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
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
To aware the topology of wireless sensor networks (WSN) with time-variety, and load-balance the resource of communication and energy, an opportunistic routing protocol for WSN based on Opportunistic Routing Entropy an...To aware the topology of wireless sensor networks (WSN) with time-variety, and load-balance the resource of communication and energy, an opportunistic routing protocol for WSN based on Opportunistic Routing Entropy and ant colony optimization, called ACO-TDOP, is proposed. At first, based on the second law of thermo-dynamics, we introduce the concept of Opportunistic Routing Entropy which is a parameter representing the transmission state of each node by taking into account the power left and the distance to the sink node. Then, it is proved that the problem of route thinking about Opportunistic Routing Entropy is shown to be NP-hard. So the protocol, ACO-TDOP, is proposed. At last, numerical results confirm that the ACO-TDOP is energy conservative and throughput gainful compared with other two existing routing protocols, and show that it is efficacious to analyze and uncover fundamental of message transmission with Opportunistic Routing in wireless network using the second law of thermodynamics.展开更多
[Objective] The aim was to research balance between manure of breeding farm and farmland load. [Method] N balance of farmlands after manure fertilized was researched in a hoggery in Beijing （600 base sows and 647 bre...[Objective] The aim was to research balance between manure of breeding farm and farmland load. [Method] N balance of farmlands after manure fertilized was researched in a hoggery in Beijing （600 base sows and 647 breeding pigs）. [Result] The suitable N input in neighboring farmlands was 191.9 kg/（hm 2 ·y） and the farmland area satisfying balance between growing and breeding was 53.7 hm 2 in the hoggery. [Conclusion] The research provided a scientific method for balance between growing and breeding.展开更多
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 ...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.展开更多
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 contr...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.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
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
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 consumptio...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.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
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
The optimization of high density and concentrated-weight freights loading requires an even distribution of the freight's weight and unconcentrated loading on the floor of the car.Based on the characteristics of co...The optimization of high density and concentrated-weight freights loading requires an even distribution of the freight's weight and unconcentrated loading on the floor of the car.Based on the characteristics of concentrated-weight category freights,an improvement method is put forward to build freight towers and a greedy-construction algorithm is utilized based on heuristic information for the initial layout.Then a feasibility analysis is performed to judge if the balanced and unconcentrated loading constrains are reached.Through introducing optimization or adjustment methods,an overall optimal solution can be obtained.Experiments are conducted using data generated from real cases showing the effectiveness of our approach: volume utility ratio of 90.4% and load capacity utility ratio of 86.7% which is comparably even to the packing of the general freights.展开更多
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 algorit...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.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
A direct comparison of urban and rural surface energy balances, as well as a variety of other variables including incoming shortwave/longwave radiation and aerosol optical depth, is conducted for the Beijing metropoli...A direct comparison of urban and rural surface energy balances, as well as a variety of other variables including incoming shortwave/longwave radiation and aerosol optical depth, is conducted for the Beijing metropolitan area. The results indicate that, overall, the urban area receives a smaller amount of incoming shortwave radiation but a larger amount of incoming longwave radiation. However, comparisons in the aerosol optical depth and cloud fraction at the two locations suggest that neither aerosol optical depth nor cloud fraction alone can explain the difference in the incoming shortwave radiation. The urban–rural differences in the incoming longwave radiation are unlikely to be caused by the presence of more abundant greenhouse gases over the urban area, as suggested by some previous studies, given that water vapor is the most dominant greenhouse gas and precipitable water is found to be less in urban areas. The higher incoming longwave radiation observed over the urban area is mostly likely due to the higher temperatures of the ambient air. The urban area is also found to always produce higher sensible heat fluxes and lower latent heat fluxes in the growing season. Furthermore, the urban area is associated with a larger amount of available energy（the sum of sensible and latent heat fluxes） than the rural area, except in May and October when evapotranspiration in the rural area significantly exceeds that in the urban area. This study provides observational evidence of urban–rural contrasts in relevant energy-balance components that plausibly arise from urban–rural differences in atmospheric and land-surface conditions.展开更多
High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation ba...High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.展开更多
One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consider...One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.展开更多
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 a...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.展开更多
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To aware the topology of wireless sensor networks (WSN) with time-variety, and load-balance the resource of communication and energy, an opportunistic routing protocol for WSN based on Opportunistic Routing Entropy and ant colony optimization, called ACO-TDOP, is proposed. At first, based on the second law of thermo-dynamics, we introduce the concept of Opportunistic Routing Entropy which is a parameter representing the transmission state of each node by taking into account the power left and the distance to the sink node. Then, it is proved that the problem of route thinking about Opportunistic Routing Entropy is shown to be NP-hard. So the protocol, ACO-TDOP, is proposed. At last, numerical results confirm that the ACO-TDOP is energy conservative and throughput gainful compared with other two existing routing protocols, and show that it is efficacious to analyze and uncover fundamental of message transmission with Opportunistic Routing in wireless network using the second law of thermodynamics.
