This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism call...This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness(DRBF), which generalizes Bottleneck-aware Allocation(BAA) to the settings of Dominant Resource Fairness(DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and(4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.展开更多
In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottlene...In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.展开更多
To mitigate interference on celledge users and improve fairness of the whole system, dynamic inter-cell interference coordination(ICIC) is one of the promising solutions. However, traditional dynamic ICIC is considere...To mitigate interference on celledge users and improve fairness of the whole system, dynamic inter-cell interference coordination(ICIC) is one of the promising solutions. However, traditional dynamic ICIC is considered as an NP-hard problem and power variability further adds another dimension to this joint optimization issue, making it even more difficult to quickly reach a near-optimal solution. Therefore, we theoretically obtain the closed-form expression of the near-optimal power allocation ratio for users in adjacent cells paired in the same resource block and interfere each other, so that the total utility corresponding to α-fairness is maximized. Dynamic ICIC using this closed-form solution could improve user fairness without causing an increment of the computational complexity. Numerical results show that, compared with the schemes using identical power for different users, our method does not obviously degrade the system's average spectral efficiency.展开更多
To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlin...To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.展开更多
A resource allocation problem considering both efficiency and fairness in orthogonal frequency division multiple access （OFDMA） systems is studied. According to the optimality conditions, a downlink resource allocat...A resource allocation problem considering both efficiency and fairness in orthogonal frequency division multiple access （OFDMA） systems is studied. According to the optimality conditions, a downlink resource allocation algorithm consisting of subcarrier assignment and power alloca- tion is proposed. By adjusting the tradeoff coefficient, the proposed algorithm can achieve different levels of compromise between efficiency and fairness. The well-known classic resource allocation policies such as sum-rate maximization algorithm, proportional fairness algorithm and max-rain algorithm are all special cases of the proposed algorithm. Simulation results show that the compromise between efficiency and fairness can be continuously adjusted according to system requirements.展开更多
This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new netw...This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new network model. With this net-work model, the multi-channel wireless network is divided into several subnets according to the num-ber of channels. Based on this, we present a link allocation algorithm with time complexity O（l^2）to al-locate all links to subnets. This link allocation algo-rithm adopts conflict matrix to minimize the network contention factor. After all links are allocated to subnets, the rate assignment algorithm to maximize a fairness utility in each subnet is presented. The rate assignment algorithm adopts a near-optirml al-gorithm based on dual decomposition and realizes in a distributed way. Simulation results demonstrate that, compared with IEEE 802.11b and slotted see-ded channel hopping algorithm, our algorithm de-creases network conflicts and improves the net-work throughput significantly.展开更多
The spectral efficiency(SE)and energy efficiency(EE)tradeoff while ensuring rate fairness among users in non-orthogonal multiple access(NOMA)systems is investigated.In order to characterize the SE-EE tradeoff with rat...The spectral efficiency(SE)and energy efficiency(EE)tradeoff while ensuring rate fairness among users in non-orthogonal multiple access(NOMA)systems is investigated.In order to characterize the SE-EE tradeoff with rate fairness,a multi-objective optimization(MOO)problem is first formulated,where the rate fairness is represented with theα-fair utility function.Then,the MOO problem is converted into a single-objective optimization(SOO)problem by the weighted sum method.To solve the converted non-convex SOO problem,we apply sequential convex programming,which helps to propose a general power allocation algorithm to realize the SE-EE tradeoff with rate fairness.We prove the convergence of the proposed algorithm and the convergent solution satisfies the KKT conditions.Simulation results demonstrate the proposed power allocation algorithm can achieve various levels of rate fairness,and higher fairness results in degraded performance of SE-EE tradeoff.A pivotal conclusion is reached that NOMA systems significantly outperform orthogonal multiple access systems in terms of SE-EE tradeoff with the same level of rate fairness.展开更多
