Is fair allocation always inefficient

It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if /spl alpha/ is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology.

In this paper, we consider the following dynamic fair allocation problem: Given a sequence of job arrivals and departures, the goal is to maintain an approximately fair allocation of the resource against a target fair allocation policy, while minimizing the total number of \em disruptions, which is the number of times the allocation of any job is changed. We consider a rich class of fair allocation policies that significantly generalize those considered in previous work. We first consider the models where jobs only arrive, or jobs only depart. We present tight upper and lower bounds for the number of disruptions required to maintain a constant approximate fair allocation every time step. In particular, for the canonical case where jobs have weights and the resource allocation is proportional to the job's weight, we show that maintaining a constant approximate fair allocation requires Θ(łog^* n) disruptions per job, almost matching the bounds in prior work for the unit weight case. For the more general setting where the allocation policy only decreases the allocation to a job when new jobs arrive, we show that maintaining a constant approximate fair allocation requires Θ(łog n) disruptions per job. We then consider the model where jobs can both arrive and depart. We first show strong lower bounds on the number of disruptions required to maintain constant approximate fairness for arbitrary instances. In contrast we then show that there there is an algorithm that can maintain constant approximate fairness with $O(1)$ expected disruptions per job if the weights of the jobs are independent of the jobs arrival and departure order. We finally show how our results can be extended to the setting with multiple resources.

In the Cellular Long-Term Evolution (LTE) downlink, the smallest radio resource unit a Scheduler can assign to a user is a Resource Block (RB). Each RB consists of twelve (12) adjacent Orthogonal Frequency Division Multiplexing (OFDM) sub-carriers with inter-subcarrier spacing of 15 kHz. Over the years, researchers have investigated the problem of radio resource allocation in cellular LTE downlink and have made useful contributions. In an earlier paper for example, we proposed a deterministic dynamic programming based technique for optimal allocation of RBs in the downlink of multiuser Cellular LTE System. We found that this proposed methodology optimally allocates RBs to users at every transmission instant, but the computational time associated with the allocation policy was high. In the current work, we propose a truncated dynamic programming based technique for efficient and optimal allocation of radio resource. This paper also addresses uncertainty emanating from users’ mobility within a Cell coverage area. The objective is to significantly reduce the computational time and dynamically select applicable modulation scheme (i.e., QPSK, 16QAM, or 64QAM) in response to users’ mobility. We compare the proposed scheme with the Fair allocation and the earlier proposed dynamic programming based techniques. It is shown that the proposed methodology is more efficient in allocating radio resource and has better performance than both the Fair Allocation and the deterministic dynamic programming based techniques.

We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. This framework can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new (counterfactual) allocation policies. Our work culminates with a set of incompatibility results that investigate the interplay between the different fairness metrics we propose. Notably, we demonstrate that: 1) fairness in allocation and fairness in outcomes are usually incompatible; 2) policies that prioritize based on a vulnerability score will usually result in unequal outcomes across groups, even if the score is perfectly calibrated; 3) policies using contextual information beyond what is needed to characterize baseline risk and treatment effects can be fairer in their outcomes than those using just baseline risk and treatment effects; and 4) policies using group status in addition to baseline risk and treatment effects are as fair as possible given all available information. Our framework can help guide the discussion among stakeholders in deciding which fairness metrics to impose when allocating scarce resources.

General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method.

In this paper a Network and Route Optimization Problem (NROP) in evacuation conditions is proposed. The variables involved in the problem are discrete (link topology, routes for rescue vehicles) and continuous (link capacity). The discrete variables are related to the link direction, lane numbers, lane use and sequence of users reached by rescue vehicles. The continuous variables, assuming that the link capacity is a function of the characteristics of the final node in the link, are related to the intersection control setting. The NROP is hence formulated as a mixed (discrete/continuous problem) in a congested network considering the ordinary demand and the evacuation demand. The network topology is optimized considering the users who evacuate on their own and the rescue vehicles that move to rescue some users. A heuristic procedure is used to find the best network configuration.

Network performance optimization is a poly-parametric task where usually the physical network topology is already provided and given. The latter is mainly defined by the geographical needs and the business models developed in a region that reflect the particular data traffic requirements. However, during the design of a new network, the physical topology can be considered as an additional parameter, and this work aims to study the potential impact of a network topology in the performance of the network. Two relevant metrics of optical WDM networks are evaluated, throughput and blocking ratio. Through the simulation of 350k implementations of more than 500 network topologies, we demonstrate a performance variation on networks with the same number of nodes and edges up to 73.9% on the total network load under same levels of requests blocking ratio, and up to 17.3% on the network throughput.

In this paper I present a simple and efficient queue management (EQM) technique based on optimal bandwidth allocation for networks. Each router calculates the bandwidth share (throughput) of each interfering flow at a link using link capacity and Interface IP Address (see RFC 3630) or round trip time (RTT) and congestion window size of the flows which are carried in the packets. The sources send packets and the routers in the path drop or allow the packets based on a certain probability and label the non-dropped packets with the allowable optimal throughput for the next round. Each router in the path modifies this label and the sources eventually adjust their sending rates based on the allowable throughput they get from the returning ACKs. In this way EQM finds the fair bandwidth allocation and gives fair queue management. I also a prove that EQM can converge to a stable point.

