through of the above (AODV, AOMDV and OLSR) protocol .We handle the network behaviour with better performance in any situation. Initially the RREP are broadcasted to find out the route. The poll of routing, select based on the network situation. The proposed IHRP start with a RREQ packet is to search the distance from its origin to its objective node. The network motion is higher than AODV protocol handle the particular situation but in any situation, while the 70% network node are stable then the route selection and transferring of data with the help of an OLSR routing protocol. The propose IHRP routing protocol also handle the network load and control the network congestion through the AOMDV routing protocol. Our proposed protocol IHRP also works in a hybrid manner while the source and destination belong in longer range such as different cluster. At that time situation based routing was followed between the clusters. We saw in the result part the comparison of IHRP and ZRP routing protocol. Execution of the improved hybrid routing protocol (IHRP) is much better than hybrid routing protocol (ZRP). In case of Packet Delivery Ratio analysis for IHRP and ZRP, we clearly show that The Packet Delivery Ratio is 97.99% by IHRP and 79.49% by ZRP, so we can say that the performance of packet delivery ratio in Improved hybrid routing protocol (IHRP) is much better than ZRP, where Improved hybrid routing protocol (IHRP) and hybrid routing protocol (ZRP) both performed the conjunction of proactive and reactive routing protocol features therefore we can say that Packet Delivery Ratio is better in improved hybrid routing protocol (IHRP) as compare to hybrid routing protocol (ZRP).
 We clearly show that the Normal Routing Load analysis for IHRP and ZRP where Normal Routing Load is 0.11% by IHRP and 4.72 % by ZRP, so we can say that performance of Improved Hybrid Routing Protocol (IHRP) in case of a Normal Routing Load is much less than the Hybrid Routing Protocol (ZRP).
 In case of overall parameter matrix (Normal Routing Load, Throughput, Packet Delivery Ratio and Average End To End Delay) the performance of Improved Hybrid Routing Protocol (IHRP) is much better than existing Hybrid routing protocol (ZRP).

Highly-Efficient Switch Migration for Controller Load Balancing in Elastic Optical Inter-Datacenter Networks

In elastic optical inter-datacenter networks, multiple software-defined networking (SDN) controllers have been used to improve scalability and reliability. The static controller deployment faces the problem of controller load imbalance under dynamic changes in network traffic. In response to this problem, existing research works proposed to use dynamic switch migration to achieve the controller load balancing. However, the efficiency of the existing switch migration is low, because the load balancing performance of controllers does not improve significantly after the switch migration and the migration activities can also cause additional migration costs. Thus the efficiency of switch migration has not yet been well resolved. In this work, we propose a highly-efficient switch migration (HESM) for controller load balancing. The proposed HESM method defines multiple load metrics to measure the load of controllers, and selects the optimal target controller with the largest remaining resource, which improves the load balancing performance. HESM selects switches based on minimizing migration costs, reducing additional migration costs. In addition, HESM can handle the load of multiple controllers in parallel during a switch migration activity, which improves the efficiency of migration. The simulation results show that HESM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.

An Exploratory Research on Constructing a Model of Innovation and Entrepreneurship Education for College Students Based on Fuzzy Neural Network Algorithm

In order to improve the adaptive scheduling ability of the innovation and entrepreneurship education model for college students, a brain computing-based innovation and entrepreneurship education model for college students is proposed. Through career-planning education, team training will gradually cultivate the leadership skills of entrepreneurs and improve the overall level of innovation and entrepreneurship of college students. The paper proposes to use the fuzzy neural network algorithm to analyze the control constraint parameters of the college students’ innovation and entrepreneurship education model and optimize the design of the control objective function of the university. The control of students’ innovation and entrepreneurship education model is carried out using quantitative optimization methods. The fuzzy neural network is used to identify the control rights of college students’ innovation and entrepreneurship education model through label recognition technology, and the control objective model of college students’ innovation and entrepreneurship education model is constructed. The adaptive load-balancing control method is adopted to control the innovation and entrepreneurship education model of college students. Research shows that this method has a good balance and adaptive control ability in the construction of college students’ innovation and entrepreneurship education model.

Research on Multi-mode Operation Control Strategy of Feeder Flexible Interconnection in Distribution Network Based on Flexible Multi-State Switch

Aiming at the problems of multi-mode operation and switching, unbalanced feeder load and heavy load of main transformer in flexible interconnection system of distribution network based on Flexible Multi-State Switch(FMSS), a multi-mode operation control strategy of feeder interconnection in distribution network based on Virtual Synchronous Generator (VSG) is proposed,which includes load balancing control strategy and heavy load main transformer automatic control strategy. Firstly, according to the feeder and main transformer load status, the operation mode of system is divided.Then, through the analysis power transmission and switching logic of different operating mode, active power control instructions of FMSS under multi-mode operation are obtained. So that multi-mode stable operation and free switching of the system, the feeder load balancing and main transformer heavy load automatic regulation are realized. In addition, there is no need for control strategy switching. Simulation verifies the rationality and effectiveness of the proposed control strategy.

