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 ...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.展开更多
If the draught of each mill stand is limited by forced bite condition for compact continuous mill,the rolling load difference between one mill stand and another is very big.If deforming regulation of relative load for...If the draught of each mill stand is limited by forced bite condition for compact continuous mill,the rolling load difference between one mill stand and another is very big.If deforming regulation of relative load for each mill stand is approximate to the same,the productive capacity of compact continuous mill can be brought into full play,and also the safety running and the smooth rolling of mill can be ensured.展开更多
Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency.A novel approach is introduced in this study to decrease a...Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency.A novel approach is introduced in this study to decrease a system's overall delay and energy consumption by using a deep reinforcement learning(DRL)model to predict and allocate incoming workloads flexibly.The proposed methodology integrates workload prediction utilising long short-term memory(LSTM)networks with efficient load-balancing techniques led by deep Q-learning and Actor-critic algorithms.By continuously analysing current and historical data,the model can efficiently allocate resources,prioritizing speed and energy preservation.The experimental results demonstrate that our load balancing system,which utilises DRL,significantly reduces average response times and energy usage compared to traditional methods.This approach provides a scalable and adaptable strategy for enhancing cloud infrastructure performance.It consistently provides reliable and durable performance across a range of dynamic workloads.展开更多
In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overl...In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.展开更多
The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss...The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss for some users.To combat this,the traffic load of base stations should be kept as balanced as possible.In this paper,we propose an efficient load balancing-aware handover algorithm for highly dynamic beyond 5G heterogeneous networks by assigning mobile users to base stations with lighter loads when a handover is performed.The proposed algorithm is evaluated in a scenario with users having different levels of mobility,such as pedestrians and vehicles,and is shown to outperform the conventional handover mechanism,as well as another algorithm from the literature.As a secondary benefit,the overall energy consumption in the network is shown to be reduced with the proposed algorithm.展开更多
In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,p...In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,parallel-connected independent-excitation direct-current(DC)motors for mud pumps,that are supplied by a single power converter.This configuration results in electrical power imbalance,thus reducing its efficiency.This paper investigates this power imbalance issue in such legacy DC mud pump drive systems and offers an innovative solution in the form of a closed-loop control system for electrical load balancing.The paper first analyzes the drilling fluid circulation and electrical drive layout to develop an analytical model that can be used for electrical load balancing and related energy efficiency improvements.Based on this analysis,a feedback control system(so-called“current mirror”control system)is designed to balance the electrical load(i.e.,armature currents)of parallel-connected DC machines by adjusting the excitation current of one of the DC machines,thus mitigating the power imbalance of the electrical drive.Theproposed control systemeffectiveness has been validated,first through simulations,followed by experimental testing on a deep drilling rig during commissioning and field tests.The results demonstrate the practical viability of the proposed“current mirror”control system that can effectively and rather quickly equalize the armature currents of both DC machines in a parallel-connected electrical drive,and thus balance both the electrical and mechanical load of individual DC machines under realistic operating conditions of the mud pump electrical drive.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
Geographic routing is a highly active area of research in mobile ad hoc networks （MANETs） owing to its efficiency and scalability. However,the use of simple greedy forwarding decreases the packet reception rate （PR...Geographic routing is a highly active area of research in mobile ad hoc networks （MANETs） owing to its efficiency and scalability. However,the use of simple greedy forwarding decreases the packet reception rate （PRR） dramatically in unreliable wireless environments; this also depresses the network lifetime. Therefore,it is important to improve delivery performance and prolong MANET lifetime simultaneously. In this article,a novel geographic routing algorithm,named energy-efficiency and load-loalanced geographic routing （ELGR）,is presented for lossy MANETs. ELGR combines energy efficiency and load balance to make routing decisions. First,a link estimation scheme for the PRR is presented that increases the network energy efficiency level. Second,a learning method is proposed to adaptively sense local network loads,allowing enhanced whole network load balance. The results of a simulation show that ELGR performs better than several other geographic routing algorithms; in particular it extends network lifetime by about 20%,with a higher delivery ratio.展开更多
In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features.The IoT has shown wide adoption in various appli...In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features.The IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,etc.Among these applications,fitness applications have been widely considered for smart fitness systems.The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities.Thus,scheduling such a huge number of requests for fitness exercise is a big challenge.Secondly,the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging.To overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches.The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource utilization.The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio.The obtained results confirm the efficiency of the proposed scheduling approach.For investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource utilization.The Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches respectively.The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.展开更多
