A New Multi-Resource Allocation Mechanism： A Tradeoff between Fairness and Efficiency in Cloud Computing

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.

Resource allocation based on fairness and QoS provisioning for OFDMA-WLAN system

To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm based on fairness and quality of service QoS provisioning is proposed. Different QoS requirements are converted into different rate requirements to calculate the QoSs atisfaction level.The optimization object is revised as a fairness-driven resource optimization function to provide fairness. The complex resource allocation problem is divided into channel allocation and power assignment sub-problems. The sub-problems are solved by the bipartite graph matching and water-filling based method.Compared with other algorithms the proposed algorithm sacrifices less data rate for higher fairnes and QoS satisfaction.The sim ulation results show that the proposed algorithm is capableo fp rovi ding QoS and fairness and performs better in a tradeoff among QoS fairness and data rate.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

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.

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

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.

A Method for Assessing the Fairness of Health Resource Allocation Based on Geographical Grid

The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development.The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling.These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model.In response to these problems,the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area,which are more accurate and efficient.Based on the query statistics of hot words in the geographic grids,this paper adopts the maximum likelihood estimation method to estimate the population in the grid region.Then,according to the statistical yearbook data of Hunan province,the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region.Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value.The estimated average error is only about 17.8 percent.This method can assess the fairness of health resource allocation in any scale,and is innovative in data acquisition and evaluation methods.

Dynamic Interference Coordination With Analytical Near-Optimal Power Allocation Toward High User Fairness

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.

Utility-based and fairness-aware radio resource allocation in OFDMA cellular relay networks with traffic prioritization

The combination of orthogonal frequency division multiple access(OFDMA) with relaying techniques provides plentiful opportunities for high-performance and cost-effective networks.It requires intelligent radio resource management schemes to harness these opportunities.This paper investigates the utility-based resource allocation problem in a real-time and non-real-time traffics mixed OFDMA cellular relay network to exploit the potentiality of relay.In order to apply utility theory to obtain an efficient tradeoff between throughput and fairness as well as satisfy the delay requirements of real-time traffics,a joint routing and scheduling scheme is proposed to resolve the resource allocation problem.Additionally,a low-complexity iterative algorithm is introduced to realize the scheme.The numerical results indicate that besides meeting the delay requirements of real-time traffic,the scheme can achieve the tradeoff between throughput and fairness effectively.

Fairness Aware Group Proportional Frequency Domain Resource Allocation in L-SC-FDMA Based Uplink

This paper presents virtual clusters based proportional fairness and resource allocation scheme for Localized Single Carrier Frequency Division Multiple Access (L-SC-FDMA). L-SC-FDMA has been selected as the uplink transmission scheme in 3GPP Long Term Evolution (LTE) due to its low Peak to Average Power Ratio (PAPR) over OFDMA in general and high rate-sum capacity over Interleaved SC-FDMA in particular. Virtual cluster-based proportional fairness (VCPF) scheduler exploits the link adaptation information available at MAC layer to form virtual clusters. The distributed proportional fairness scheduler ensures a minimum throughput for all users in the coverage area by assigning contiguous RBs, proportional to the throughput and the number of users in a particular cluster or group. Simulations have been performed using practical scenario of uniformly distributed users in Rayleigh faded coverage area and design formulas have been devised for network planning to get the best possible fairness with promising level of quality of service (QoS).

Resource Allocation for OFDMA-MIMO Relay Systems with Proportional Fairness Constraints

In this paper, we study resource allocation problem in orthogonal frequency division multiple access multiple-input multiple-output (OFDMA-MIMO) relay systems and formulate the optimal instantaneous resource allocation problem including subcarrier assignment, relay selection and power allocation to maximize system capacity. Based on the assumption that the availability of perfect channel state information (CSI) is known at the resource allocation controller, we propose a new resource allocation algorithm which can guarantee proportional fairness among users. In the proposed algorithm, a two-step suboptimal method is taken into account. Firstly, we assume equal power allocation for each user to linearize the problem and propose the subcarrier assignment and relay selection scheme based on equivalent channel gain. Secondly, we derive the closed-form expressions for power allocation through relaxing the proportional fairness constraints. Numerical simulations show that the proposed algorithm performs well in terms of satisfying proportional fairness among users in strict sense and achieving improvement on system total capacity.

Capacity and Fairness Maximization-Based Resource Allocation for Downlink NOMA Networks

Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA systems.This paper presents a novel UP and PA(UPPA)technique for capacity and fairness maximization in NOMA called(CFM-UPPA).The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated.Then,based on this investigation,the PA and UP algorithms of the CFM-UPPA technique are proposed.The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness,and its maximum value is adjusted to guarantee the successive interference cancellation(SIC)constraints.The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity.The performance evaluation of the proposed CFM-UPPA technique in terms of capacity,fairness,and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access(OMA)system and that of the NOMA system with random UP.Also,the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms,such as the random UP algorithm.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

Resource allocation for load balancing and fairness improvement in relay-assisted OFDMA cellular

A resource allocation scheme with the considerations of user fairness and load balancing is proposed in orthogonal frequency division multiple access （OFDMA）-based relay networks. The optimal resource allocation scheme is formulated mathematically to maximize the minimum achievable rate among all user equipments （UEs） for fairnegs improvement. The optimal problem has been proved to be N-P-hard and it is prohibitive to find the optimal solution for its computational complexity. Accordingly, this paper proposes a suboptimal scheme which considers not only user fairness but also load balancing among base stations （BS） and relay stations （RSs） during resource allocation procedure. The suboptimal scheme takes the traffic load of access nodes （BS and RSs） into consideration to balance the system traffic load, which would prevent them from overloading and throughput degradation. Simulation results show that the suboptimal scheme performs similarly to the optimal solution and can enhance the system fairness and load balancing performance significantly compared with the traditional schemes.