文摘[Objective] The aim was to research balance between manure of breeding farm and farmland load. [Method] N balance of farmlands after manure fertilized was researched in a hoggery in Beijing （600 base sows and 647 breeding pigs）. [Result] The suitable N input in neighboring farmlands was 191.9 kg/（hm 2 ·y） and the farmland area satisfying balance between growing and breeding was 53.7 hm 2 in the hoggery. [Conclusion] The research provided a scientific method for balance between growing and breeding.
基金supported in part by the National Natural Science Foundation of China 62072096the Fundamental Research Funds for the Central Universities under Grant 2232020A-12+4 种基金the International S&T Cooperation Program of Shanghai Science and Technology Commission under Grant 20220713000the Young Top-notch Talent Program in Shanghaithe"Shuguang Program"of Shanghai Education Development Foundation and Shanghai Municipal Education Commissionthe Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University CUSF-DH-D-2019093supported in part by the NSF under grants CNS-2107190 and ECCS-1923717。
文摘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.
基金supported in part by the Project of National Network Cyberspace Security (Grant No.2017YFB0803204)the National High-Tech Research and Development Program of China (863 Program) (Grant No. 2015AA016102)+1 种基金Foundation for Innovative Research Group of the National Natural Science Foundation of China (Grant No.61521003)Foundation for the National Natural Science Foundation of China (Grant No. 61502530)
文摘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.
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.
文摘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.
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.
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.
基金Project(71371193)supported by the National Natural Science Foundation of ChinaProjects(2005K1001,2007K1005)supported by Guangzhou-Shenzhen Railway Company Limited,China
文摘The optimization of high density and concentrated-weight freights loading requires an even distribution of the freight's weight and unconcentrated loading on the floor of the car.Based on the characteristics of concentrated-weight category freights,an improvement method is put forward to build freight towers and a greedy-construction algorithm is utilized based on heuristic information for the initial layout.Then a feasibility analysis is performed to judge if the balanced and unconcentrated loading constrains are reached.Through introducing optimization or adjustment methods,an overall optimal solution can be obtained.Experiments are conducted using data generated from real cases showing the effectiveness of our approach: volume utility ratio of 90.4% and load capacity utility ratio of 86.7% which is comparably even to the packing of the general freights.
基金supported by the High Technology Research and Development Programme of China(No.2006AA01Z235)
文摘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.
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supported by the National Key Basic Research Program (Grant Nos. 2010CB428502 and 2012CB417203)the National Natural Science Foundation of China (Grant Nos. 41405018 and 41275022)+2 种基金the China Meteorological Administration (Grant No. GYHY201006024)the CAS Strategic Priority Research Program (Grant No. XDA05110101)the support of the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences (Grant No. LAPC-KF-2009-02)
文摘A direct comparison of urban and rural surface energy balances, as well as a variety of other variables including incoming shortwave/longwave radiation and aerosol optical depth, is conducted for the Beijing metropolitan area. The results indicate that, overall, the urban area receives a smaller amount of incoming shortwave radiation but a larger amount of incoming longwave radiation. However, comparisons in the aerosol optical depth and cloud fraction at the two locations suggest that neither aerosol optical depth nor cloud fraction alone can explain the difference in the incoming shortwave radiation. The urban–rural differences in the incoming longwave radiation are unlikely to be caused by the presence of more abundant greenhouse gases over the urban area, as suggested by some previous studies, given that water vapor is the most dominant greenhouse gas and precipitable water is found to be less in urban areas. The higher incoming longwave radiation observed over the urban area is mostly likely due to the higher temperatures of the ambient air. The urban area is also found to always produce higher sensible heat fluxes and lower latent heat fluxes in the growing season. Furthermore, the urban area is associated with a larger amount of available energy（the sum of sensible and latent heat fluxes） than the rural area, except in May and October when evapotranspiration in the rural area significantly exceeds that in the urban area. This study provides observational evidence of urban–rural contrasts in relevant energy-balance components that plausibly arise from urban–rural differences in atmospheric and land-surface conditions.
基金supported by National Science and Technology Support Program of China (Grant No. 2012BAF15G00)
文摘High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.
基金supported by Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry under Grant No.2010-2011 and Chinese Post-doctoral Research Foundation
文摘One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
基金supported by the National Natural Science Foundation of China (No.61401413)the Digital Home Industry Cluster Oriented Technology Service Innovation Pilot Project in 2015
文摘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.
