Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

A Sender-Initiated Fuzzy Logic Contrnol Method for Network Load Balancing

In this paper, a sender-initiated protocol is applied which uses fuzzy logic control method to improve computer networks performance by balancing loads among computers. This new model devises sender-initiated protocol for load transfer for load balancing. Groups are formed and every group has a node called a designated representative (DR). During load transferring processes, loads are transferred using the DR in each group to achieve load balancing purposes. The simulation results show that the performance of the protocol proposed is better than the compared conventional method. This protocol is more stable than the method without using the fuzzy logic control.

Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

Sensors are considered as important elements of electronic devices.In many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop communications.But,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much needed.However,reducing energy con-sumption can increase the network lifetime in effective manner.For that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes.In that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes.For providing equal rate of energy consumption among nodes,the dimensions of framed clusters are measured.Moreover,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station.The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node.Additionally,the model also provides higher throughput and minimal delay in delivering data packets.

BE-RPL:Balanced-load and Energy-efficient RPL

Internet of Things(IoT)empowers imaginative applications and permits new services when mobile nodes are included.For IoT-enabled low-power and lossy networks(LLN),the Routing Protocol for Low-power and Lossy Networks(RPL)has become an established standard routing protocol.Mobility under standard RPL remains a difficult issue as it causes continuous path disturbance,energy loss,and increases the end-to-end delay in the network.In this unique circumstance,a Balanced-load and Energy-efficient RPL(BE-RPL)is proposed.It is a routing technique that is both energy-efficient and mobility-aware.It responds quicker to link breakage through received signal strength-based mobility monitoring and selecting a new preferred parent reactively.The proposed system also implements load balancing among stationary nodes for leaf node allocation.Static nodes with more leaf nodes are restricted from participating in the election for a new preferred parent.The performance of BE-RPL is assessed using the COOJA simulator.It improves the energy use,network control overhead,frame acknowledgment ratio,and packet delivery ratio of the network.

Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.

Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

Load Balancing Based on Multi-Agent Framework to Enhance Cloud Environment

According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.

Classification of Request-Based Mobility Load Balancing in Fog Computing

Every day,more and more data is being produced by the Internet of Things(IoT)applications.IoT data differ in amount,diversity,veracity,and velocity.Because of latency,various types of data handling in cloud computing are not suitable for many time-sensitive applications.When users move from one site to another,mobility also adds to the latency.By placing computing close to IoT devices with mobility support,fog computing addresses these problems.An efficient Load Balancing Algorithm(LBA)improves user experience and Quality of Service(QoS).Classification of Request(CoR)based Resource Adaptive LBA is suggested in this research.This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request.The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests.LBA does the operation based on these classifications.The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs.The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance,which was attained by(90.8%).Using LBA,several metrics may be examined,including Response Time(RT),delay(d),Energy Consumption(EC),and latency.Through the on-demand provisioning of necessary resources to IoT users,our suggested LBA assures effective resource usage.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

In wireless sensor network(WSN),the gateways which are placed far away from the base station(BS)forward the collected data to the BS through the gateways which are nearer to the BS.This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load.So,to over-come this issue,loads around the gateways are to be balanced by presenting energy efficient clustering approach.Besides,to enhance the lifetime of the net-work,optimal routing path is to be established between the source node and BS.For energy efficient load balancing and routing,multi objective based beetle swarm optimization(BSO)algorithm is presented in this paper.Using this algo-rithm,optimal clustering and routing are performed depend on the objective func-tions routingfitness and clusteringfitness.This approach leads to decrease the power consumption.Simulation results show that the performance of the pro-posed BSO based clustering and routing scheme attains better results than that of the existing algorithms in terms of energy consumption,delivery ratio,through-put and network lifetime.Namely,the proposed scheme increases throughput to 72%and network lifetime to 37%as well as it reduces delay to 37%than the existing optimization algorithms based clustering and routing schemes.

