Fairness Constraints Research Articles

Automatically disproving fair termination of higher-order functional programs

We propose an automated method for disproving fair termination of higher-order functional programs, which is complementary to Murase et al.’s recent method for proving fair termination. A program is said to be fair terminating if it has no infinite execution trace that satisfies a given fairness constraint. Fair termination is an important property because program verification problems for arbitrary ω-regular temporal properties can be transformed to those of fair termination. Our method reduces the problem of disproving fair termination to higher-order model checking by using predicate abstraction and CEGAR. Given a program, we convert it to an abstract program that generates an approximation of the (possibly infinite) execution traces of the original program, so that the original program has a fair infinite execution trace if the tree generated by the abstract program satisfies a certain property. The method is a non-trivial extension of Kuwahara et al.’s method for disproving plain termination.

Stochastic regret minimization for revenue management problems with nonstationary demands

AbstractWe study an admission control model in revenue management with nonstationary and correlated demands over a finite discrete time horizon. The arrival probabilities are updated by current available information, that is, past customer arrivals and some other exogenous information. We develop a regret‐based framework, which measures the difference in revenue between a clairvoyant optimal policy that has access to all realizations of randomness a priori and a given feasible policy which does not have access to this future information. This regret minimization framework better spells out the trade‐offs of each accept/reject decision. We proceed using the lens of approximation algorithms to devise a conceptually simple regret‐parity policy. We show the proposed policy achieves 2‐approximation of the optimal policy in terms of total regret for a two‐class problem, and then extend our results to a multiclass problem with a fairness constraint. Our goal in this article is to make progress toward understanding the marriage between stochastic regret minimization and approximation algorithms in the realm of revenue management and dynamic resource allocation. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 433–448, 2016

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

Verification of population protocols

Population protocols (Angluin et al. in PODC, 2004) are a formal model of sensor networks consisting of identical mobile devices. Two devices can interact and thereby change their states. Computations are infinite sequences of interactions satisfying a strong fairness constraint. A population protocol is well specified if for every initial configuration C of devices, and every computation starting at C, all devices eventually agree on a consensus value depending only on C. If a protocol is well specified, then it is said to compute the predicate that assigns to each initial configuration its consensus value. While the computational power of well-specified protocols has been extensively studied, the two basic verification problems remain open: Is a given protocol well specified? Does a given protocol compute a given predicate? We prove that both problems are decidable by reduction to the reachability problem of Petri nets. We also give a new proof of the fact that the predicates computed by well-defined protocols are those definable in Presburger arithmetic (Angluin et al. in PODC, 2006).

Joint scheduling and mapping in support of downlink fairness and spectral efficiency in ieee 802.16e OFDMA system

SummaryThe next generation broadband wireless networks deploy orthogonal frequency division multiple access (OFDMA) as the enabling technologies for broadband data transmission with QoS capabilities. In such broadband wireless systems, one major issue is how to utilize radio resource efficiently while maintaining fairness between sessions as well as providing adequate QoS. In this work, we propose an approach for OFDMA/time division duplex (TDD) downlink suitable for IEEE802.16e WiMAX systems that combines scheduling and burst mapping algorithms for a trade‐off between session fairness, QoS, and spectral efficiency. While optimizing radio resources under QoS and fairness constraints is an NP‐hard problem, we follow a heuristic approach that simplifies the complexity of the algorithm. Performance results show that while the new scheme outperforms the Proportional Fair algorithm in terms of fairness, it also improves the overall system spectral efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

Energy Efficiency With Proportional Rate Fairness in Multirelay OFDM Networks

This paper investigates the energy efficiency (EE) in multiple relay aided OFDM system, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the EE performance with user fairness for such a system, we formulate an optimization problem to maximize the EE by jointly considering several factors, the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the non-convex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. We also investigate the effects of the circuit power consumption on system performances and observe that with both the constant and the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the system average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the system average CNR. This observation is very important which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance.