With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) ...With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.展开更多
针对NOMA-VLC系统中固定功率分配(Fixed Power Allocation,FPA)方法存在用户间干扰严重问题导致通信可靠性差,迭代注水功率分配(Iterative Water-filling Power Allocation,IWPA)方法存在难以保证用户公平性问题,提出基于信道容量的功...针对NOMA-VLC系统中固定功率分配(Fixed Power Allocation,FPA)方法存在用户间干扰严重问题导致通信可靠性差,迭代注水功率分配(Iterative Water-filling Power Allocation,IWPA)方法存在难以保证用户公平性问题,提出基于信道容量的功率分配方法,首先对多用户场景下的系统模型进行分析;在此基础上,基于迭代优化的方式进行发送端用户功率分配,达到信道容量的目标函数,保证系统可靠性和用户公平性;最后建立实验平台,通过蒙特卡洛实验对系统性能进行分析,实验结果表明:在三种不同的调制格式下,文中方法相较于FPA方法系统平均获得10 dB以上性能增益,相较于IWPA方法系统保证两用户可靠通信;随着前端调制阶次的提高,文中方法有效降低SNR需求,减小用户间的性能差异,使用户公平性得到良好保证。展开更多
为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决...为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决斗网络(dueling network,DN)结构以克服动态环境的部分可观测问题,联合优化了UAV-BS的位置和下行链路功率分配,在更符合实际的空地概率信道模型中检验了Dueling-DQN算法的性能。结果表明,相较于对比算法,所提出的Dueling-DQN算法可以提供更高的数据速率和服务公平性,且随着地面用户数量的增大,算法的优势更加明显。Dueling-DQN算法可有效解决复杂非凸性问题,为UAV-BS的资源分配问题提供理论参考。展开更多
基金financial support of the Oversea Study Program of the Guangzhou Elite Project(GEP)supported by the National Natural Science Foundation of China under Grant 61471173Guangdong Science Technology Project(no:2017A010101027)
文摘This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness(DRBF), which generalizes Bottleneck-aware Allocation(BAA) to the settings of Dominant Resource Fairness(DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and(4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.
基金supported in part by Program for Changjiang Scholars and Innovative Research Team in University No.IRT1078The Key Program of NSFC-Guangdong Union Foundation No.U1135002The Fundamental Research Funds for the Central Universities No.JY0900120301
文摘In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.
基金supported by the National Natural Science Foundation of China under Grant No. 61501160supported by the Fundamental Research Funds for the Central Universities of China under Grant No. 2015HGCH0013
文摘To mitigate interference on celledge users and improve fairness of the whole system, dynamic inter-cell interference coordination(ICIC) is one of the promising solutions. However, traditional dynamic ICIC is considered as an NP-hard problem and power variability further adds another dimension to this joint optimization issue, making it even more difficult to quickly reach a near-optimal solution. Therefore, we theoretically obtain the closed-form expression of the near-optimal power allocation ratio for users in adjacent cells paired in the same resource block and interfere each other, so that the total utility corresponding to α-fairness is maximized. Dynamic ICIC using this closed-form solution could improve user fairness without causing an increment of the computational complexity. Numerical results show that, compared with the schemes using identical power for different users, our method does not obviously degrade the system's average spectral efficiency.
基金supported by the National Natural Science Foundation of China(No.62071354)the Key Research and Development Program of Shaanxi(No.2022ZDLGY05-08)supported by the ISN State Key Laboratory。
文摘To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.
文摘A resource allocation problem considering both efficiency and fairness in orthogonal frequency division multiple access （OFDMA） systems is studied. According to the optimality conditions, a downlink resource allocation algorithm consisting of subcarrier assignment and power alloca- tion is proposed. By adjusting the tradeoff coefficient, the proposed algorithm can achieve different levels of compromise between efficiency and fairness. The well-known classic resource allocation policies such as sum-rate maximization algorithm, proportional fairness algorithm and max-rain algorithm are all special cases of the proposed algorithm. Simulation results show that the compromise between efficiency and fairness can be continuously adjusted according to system requirements.