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

L2-LBMT:A Layered Load Balance Routing Protocol for Underwater Multimedia Data Transmission

Providing highly efficient underwater transmission of mass multimedia data is challenging due to the particularities of the underwater environment. Although there are many schemes proposed to optimize the underwater acoustic network communication protocols, from physical layer, data link layer, network layer to transport layer, the existing routing protocols for underwater wireless sensor network(UWSN) still cannot well deal with the problems in transmitting multimedia data because of the difficulties involved in high energy consumption, low transmission reliability or high transmission delay. It prevents us from applying underwater multimedia data to real-time monitoring of marine environment in practical application, especially in emergency search, rescue operation and military field. Therefore, the inefficient transmission of marine multimedia data has become a serious problem that needs to be solved urgently. In this paper, A Layered Load Balance Routing Protocol(L2-LBMT) is proposed for underwater multimedia data transmission. In L2-LBMT, we use layered and load-balance Ad Hoc Network to transmit data, and adopt segmented data reliable transfer(SDRT) protocol to improve the data transport reliability. And a 3-node variant of tornado(3-VT) code is also combined with the Ad Hoc Network to transmit little emergency data more quickly. The simulation results show that the proposed protocol can balance energy consumption of each node, effectively prolong the network lifetime and reduce transmission delay of marine multimedia data.

Reliable and Load Balance-Aware Multi-Controller Deployment in SDN

Software Defined Networking(SDN) provides flexible network management by decoupling control plane and data plane. However, such separation introduces the issues regarding the reliability of the control plane and controller load imbalance in the distributed SDN network, which will cause the low network stability and the poor controller performance. This paper proposes Reliable and Load balance-aware Multi-controller Deployment(RLMD) strategy to address the above problems. Firstly, we establish a multiple-controller network model and define the relevant parameters for RLMD. Then, we design the corresponding algorithms to implement this strategy. By weighing node efficiency and path quality, Controller Placement Selection(CPS) algorithm is introduced to explore the reliable deployments of the controllers. On this basis, we design Multiple Domain Partition(MDP) algorithm to allocate switches for controllers according to node attractability and controller load balancing rate, which could realize the reasonable domain planning. Finally, the simulations show that, compared with the typical strategies, RLMD has the better performance in improving the reliability of the control plane and balancing the distribution of the controller loads.

On the use of the genetic programming for balanced load distribution in software-defined networks

As a new networking paradigm,Software-Defined Networking(SDN)enables us to cope with the limitations of traditional networks.SDN uses a controller that has a global view of the network and switch devices which act as packet forwarding hardware,known as“OpenFlow switches”.Since load balancing service is essential to distribute workload across servers in data centers,we propose an effective load balancing scheme in SDN,using a genetic programming approach,called Genetic Programming based Load Balancing(GPLB).We formulate the problem to find a path:1)with the best bottleneck switch which has the lowest capacity within bottleneck switches of each path,2)with the shortest path,and 3)requiring the less possible operations.For the purpose of choosing the real-time least loaded path,GPLB immediately calculates the integrated load of paths based on the information that receives from the SDN controller.Hence,in this design,the controller sends the load information of each path to the load balancing algorithm periodically and then the load balancing algorithm returns a least loaded path to the controller.In this paper,we use the Mininet emulator and the OpenDaylight controller to evaluate the effectiveness of the GPLB.The simulative study of the GPLB shows that there is a big improvement in performance metrics and the latency and the jitter are minimized.The GPLB also has the maximum throughput in comparison with related works and has performed better in the heavy traffic situation.The results show that our model stands smartly while not increasing further overhead.

P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using Mathematical Approach

Cloud computing is a collection of disparate resources or services,a web of massive infrastructures,which is aimed at achieving maximum utilization with higher availability at a minimized cost.One of the most attractive applications for cloud computing is the concept of distributed information processing.Security,privacy,energy saving,reliability and load balancing are the major challenges facing cloud computing and most information technology innovations.Load balancing is the process of redistributing workload among all nodes in a network;to improve resource utilization and job response time,while avoiding overloading some nodes when other nodes are underloaded or idle is a major challenge.Thus,this research aims to design a novel load balancing systems in a cloud computing environment.The research is based on the modification of the existing approaches,namely;particle swarm optimization(PSO),honeybee,and ant colony optimization(ACO)with mathematical expression to form a novel approach called PACOHONEYBEE.The experiments were conducted on response time and throughput.The results of the response time of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:2791,2780,2784,2767,2727,and 2599(ms)respectively.The outcome of throughput of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:7451,7425,7398,7357,7387 and 7482(bps)respectively.It is observed that P-ACOHONEYBEE approach produces the lowest response time,high throughput and overall improved performance for the 10 nodes.The research has helped in managing the imbalance drawback by maximizing throughput,and reducing response time with scalability and reliability.