OFDMA-Based DF Secure Cooperative Communication With Untrusted Users

In this letter we consider resource allocation for OFDMA-based secure cooperative communication by employing a trusted Decode and Forward (DF) relay among the untrusted users. We formulate two optimization problems, namely, (i) sum rate maximization subject to individual power constraints on source and relay, and (ii) sum power minimization subject to a fairness constraint in terms of per-user minimum support secure rate requirement. The optimization problems are solved utilizing the optimality of KKT conditions for pseudolinear functions.

Outage Balancing in Downlink Non-Orthogonal Multiple Access With Statistical Channel State Information

This paper considers a downlink non-orthogonal multiple access (NOMA) system where the source intends to transmit independent information to the users at targeted data rates under statistical channel state information at the transmitter. The problem of outage balancing among the users is studied with the issues of power allocation, decoding order selection, and user grouping being taken into account. Specifically, with regard to the max-min fairness criterion, we derive the optimal power allocation in closed-form and prove the corresponding optimal decoding order for the elementary downlink NOMA system. By assigning a weighting factor for each user, the analytical results can be used to evaluate the outage performance of the downlink NOMA system under various fairness constraints. Further, we investigate the case with user grouping, in which each user group can be treated as an elementary downlink NOMA system. The associated problems of power and resource allocation among different user groups are solved. The implementation complexity issue of NOMA is also considered with focus on that caused by successive interference cancellation and user grouping. The complexity and performance tradeoff is analyzed by simulations, which provides fruitful insights for the practical application of NOMA. The simulation results substantiate our analysis and show considerable performance gain of NOMA when compared with orthogonal multiple access.

A Game-Theoretic Based Resource Allocation Strategy for Cloud Computing Services

We propose an economics-oriented cloud computing resources allocation strategy with the use of game theory. Then we develop a resource allocation algorithm named NCGRAA (noncooperative game resource allocation algorithm) to search the Nash equilibrium solution that makes the utility of various resource providers achieve optimum. We also propose an algorithm named BGRAA (bargaining game resource allocation algorithm) to further increase the overall revenue with the constraints of efficiency and fairness. Based on numerical results, we discuss the influence of NCGRAA and BGRAA for the utility of resource on the system performance. It shows that the choice of parameters of the two algorithms is significant in improving the system performance and converging to the Nash equilibrium and Nash bargaining.

Throughput-Efficient Super-TDMA MAC Transmission Schedules in Ad Hoc Linear Underwater Acoustic Networks

Underwater acoustic (UWA) sensor network deployments may be used in many applications for environmental, scientific, military, and commercial purposes. Several topologies are in use, but the most widely used topology is linear. Indeed, placing nodes on a single line offers more opportunities in terms of large coverage and high-rate services. The UWA channel is a shared medium. Thus, a medium access control (MAC) protocol is necessary, primarily to regulate and coordinate nodes’ access. MAC protocol design should take into consideration large propagation delays to favor better network throughput. Performance of most developed protocols in linear topologies does not exceed 1 in terms of normalized network throughput, or equivalently, channel utilization. We explore transmission schedules in three important contexts. 1) Single collision domain with unicast traffic. In an $N$ -node network, we develop transmission schedules achieving a normalized network throughput of $2 - (2/N)$ . This is the best that can be done in such a context, as demonstrated using a general greedy approach combined with an exhaustive search for small-size networks. 2) Single collision domain with broadcast traffic. We propose a periodic per-node fair schedule with the shortest period. Achievable throughput in such conditions is close to $N/2$ . Likewise, we prove that the throughput is upper bounded by $N - 1$ under the per-node fairness constraint. 3) Partially overlapping collision domains with unicast traffic. We consider a simple illustration of such a configuration. The proposed transmission schedule depicts a scenario where messages originate at one end of the network, and are sequentially relayed node by node (i.e., hop by hop) in the direction of the final destination located at the other end of the network. Furthermore, for all three discussed contexts, we build up computationally efficient algorithms that generate transmission schedules regardless of network size. We explore the idea of exploiting nonzero propagation delays for linear topologies to improve network throughput. In recent UWA sensor networks, the linear topology is a fundamental component that may be used to build more complex network configurations. This study would then serve as a base for future research into this area.