基金This work was supported by the National Natural Science Foundation of China under Cxant No. 60902010 the Research Fund of State Key Laboratory of Mobile Communications un-der Crant No. 2012A03.
文摘This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new network model. With this net-work model, the multi-channel wireless network is divided into several subnets according to the num-ber of channels. Based on this, we present a link allocation algorithm with time complexity O（l^2）to al-locate all links to subnets. This link allocation algo-rithm adopts conflict matrix to minimize the network contention factor. After all links are allocated to subnets, the rate assignment algorithm to maximize a fairness utility in each subnet is presented. The rate assignment algorithm adopts a near-optirml al-gorithm based on dual decomposition and realizes in a distributed way. Simulation results demonstrate that, compared with IEEE 802.11b and slotted see-ded channel hopping algorithm, our algorithm de-creases network conflicts and improves the net-work throughput significantly.
基金Supported by the Fundamental Research Funds for the Central Universities(2016RC055)
文摘The spectral efficiency(SE)and energy efficiency(EE)tradeoff while ensuring rate fairness among users in non-orthogonal multiple access(NOMA)systems is investigated.In order to characterize the SE-EE tradeoff with rate fairness,a multi-objective optimization(MOO)problem is first formulated,where the rate fairness is represented with theα-fair utility function.Then,the MOO problem is converted into a single-objective optimization(SOO)problem by the weighted sum method.To solve the converted non-convex SOO problem,we apply sequential convex programming,which helps to propose a general power allocation algorithm to realize the SE-EE tradeoff with rate fairness.We prove the convergence of the proposed algorithm and the convergent solution satisfies the KKT conditions.Simulation results demonstrate the proposed power allocation algorithm can achieve various levels of rate fairness,and higher fairness results in degraded performance of SE-EE tradeoff.A pivotal conclusion is reached that NOMA systems significantly outperform orthogonal multiple access systems in terms of SE-EE tradeoff with the same level of rate fairness.
基金supported by China National S&T Major Project 2013ZX03003002003Beijing Natural Science Foundation No.4152047+1 种基金the 863 project No.2014AA01A701111 Project of China under Grant B14010
文摘With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.
文摘针对NOMA-VLC系统中固定功率分配(Fixed Power Allocation,FPA)方法存在用户间干扰严重问题导致通信可靠性差,迭代注水功率分配(Iterative Water-filling Power Allocation,IWPA)方法存在难以保证用户公平性问题,提出基于信道容量的功率分配方法,首先对多用户场景下的系统模型进行分析;在此基础上,基于迭代优化的方式进行发送端用户功率分配,达到信道容量的目标函数,保证系统可靠性和用户公平性;最后建立实验平台,通过蒙特卡洛实验对系统性能进行分析,实验结果表明:在三种不同的调制格式下,文中方法相较于FPA方法系统平均获得10 dB以上性能增益,相较于IWPA方法系统保证两用户可靠通信;随着前端调制阶次的提高,文中方法有效降低SNR需求,减小用户间的性能差异,使用户公平性得到良好保证。
文摘为了提高无人机基站(unmanned aerial vehicle base stations,UAV-BS)为地面多用户服务时的数据速率,提出一种基于决斗深度神经网络(dueling deep Q-network,Dueling-DQN)的深度强化学习(deep reinforcement learning,DRL)算法。采用决斗网络(dueling network,DN)结构以克服动态环境的部分可观测问题,联合优化了UAV-BS的位置和下行链路功率分配,在更符合实际的空地概率信道模型中检验了Dueling-DQN算法的性能。结果表明,相较于对比算法,所提出的Dueling-DQN算法可以提供更高的数据速率和服务公平性,且随着地面用户数量的增大,算法的优势更加明显。Dueling-DQN算法可有效解决复杂非凸性问题,为UAV-BS的资源分配问题提供理论参考。