Dynamical Control Domain Division for Software-Defined Satellite-Ground Integrated Vehicular Networks

Software-defined satellite-ground integrated vehicular networks have emerged as indispensable infrastructures that provide rural area coverage and diverse vehicular services. However, previous research on control domain division mostly focuses on data center networks. To address these issues, we formulate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamical control domain division (DCDD)</i> problem to minimize the total management cost. Since the DCDD problem is NP-hard, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximation algorithm RDCA</i> based on the randomized rounding. We formally analyze the performance of the RDCA algorithm. Guided by the rounding procedure, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heuristic algorithm HCA</i> to greedily choose the best controller at each time slot. Two kinds of switch migration operations are designed to further minimize the total management cost. Extensive simulations show that our HCA algorithm outperforms related schemes in terms of management cost, response time and controller load balancing.

Joint Traffic-Aware Consolidated Middleboxes Selection and Routing in Distributed SDNs

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

Research on Load Balancing Control of Internet of Things (IoT) Link Based on Sparse Random Clustering

Aiming at the problems of poor channel balancing control ability and high output bit error rate in traditional IoT link load balancing control methods, an improved ant colony algorithm based load balancing control method for IoT link is proposed. This paper constructs the channel transmission model of IoT, uses sparse random clustering analysis method to detect the load characteristics of Internet of things links, and extracts the statistical characteristics of IoT link. The output balance control and interference suppression of IoT link are carried out by matching filter detection method, and the load balance of IoT link is fuzzy control combined with fuzzy PID control method. The simulation results show that the proposed method has good self-adaptability, strong channel equalization control ability and lower output bit error rate.

Research on a New Attribute Encryption Anonymous Algorithm in Cloud Computing Environment

The attribute encryption anonymous model is used to encrypt data and protect anonymity in cloud computing environment, so as to realize attribute encryption and privacy protection in cloud computing environment. In order to improve the self-adaptability and accurate control of data encryption in cloud computing environment, an anonymous data encryption algorithm based on hyperbolic cyclic mapping is proposed. The priority list control model of adaptive privacy protection for cloud data is constructed. The load balancing control and encryption key design of anonymous big data with hyperbolic cyclic mapping are implemented by using bilinear differential control protocol. The hyperbolic differential equation control and adaptive weighting method are used to construct the key function for anonymous encryption of cloud computing data. The time window and privacy protection protocol of attribute encryption anonymous control is optimized by slot allocation. The encryption anonymous algorithm is improved. Simulation results show that the adaptive equalization performance of attribute anonymous encryption in cloud computing environment is better, the ability of anti-attack is better, and the recall rate of data is better than the traditional method.

THE METHOD OF SDN CLUSTERING FOR CONTROLLER LOAD BALANCING

The paper proposes the method of SDN clustering using connections density and controller load distribution that solves the problem of controller load balancing. Clustering efficiency criteria have been considered, including fault tolerance, controller-to-switch and intercontroller latency and network limitations. Review of the key clustering methods has been performed, and the base algorithm for modification has been chosen. Density-based controller placement algorithm is modified to solve the problem of multicontroller placement. Metric of the node boundary index is introduced to advance the efficiency of proposed algorithm. A software implementation of the developed algorithm has been created, and its performance has been tested. The algorithm’s modelling results have been compared with those of the other algorithms using the metrics of distribution of service traffic propagation delay and computational speed in relation to network topology size.

Improving acceptance rate of QoS-guaranteed point-to-multipoint traffic flows in elastic optical networks

Abstract This paper studies the load-balanced routing and admission control for point-to-multipoint traffic flows in elastic optical networks (EONs). EONs have emerged as a promising technology to provide high capacity for next-generation networks. Elastic bandwidth allocation promotes the spectrum utilization efficiency. We consider the problem of constructing bandwidth-guaranteed trees for on-demand point-to-multipoint session requests. A request is accepted, if a multicast tree with sufficient bandwidth on each link can be established. The main objective is to increase the multicast acceptance rate in the network. For this purpose, we propose an efficient scheme, namely optical load-balanced multicast routing with admission control (OLMR-AC), to improve the wavelength resources utilization in the network. As an advantage, OLMR-AC jointly considers both distance and congestion factors to create a multicast session. For each admitted session, a bandwidth–guaranteed steiner tree is provided in which not only the source node has a minimum-cost path to each receiver, but also its overall cost is minimal. The proposed scheme distributes the on-demand traffic flows over the available links fairly. Simulation results show the efficiency of the OLMR-AC algorithm in terms of multicast acceptance rate, average length of lightpaths, throughput, number of transmissions, and load variance.

Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarms

MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning

The control plane plays a significant role in Software-Defined Networking (SDN). A large SDN usually implements its control plane with several distributed controllers, each controlling a subset of switches and synchronizing with other controllers to maintain a consistent network view. Under the fluctuating network traffic, a static controller-switch mapping relationship could lead to imbalanced workload allocation. Controllers may getoverloaded and reject new requests, eventually reducing the control plane's request processing ability. Most existing schemes have relied heavily on iterative optimization algorithms to manipulate the mapping relationship between controllers and switches, which are either time-consuming or less satisfactory in terms of performance. In this paper, we propose a dynamic controller workload balancing scheme, that is termed MARVEL, based on multi-agent reinforcement learning for generation of switch migration actions. MARVEL works in two phases: offline training and online decision making. In the training phase, each agent learns how to migrate switches through interacting with the network. In the online phase, MARVEL is deployed to make decisions on migrating switches. Experimental results show that MARVEL outperforms competing existing schemes by improving the control plane's request processing ability at least 27.3% while using 25% less processing time.

Performance analysis of wireless sensor network with load balancing for data transmission using xbee zb module

&lt;p&gt;&lt;span&gt;In general, research in the field of wireless sensor network (WSN) has never discussed the reliability aspect of network routing with router devices that can find new routes when damage occurs. &lt;/span&gt;&lt;span lang="IN"&gt;To date, o&lt;/span&gt;&lt;span&gt;verloaded routers will be &lt;/span&gt;&lt;span lang="IN"&gt;ignored&lt;/span&gt;&lt;span&gt; without any response that gives control which can reduce the quality of network performance. Therefore, we propose research using the AODV routing&lt;/span&gt;&lt;span lang="IN"&gt;,&lt;/span&gt;&lt;span&gt; and Mesh routing algorithm to find other routes as an alternative when problems occur and using the Round Robbin based xbee algorithm on providing load balance control carried out by the router. &lt;/span&gt;&lt;span lang="IN"&gt;The experiments on&lt;/span&gt;&lt;span&gt; the performance of non-balancing networks and balancing&lt;/span&gt;&lt;span lang="IN"&gt; were conducted&lt;/span&gt;&lt;span&gt;. Both trials used quality of service (QoS) parameters as a guarantee of performance to be more effective and in line with expectations. Measurements performed by testing the parameters of packet loss, delay, throughput, and fault tolerance&lt;em&gt;. &lt;/em&gt;The network performance in finding other alternative routes has been successfully carried out by &lt;/span&gt;&lt;span lang="IN"&gt;transmitting&lt;/span&gt;&lt;span&gt; 100 packets from the end device node to the coordinator node via the router based on distance variations from 0 to 100 meters. The recovery time required by the dead router to find another route was 10 seconds, this was related to the parameter delay, and fault tolerance. The experimental results of the non-balancing system showed an average 20 % packet loss in one transmission, meanwhile the packet loss was smaller than the previous experiment by 37%. Therefore, the WSN with balancing system was proven to be more effective that could improve QoS performance by 17%.&lt;/span&gt;&lt;/p&gt;

An Efficient Adaptive Load Balancing Scheme for Mitigating Reduction of Quality-Based DDOS Attack on Cloud File Storage Environment

Availability of cloud resources to the cloud users is considered as the serious challenge that pose security essentialities during the process of on-demand service provision. Moreover, a specific type of attack named Reduction of Quality (RoQ)-based DDoS attack is determined to be vulnerable in the cloud computing since it exploits the benefits of the embedded adaptive load balancing and admission control methods of the environment. In this paper, an Efficient Adaptive Load Balancing Scheme-based on Wilcoxon–Mann–Whitney Hypothesis Test (EALBS-WMW-HT) is proposed for mitigating Reduction of Quality-based DDoS attack in order to minimize its influence for enhancing the degree of availability to the cloud users. This proposed EALBS-HT scheme uses the merits of statistical testing on the traffic flow and contributes to the detection of RoQ-based DDoS attack such that they does not disturb the inherent load balancing process of the cloud environment. The experiments of the proposed EALBS-HT scheme revealed an excellent detection accuracy, true positive rate and true negative rate with minimized false negative rate studied on par with the baseline approaches considered for analysis.