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

We conduct an empirical investigation of the impact of queue management on patients’ average wait time and length of stay (LOS). Using an emergency department’s (ED) patient-level data from 2007 to 2010, we find that patients’ average wait time and LOS are longer when physicians are assigned patients under a pooled queuing system with a fairness constraint compared to a dedicated queuing system with the same fairness constraint. Using a difference-in-differences approach, we find the dedicated queuing system is associated with a 17% decrease in average LOS and a 9% decrease in average wait time relative to the control group—a 39-minute reduction in LOS and a four-minute reduction in wait time for an average patient of medium severity in this ED. Interviews and observations of physicians suggest that the improved performance stems from the physicians’ increased ownership over patients and resources that is afforded by a dedicated queuing system, which enables physicians to more actively manage the flow of patients into and out of ED beds. Our findings suggest that the benefits from improved flow management in a dedicated queuing system can be large enough to overcome the longer wait time predicted to arise from nonpooled queues. We conduct additional analyses to rule out alternate explanations for the reduced average wait time and LOS in the dedicated system, such as stinting and decreased quality of care. Our paper has implications for healthcare organizations and others seeking to reduce patient wait time and LOS without increasing costs. This paper was accepted by Serguei Netessine, operations management.

Full Duplex-Assisted Intercell Interference Cancellation in Heterogeneous Networks

This paper studies the suppression of cross-tier intercell interference (ICI) generated by a macro base station (MBS) to pico user equipments (PUEs) in heterogeneous networks (HetNets). Different from existing ICI avoidance schemes such as enhanced ICI cancellation (eICIC) and coordinated beamforming, which generally operate at the MBS, we propose a full duplex (FD)-assisted ICI cancellation (fICIC) scheme, which can operate at each pico BS (PBS) individually and is transparent to the MBS. The basic idea of the fICIC is to apply FD technique at the PBS such that the PBS can send the desired signals and forward the listened cross-tier ICI simultaneously to PUEs. We first consider the narrowband single-user case, where the MBS serves a single macro UE and each PBS serves a single PUE. We obtain the closed-form solution of the optimal fICIC scheme, and analyze its asymptotical performance in ICI-dominated scenario. We then investigate the general narrowband multiuser case, where both MBS and PBSs serve multiple UEs. We devise a low-complexity algorithm to optimize the fICIC aimed at maximizing the downlink sum rate of the PUEs subject to user fairness constraint. Finally, the generalization of the fICIC to wideband systems is investigated. Simulations validate the analytical results and demonstrate the advantages of the fICIC on mitigating cross-tier ICI.

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems.

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems.

An adaptive scheduling scheme for cooperative energy harvesting networks

Energy harvesting devices have been proposed for sensor networking applications where batteries cannot be replaced, and cooperative communication schemes have been used to increase energy efficiency for wireless systems. Here, we develop transmission scheduling schemes for multi-terminal cooperative energy harvesting networks that maximize the packet delivery ratio, i.e., the probability that an event is reported successfully. We see that the proposed scheme provides virtually the same performance as the state-of-the-art threshold-based scheme, but does not require auxiliary parameter optimization. The proposed scheme also permits extensions to multiple cooperating nodes and sources, and it can be modified to accommodate fairness constraints.