A load balance optimization framework for sharded-blockchain enabled Internet of Things

Recently,sharded-blockchain has attracted more and more attention.Its inherited immutabili-ty,decentralization,and promoted scalability effectively address the trust issue of the data sharing in the Internet of Things(IoT).Nevertheless,the traditional random allocation between validator groups and transaction pools ignores the differences of shards,which reduces the overall system per-formance due to the unbalance between computing capacity and transaction load.To solve this prob-lem,a load balance optimization framework for sharded-blockchain enabled IoT is proposed,where the allocation between the validator groups and transaction pools is implemented reasonably by deep reinforcement learning(DRL).Specifically,based on the theoretical analysis of the intra-shard consensus and the final system consensus,the optimization of system performance is formed as a Markov decision process(MDP),and the allocation of the transaction pools,the block size,and the block interval are jointly trained in the DRL agent.The simulation results show that the proposed scheme improves the scalability of the sharded blockchain system for IoT.

Long-Term Assessment of Nitrogen Pollution Load Potential for Groundwater by Mass Balance Analysis in the Tedori River Alluvial Fan Area, Japan

To evaluate the nitrogen pollution load in an aquifer, a water and nitrogen balance analysis was conducted over a thirty-five year period at five yearly intervals. First, we established a two-horizon model comprising a channel/soil horizon, and an aquifer horizon, with exchange of water between the aquifer and river. The nitrogen balance was estimated from the product of nitrogen concentration and water flow obtained from the water balance analysis. The aquifer nitrogen balance results were as follows: 1) In the aquifer horizon, the total nitrogen pollution load potential (NPLP) peaked in the period 1981-1990 at 1800 t·yr-1;following this the NPLP rapidly decreased to about 600 t·yr-1 in the period 2006-2010. The largest NPLP input component of 1000 t·yr-1 in the period 1976-1990 was from farmland. Subsequently, farmland NPLP decreased to only 400 t·yr-1 between 2006 and 2010. The second largest input component, 600 t·yr-1, was effluent from wastewater treatment works (WWTWs) in the period 1986-1990;this also decreased markedly to about 100 t·yr-1 between 2006 and 2010;2) The difference between input and output in the aquifer horizon, used as an index of groundwater pollution, peaked in the period 1986-1990 at about 1200 t·yr-1. This gradually decreased to about 200 t·yr-1 by 2006-2010. 3) The temporal change in NPLP coincided with the nitrogen concentration of the rivers in the study area. In addition, nitrogen concentrations in two test wells were 1.0 mg·l-1 at a depth of 150 m and only 0.25 mg·l-1 at 50 m, suggesting gradual percolation of the nitrogen polluted water deeper in the aquifer.

A Load-balance and EEDF based strategy for reducing transmission delay of VoIP services in IEEE 802.16e system

To decrease the transmission delay of uplink voice over IP(VoIP)services in IEEE 802.16e sys-tem,a novel strategy which includes a load-balance algorithm and an extended earliest deadline first(EEDF)scheduling algorithm is proposed.Subsequently,this paper analyzes the performance of the pro-posed strategy in terms of transmission delay of VoIP services,system capacity,throughput and compati-bility with IEEE 802 .16e standard.Finally,simulation experiments are carried out to verify the improve-ment of the proposed strategy.The simulation results match well with the theoretical analysis and showthat the proposed strategy reduces the transmission delay of uplink VoIP services and improves the capaci-ty and throughput.These improvements are remarkable especially when the load of system is heavy.