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 ...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.展开更多
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(...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.展开更多
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...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.展开更多
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 dependenci...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.展开更多
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 ...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.展开更多
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...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.展开更多
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...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.展开更多
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...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.展开更多
To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve ...To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node （HLN）are spread along an MT whose root is the HLN. And the lightly loaded nodes（LLNs） covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.展开更多
To improve data distribution efficiency a load-balancing data distribution LBDD method is proposed in publish/subscribe mode.In the LBDD method subscribers are involved in distribution tasks and data transfers while r...To improve data distribution efficiency a load-balancing data distribution LBDD method is proposed in publish/subscribe mode.In the LBDD method subscribers are involved in distribution tasks and data transfers while receiving data themselves.A dissemination tree is constructed among the subscribers based on MD5 where the publisher acts as the root. The proposed method provides bucket construction target selection and path updates furthermore the property of one-way dissemination is proven.That the average out-going degree of a node is 2 is guaranteed with the proposed LBDD.The experiments on data distribution delay data distribution rate and load distribution are conducted. Experimental results show that the LBDD method aids in shaping the task load between the publisher and subscribers and outperforms the point-to-point approach.展开更多
This paper focuses on solving a problem of improving system robustness and the efficiency of a distributed system at the same time. Fault tolerance with active replication and load balancing techniques are used. The p...This paper focuses on solving a problem of improving system robustness and the efficiency of a distributed system at the same time. Fault tolerance with active replication and load balancing techniques are used. The pros and cons of both techniques are analyzed, and a novel load balancing framework for fault tolerant systems with active replication is presented. Hierarchical architecture is described in detail. The framework can dynamically adjust fault tolerant groups and their memberships with respect to system loads. Three potential task scheduler group selection methods are proposed and simulation tests are made. Further analysis of test data is done and helpful observations for system design are also pointed out, including effects of task arrival intensity and task set size, relationship between total task execution time and single task execution time.展开更多
文摘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.
文摘If the draught of each mill stand is limited by forced bite condition for compact continuous mill,the rolling load difference between one mill stand and another is very big.If deforming regulation of relative load for each mill stand is approximate to the same,the productive capacity of compact continuous mill can be brought into full play,and also the safety running and the smooth rolling of mill can be ensured.
文摘Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency.A novel approach is introduced in this study to decrease a system's overall delay and energy consumption by using a deep reinforcement learning(DRL)model to predict and allocate incoming workloads flexibly.The proposed methodology integrates workload prediction utilising long short-term memory(LSTM)networks with efficient load-balancing techniques led by deep Q-learning and Actor-critic algorithms.By continuously analysing current and historical data,the model can efficiently allocate resources,prioritizing speed and energy preservation.The experimental results demonstrate that our load balancing system,which utilises DRL,significantly reduces average response times and energy usage compared to traditional methods.This approach provides a scalable and adaptable strategy for enhancing cloud infrastructure performance.It consistently provides reliable and durable performance across a range of dynamic workloads.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFB2902501the Fundamental Research Funds for the Central Universities under Grant No.2023ZCJH09the Haidian District Golden Bridge Seed Fund of Beijing Municipality under Grant No.S2024161.
文摘In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.
基金supported in part by the Istanbul Technical University Scientific Research Projects Coordination Unit under Grant FHD-2024-45764in part by TUBITAK 1515 Frontier R&D Laboratories Support Program for Turkcell 6GEN LAB under Grant 5229902Turkcell Technology R&D Center(Law no.5746)has partially supported this study。
文摘The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss for some users.To combat this,the traffic load of base stations should be kept as balanced as possible.In this paper,we propose an efficient load balancing-aware handover algorithm for highly dynamic beyond 5G heterogeneous networks by assigning mobile users to base stations with lighter loads when a handover is performed.The proposed algorithm is evaluated in a scenario with users having different levels of mobility,such as pedestrians and vehicles,and is shown to outperform the conventional handover mechanism,as well as another algorithm from the literature.As a secondary benefit,the overall energy consumption in the network is shown to be reduced with the proposed algorithm.