Optimal Fair Opportunistic Scheduling For Wireless Systems Via Classification Framework

In this work, we exploit historical channel data via linear programming and machine learning tools to perform opportunistic scheduling for multiuser wireless systems under temporal fairness constraints. We first derive linear program-based scheduling (LPS) algorithms that compute the scheduling decisions from a window of past user metrics. The proposed linear program scheduling policies approach the optimal policy as the window size gets large. However, as demonstrated via simulations, even with a short window, the performance of the proposed policies can be very close to optimal. For stationary environments, we introduce a new interpretation of the scheduling problem that casts the resource allocation problem as one of statistical classification. We then propose a novel supervised classification-based scheduling (SCS) framework, which uses the LPS decisions to obtain labeled samples for training a multiclass classifier and obtaining optimal scheduling decision boundaries. In addition, as applications of the proposed classification framework, we devise efficient classification methods to learn the scheduling offsets for existing offset-driven scheduling policies.

A Two-Level Temporal Fair Scheduler for Multi-Cell Wireless Networks

We propose a two-level scheduler for a frequency reuse-1 multi-cell wireless network satisfying inter-and intra-cell weighted temporal fairness constraints. As opposed to hard partitioning of the entire frequency band to different cell patterns in frequency reuse-M systems (M > 1), we propose sharing this band opportunistically in time by these patterns. Through numerical examples, we show notable gains in overall network throughput due to improved multi-user diversity in comparison with a conventional frequency reuse-3 system.

Reduction of the feedback delay effect on a proportional fair scheduler in LTE downlink using nonlinear support vector machine prediction

The scheduling of mobile users often relies on accurate feedback from the channel quality indicator (CQI). In this paper, we determine the strength of the effect of feedback delay on the scheduler in a Long-Term Evolution (LTE) system. We study this degradation under fairness constraints using a proportional fair scheduler. We propose a nonlinear support vector machine regression with a modified cost function in order to reduce the effect of feedback delay on the scheduler, which operates by predicting the CQI from previous feedback and using that for scheduling instead of the delayed feedback. The simulation results show important improvements in terms of throughput.

Token Bucket Fair Scheduling Algorithm with Adaptive Rate Allocations for Heterogeneous Wireless Networks

In this paper, we address the problem of uplink multi-traffic sources scheduling in heterogeneous wireless networks (HWNs) under real time traffics that requires fairness constraint and strict per-...

Reasoning about memoryless strategies under partial observability and unconditional fairness constraints

Alternating-time Temporal Logic is a logic to reason about strategies that agents can adopt to achieve a specified collective goal.A number of extensions for this logic exist; some of them combine strategies and partial observability, some others include fairness constraints, but to the best of our knowledge no work provides a unified framework for strategies, partial observability and fairness constraints. Integration of these three concepts is important when reasoning about the capabilities of agents without full knowledge of a system, for instance when the agents can assume that the environment behaves in a fair way.We present ATLKirF, a logic combining strategies under partial observability in a system with fairness constraints on states. We introduce a model-checking algorithm for ATLKirF by extending the algorithm for a full-observability variant of the logic and we investigate its complexity. We validate our proposal with an experimental evaluation.

Balancing Long Lifetime and Satisfying Fairness in WBAN Using a Constrained Markov Decision Process

As an important part of the Internet of Things (IOT) and the special case of device-to-device (D2D) communication, wireless body area network (WBAN) gradually becomes the focus of attention. Since WBAN is a body-centered network, the energy of sensor nodes is strictly restrained since they are supplied by battery with limited power. In each data collection, only one sensor node is scheduled to transmit its measurements directly to the access point (AP) through the fading channel. We formulate the problem of dynamically choosing which sensor should communicate with the AP to maximize network lifetime under the constraint of fairness as a constrained markov decision process (CMDP). The optimal lifetime and optimal policy are obtained by Bellman equation in dynamic programming. The proposed algorithm defines the limiting performance in WBAN lifetime under different degrees of fairness constraints. Due to the defect of large implementation overhead in acquiring global channel state information (CSI), we put forward a distributed scheduling algorithm that adopts local CSI, which saves the network overhead and simplifies the algorithm. It was demonstrated via simulation that this scheduling algorithm can allocate time slot reasonably under different channel conditions to balance the performances of network lifetime and fairness.