Fair subcarrier-power allocation scheme for multiuser multicarrier systems

The main objective of multiuser orthogonal frequency division multiple access(MU-OFDM) is to maximize the total system capacity in wireless communication systems. Thus, the problem in MU-OFDM system is the adaptive allocation of the resources(subcarriers, bits and power) to different users subject to several restrictions to maximize the total system capacity. In this work, a proposed subcarrier allocation algorithm was presented to assign the subcarriers with highest channel gain to the users. After the subcarrier allocation, subcarrier gain-based power allocation(SGPA) was employed for power and bit loading. The simulation results show that the proposed subcarrier-power allocation scheme can achieve high total system capacity and good fairness in allocating the resources to the users with slightly high computational complexity compared to the existing subcarrier allocation algorithms.

Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence,the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment.TheKuramoto model is described as nonlinear selfsustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength,in which a mutual behavior is accomplished.In this work,we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network,where each user has different quality of service(QoS)requirements.Because the original Kuramoto model is the synchronization model,we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieveweighted fair resource allocation for users with different QoS requirements.The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner.We analyze the convergence condition for the proposed model,and the results reveal that the proposed algorithm achieves aweighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.

Resource Allocation to Minimize the Makespan with Multi-Resource Operations

This paper investigates a new resource-allocation problem involving multi-resource operations,where completing an operation requires simultaneous use of multiple(renewable)resources,probably of different types.The goal of the study is to provide a solution method that minimizes the makespan.The authors formulate the problem into a novel mixed-integer linear program(MILP)model.To efficiently solve practical-sized instances,an exact Benders decomposition algorithm is developed.This algorithm divides the original problem into a master problem of allocating resources and a subproblem of calculating the makespan,and both are linked via Benders cuts.The convergence is sped up by improving the mathematical model and embedding the variable neighborhood search algorithm.Compared with CPLEX,a commonly used MILP solver,the computational results demonstrate that the proposed algorithm provides tighter upper and lower bounds in most instances.In particular,compared with CPLEX,the proposed method can on average improve the upper and lower bounds by 4.76%and 4.39%,respectively,in solving practical-sized instances.

Efficient and fair resource allocation in downlink OFDMA systems

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.

DISTRIBUTED PROPORTIONAL FAIR FREQUENCY ALLOCATION ACROSS MULTIPLE BASE STATIONS

The problem of distributed proportional fair inter-cell frequency allocation for flat-structured cellular systems is studied in this paper. We firstly propose a framework of the frequency allocation in which the whole frequency allocation process is decomposed into many consecutive stages, then identify that for each stage the key is to find the Maximum Weight Independent Set (MWIS) in a given weighted conflict graph in the distributed manner. A new distributed algorithm for MWIS is described in which each node iteratively exchanges messages with neighbors. With this distributed MWIS algorithm, a new distributed proportional fair frequency allocation scheme is presented. The performance of the proposed algorithm is tested in computer experiments simulating the Long Term Evolution (LTE) cellular systems. Simulation results show the performance of the proposed distributed proportional fair frequency allocation scheme is comparable with the centralized ones.

PERFORMANCE ANALYSIS OF USER FAIR-BASED RELAY ADAPTIVE POWER ALLOCATION IN PHYSICAL-LAYER NETWORK CODING COOPERATION

Network Coding (NC) is an effective technology to enhance the cooperative system spectral efficiency. However, since it is network-oriented, the existing performance metric of single-user outage can not comprehensively evaluate its gain and the impact to the entire network, which affect the user fairness. This paper proposes two novel user fair-based adaptive relay power allocation algorithms in single-relay NC cooperative multiple access channels. Firstly, common outage probability is employed as the performance metric, and to minimize it, a specific condition is deduced. On this basis, the instantaneous channel information-based adaptive relay power allocation scheme and the channel statistic information-based one with lower complexity are designed respectively, which make users' signals superimposed at accurately calculated proportion to maintain fairness. Simulation results show that compared with other existing schemes, the proposed schemes can best maintain user fairness, and effectively improve the common outage performance of the whole system, at the expense of small spectral efficiency.

Dynamic bandwidth allocation algorithm for full-band utilization

To improve and optimize the bandwidth utilization for multi-service packet transporting system, a kind of Dynamic Full Bandwidth Utilized （DFBU） allocation algorithm allowing a single link to use far beyond its fair share bandwidth is presented. Three important parameters as the bound on max and minimum bandwidth, the maximum packet delay and the minimum bandwidth utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system use all the spare bandwidth （up to full-bandwidth） while the performance of fairness and QoS requirement is still guaranteed.