A network optimization model (NOM) that provides a tool for performance analysis and optimal design of a broad class of routing strategies is presented. It can evaluate and optimize both static and dynamic, local and global routing strategies. The tool is applicable to both packet-switched and circuit-switched networks. NOM software is based on original modeling methods and optimization algorithms. It is user-friendly and menu-driven, and has extensive 'what if' capabilities. NOM can be used for performance evaluation of existing routing strategy and for optimal routing design for a given performance criterion. As a performance evaluation tool, for a given network topology, link and node capacities, point-to-point demand matrix, and routing strategy, NOM models the routing and computes important characteristics of network performance. As a routing optimization tool, for a given network topology, link and node capacities, point-to-point demand matrix, and performance criterion, NOM generates the optimal routing, and computes important characteristics of network performance. The current Telenet local dynamic routing strategy is compared with a set of optimal routing strategies designed for various performance criteria. >

In wireless networks, the broadcast nature of wireless transmission enables cooperation by sharing the same transmissions with nearby receivers and thus can help improve spatial reuse and boost network throughput along a multi-hop routing. The performance of wireless networks can be further improved if prior information available at the receivers can be utilized to achieve perfect interference subtraction. In this paper, we investigate performance gain on network throughput for wireless cooperative networks by using a simple MUD scheme, called overlapped transmission, in which multiple transmissions are allowed only when the information in the interfering signal is known at the receiver. It is shown that the scheme of cooperative transmission with overlapping increases network throughput by 24% compared to that of direct transmission with overlapping. We then propose a new cooperation scheme called supplementary cooperation, which improves the performance gain of direct transmission with overlapping by 42%. Analytical results are developed to show that in a general network scenario, supplementary cooperation achieves bit error rate (BER) reduction of 34.87%, compared with the conventional cooperative transmission. Furthermore, we proposed a criterion for finding the best cooperative route to achieve maximum network throughput in a general network.

Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector

Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

Packet delay and throughput are critical performance metrics in the design and evaluation of computer networks, particularly in small-scale environments where resource constraints and traffic dynamics strongly influence quality of service. This research presents a simulation-based analysis of packet delay and throughput in a small-scale computer network using a controlled network model that captures realistic traffic patterns, queueing behavior, and protocol interactions. The simulation framework enables systematic variation of key parameters such as packet arrival rate, link bandwidth, buffer size, and routing configuration to observe their impact on end-to-end delay and achievable throughput. Performance results demonstrate that increasing traffic load leads to nonlinear growth in packet delay due to queue saturation, while throughput initially increases with load before reaching a stable limit governed by link capacity. The research further shows that appropriate buffer sizing can reduce packet loss but may increase average delay, highlighting an inherent trade-off between latency and throughput. Comparative observations across multiple simulation scenarios indicate that balanced resource allocation and moderate traffic intensity yield optimal performance in small networks. The findings emphasize the usefulness of simulation tools for understanding complex network behavior without the cost and rigidity of physical test beds. By providing quantitative insights into delay-throughput relationships, this work supports informed network design decisions for educational laboratories, small offices, and experimental test environments. The results also offer a foundation for extending simulation-based evaluation toward more advanced networks incorporating quality-of-service mechanisms, adaptive routing, and heterogeneous traffic types. Such controlled experimentation assists researchers and students in visualizing performance trends, validating analytical expectations, and developing intuition about congestion effects, protocol efficiency, and scalability limits, thereby strengthening foundational understanding of network performance analysis and supporting reproducible, low-cost experimentation. These insights are particularly valuable for preliminary design stages and academic demonstrations involving simplified yet representative network configurations under varied simulated operating conditions.

Throughput is an important performance consideration for multihop wireless networks. In this paper, we study the joint link scheduling and power control problem, focusing on maximizing the network throughput. We formulate the maximum throughput link scheduling with power control (MATH-SPC) problem, and present a mixed integer linear programming (MILP) formulation to provide optimal solutions. However, simply maximizing the throughput leads to a severe bias on bandwidth allocation among all links. In order to enhance both throughput and fairness, we define a new parameter, the demand satisfaction factor (DSF), to characterize the fairness of bandwidth allocation. We formulate the maximum throughput fair link scheduling with power control (MATA-SPC) problem and present an MILP formulation for this problem. We also present an effective polynomial time heuristic algorithm, namely, the serial LP rounding (SLPR) heuristic. Our numerical results show that bandwidth can be fairly allocated among all links/flows by solving our MATA-SPC formulation or using our heuristic algorithm at the cost of a minor reduction of network throughput

This paper presents a mathematical model for planning in public schools, considering the satisfaction of the total demand for education, fair students' access to schools, and the total travel distance. Given a set of schools, the aim of the model is to find an optimal allocation of students such that school should is available to each group of students. The model includes information about geographical prioritised access zones which should be covered by each school. The objective function essentially seeks to minimise the total distance travelled by students. We propose a multi-commodity programming model for solving this problem. The model is used to analyse the effects of different allocation policies on the fairness of student allocation to schools. Results show that a fair distribution of students of each social class to each school is obtained, while the total distance being travelled is minimised.