Efficient dynamical system resource management method in cloud computing

The resource management of virtualisation system is a core issue in the field of cloud computing. For achieving the goal of efficient manage system resource without performance loss in clouding environment, the authors propose an optimal load balancing controller to assign the resource to different virtual machines, which run on a single physical server. Here, the authors focus on dynamical web workloads that the system resource management problem can be formulated as a dynamical optimisation problem. In the face of dynamical and robust workloads, the authors present an alternative approach based on the constrained boundary linear quadratic control (cBLQC) method, the optimal resource allocation scheme is solved by minimising the quadratic cost function with constrains on the system input and output. Different with existing works that assume a particular distribution for system noise or ignore the noise directly, the authors loosen the restrictions on system noise, which simply assume that system noise belongs with some reasonable norm-bounded set. Experiments on the Xen-based platform with a set of workload patterns show that the efficiency of authors’ method in terms of control precision and stability.

Low-Complexity Distributed SDN Controller Environments on Switch Migration Testbed

Control Path Management is an important component in the Distributed SDN Controller, and plays a key role in Software Defined Networking (SDN). Testbed simulations are done with an implementation of several controllers are deployed to improve the scalability and reliability of the control plane framework by employed network clustering into several subdomains with separate controllers. This paper investigates the deployment of distributed SDN controller but logically centralized systems for decouples control plane using Distributed Traffic Engineering mechanism. The results obtained show that the proposed mechanism can achieve controller load balancing with better performance.

Accurate Privacy Preserving Average Consensus

Average consensus has significant applications in dynamic load balancing and cooperative control of vehicle formations, where all the agents receive information from neighboring agents via communication network and update their states to achieve an agreement. However, this approach would result in an undesirable disclosure on the initial states of agents to their neighbors. In this brief, we propose an accurate privacy preserving average consensus (APPAC) algorithm, where all the agents independently generate and transmit random numbers based on Paillier cryptosystem to conceal their initial states. Under the proposed APPAC algorithm, the accurate average consensus is indeed achieved. Besides, the necessary and sufficient conditions that initial states can be inferred are also discussed. Extensive simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

Optimal energy-aware load balancing and base station switch-off control in 5G HetNets

We consider optimal energy-aware load balancing of elastic downlink data traffic inside a macrocell with multiple small cells within its coverage area. The system is modeled as a set of parallel queues. In particular, the model of the small cell includes the setup delay resulting from activating the base station after being placed in a low power off state and the idle timer controlling the amount of time to wait before being switched off. We apply the theory of MDPs to develop state-dependent dynamic policies for controlling both the routing of the arrivals as well as the length of the idle timer that minimizes the weighted sum of energy and performance. In particular, we show that in the optimal policy the idle timer control can be simplified to selecting a value arbitrarily close to zero or infinite. Additionally, by utilizing the first step of the well-known policy iteration method, we develop an explicit near-optimal dynamic policy for routing the arrivals and also for determining the idle timer configuration of the system, based on the expressions for the future marginal costs. The performance of the policy is illustrated through numerical examples.

A two-wheel load balance control strategy for an HVTL inspection robot based on second-order sliding-mode

PurposeOverhead high-voltage transmission line (HVTL) inspection robots are used to inspect the transmission lines and/or maintain the infrastructures of a power transmission grid. One of the most serious problems is that the load on the front wheel is much larger than that on the back one when the robot travels along a sloping earth wire. Thus, ongoing operation of the inspection robot mainly depends on the front wheel motor’s ability. This paper aims to extend continuous operation time of the HVTL inspection robots.Design/methodology/approachBy introducing a traction force model, the authors have established a dynamic model of the robot with slip. The total load is evenly distributed to both wheels. According to the traction force model, the desired wheel slip is calculated to achieve the goal of load balance. A wheel slip controller was designed based on second-order sliding-mode control methodology.FindingsThis controller accomplishes the control objective, such that the actual wheel slip tracks the desired wheel slip. A simulation and experiment verify the feasibility of the load balance control system. These results indicate that the loads on both wheels are generally equal.Originality/valueBy balancing the loads on both wheels, the inspection robot can travel along the earth wire longer, improving its efficiency.

Fault-Tolerance Distributed Control Plane for Software Defined Networks

The architecture of the high availability distributed control plane for SDN/OpenFlow networks are considered. High availability is achieved by redundancy of controller instances, active switch-controller communications, computing resources and tools for a controller instance failure and overloading detection and recovery. The proactive backup controller allocation algorithm which allows to minimize the time to repair in the case of a single controller instance failure is discussed. The algorithm for controller load-balancing allows dynamically reconfigure the control plane with a minimum number of switch control transfer operations to avoid controller instance overloading. The initial experimental results of the proposed algorithms for the HA distributed SDN control plane are described.