文摘In deep drilling applications,such as those for geothermal energy,there are many challenges,such as those related to efficient operation of the drilling fluid(mud)pumping system.Legacy drilling rigs often use paired,parallel-connected independent-excitation direct-current(DC)motors for mud pumps,that are supplied by a single power converter.This configuration results in electrical power imbalance,thus reducing its efficiency.This paper investigates this power imbalance issue in such legacy DC mud pump drive systems and offers an innovative solution in the form of a closed-loop control system for electrical load balancing.The paper first analyzes the drilling fluid circulation and electrical drive layout to develop an analytical model that can be used for electrical load balancing and related energy efficiency improvements.Based on this analysis,a feedback control system(so-called“current mirror”control system)is designed to balance the electrical load(i.e.,armature currents)of parallel-connected DC machines by adjusting the excitation current of one of the DC machines,thus mitigating the power imbalance of the electrical drive.Theproposed control systemeffectiveness has been validated,first through simulations,followed by experimental testing on a deep drilling rig during commissioning and field tests.The results demonstrate the practical viability of the proposed“current mirror”control system that can effectively and rather quickly equalize the armature currents of both DC machines in a parallel-connected electrical drive,and thus balance both the electrical and mechanical load of individual DC machines under realistic operating conditions of the mud pump electrical drive.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China (60921001)National Science Foundation for Distinguished Young Scholars of China (60625102)National Natural Science Foundation of China (60972007)
文摘Geographic routing is a highly active area of research in mobile ad hoc networks （MANETs） owing to its efficiency and scalability. However,the use of simple greedy forwarding decreases the packet reception rate （PRR） dramatically in unreliable wireless environments; this also depresses the network lifetime. Therefore,it is important to improve delivery performance and prolong MANET lifetime simultaneously. In this article,a novel geographic routing algorithm,named energy-efficiency and load-loalanced geographic routing （ELGR）,is presented for lossy MANETs. ELGR combines energy efficiency and load balance to make routing decisions. First,a link estimation scheme for the PRR is presented that increases the network energy efficiency level. Second,a learning method is proposed to adaptively sense local network loads,allowing enhanced whole network load balance. The results of a simulation show that ELGR performs better than several other geographic routing algorithms; in particular it extends network lifetime by about 20%,with a higher delivery ratio.
基金This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)this research was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2018-0-01456,AutoMaTa:Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT).Any correspondence related to this paper should be addressed to Do-hyeun Kim.Conflicts of Interest:The auth。
文摘In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features.The IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,etc.Among these applications,fitness applications have been widely considered for smart fitness systems.The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities.Thus,scheduling such a huge number of requests for fitness exercise is a big challenge.Secondly,the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging.To overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches.The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource utilization.The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio.The obtained results confirm the efficiency of the proposed scheduling approach.For investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource utilization.The Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches respectively.The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.
文摘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.
文摘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.
文摘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.
基金funded by the Science and Technology Foundation of State Grid Corporation of China(Grant No.5108-202218280A-2-397-XG).
文摘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.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807003in part by the National Natural Science Foundation of China under Grants 61901381,62171385,and 61901378+3 种基金in part by the Aeronautical Science Foundation of China under Grant 2020z073053004in part by the Foundation of the State Key Laboratory of Integrated Services Networks of Xidian University under Grant ISN21-06in part by the Key Research Program and Industrial Innovation Chain Project of Shaanxi Province under Grant 2019ZDLGY07-10in part by the Natural Science Fundamental Research Program of Shaanxi Province under Grant 2021JM-069.
文摘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.
文摘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.
文摘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.
文摘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.
基金The National Natural Science Foundation of China(No.69973007).
文摘To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node （HLN）are spread along an MT whose root is the HLN. And the lightly loaded nodes（LLNs） covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.
基金The National Key Basic Research Program of China(973 Program)
文摘To improve data distribution efficiency a load-balancing data distribution LBDD method is proposed in publish/subscribe mode.In the LBDD method subscribers are involved in distribution tasks and data transfers while receiving data themselves.A dissemination tree is constructed among the subscribers based on MD5 where the publisher acts as the root. The proposed method provides bucket construction target selection and path updates furthermore the property of one-way dissemination is proven.That the average out-going degree of a node is 2 is guaranteed with the proposed LBDD.The experiments on data distribution delay data distribution rate and load distribution are conducted. Experimental results show that the LBDD method aids in shaping the task load between the publisher and subscribers and outperforms the point-to-point approach.
文摘This paper focuses on solving a problem of improving system robustness and the efficiency of a distributed system at the same time. Fault tolerance with active replication and load balancing techniques are used. The pros and cons of both techniques are analyzed, and a novel load balancing framework for fault tolerant systems with active replication is presented. Hierarchical architecture is described in detail. The framework can dynamically adjust fault tolerant groups and their memberships with respect to system loads. Three potential task scheduler group selection methods are proposed and simulation tests are made. Further analysis of test data is done and helpful observations for system design are also pointed out, including effects of task arrival intensity and task set size, relationship between total task execution time and single task execution time.
