Strict Fairness Research Articles

Generosity in dictator game and social value orientation predicted the type of costly punishment in ultimatum game.

The current study investigated two types of punishment (prosocial and proself punishment) in the ultimatum game (UG) and examined the impact of individuals' prosocial orientation on the costly punishment in the UG in a representative sample of Chinese undergraduate students (N=344). The prosocial orientation was measured with individuals' performance in the dictator game (DG), social value orientation (SVO), and the personal norm of reciprocity. Prosocial and proself punishers were established with responses in advantage and disadvantage offers in the UG. Results showed that prosocial and proself punishers coexisted in the UG, and prosocial punishers were more likely to be prosocial in SVO and allocated more resources to the recipient in the DG than proself punishers, but differences in positive/negative reciprocity between prosocial and proself punishers were not found. Further regression analysis found that SVO type and generosity in the DG could predict the type of punishers in the UG, but not positive/negative reciprocity. These findings indicate that individuals rejecting unfair offers in the UG may be motivated by either prosocial motivation with strict fairness concern (prosocial punishment) or selfish motivation with maximizing self-interest concern (proself punishment). Moreover, SVO type and generosity in the DG might be effective measures to discriminate prosocial and proself punishers in the UG.

GVTS: Global Virtual Time Fair Scheduling to Support Strict Fairness on Many Cores

Proportional fairness in CPU scheduling has been widely adopted to fairly distribute CPU shares corresponding to their weights. With the emergence of cloud environments, the proportionally fair scheduling has been extended to groups of threads or nested groups to support virtual machines or containers. Such proportional fairness has been supported by popular schedulers, such as Linux Completely Fair Scheduler (CFS) through virtual time scheduling. However, CFS, with a distributed runqueue per CPU, implements the virtual time scheduling locally . Across different queues, the virtual times of threads are not strictly maintained to avoid potential scalability bottlenecks. The uneven fluctuation of CPU shares caused by the limitations of CFS not only violates the fairness support for CPU assignments, but also significantly increases the tail latencies of latency-sensitive applications. To mitigate the limitations of CFS, this paper proposes a global virtual-time fair scheduler (GVTS) , which enforces global virtual time fairness for threads and thread groups, even if they run across many physical cores. The new scheduler employs the hierarchical enforcement of target virtual time to enhance the scalability of schedulers, which is aware of the topology of CPU organization. We implemented GVTS in Linux kernel 4.6.4 with several optimizations to provide global virtual time efficiently. Our experimental results show that GVTS can almost eliminate the fairness violation of CFS for both non-grouped and grouped executions. Furthermore, GVTS can curtail the tail latency when latency-sensitive applications are co-running with batch tasks.

Strict fairness of equilibria in asymmetric information economies and mixed markets

We investigate the fairness property of equal-division competitive market equilibria (CME) in asymmetric information economies with a space of agents that may contain non-negligible (large) traders. We first propose an extension to our framework of the notion of strict fairness due to Zhou (J Econ Theory 57:158–175, 1992). We prove that once agents are asymmetrically informed, any equal-division CME allocation is strictly fair, but a strictly fair allocation might not be supported by an equilibrium price. Then, we investigate the role of large traders and we provide two sufficient conditions under which, in the case of complete information economies, a redistribution of resources is strictly fair if and only if it results from a competitive mechanism.

Cache-Aided Communications With Multiple Antennas at Finite SNR

We study the problem of cache-aided communication for cellular networks with multi-user and multiple antennas at finite signal-to-noise ratio. Users are assumed to have non-symmetric links, modeled by wideband fading channels. We show that the problem can be formulated as a linear program, whose solution provides a joint cache allocation along with pre-fetching and fetching schemes that minimize the duration of the communication in the delivery phase. The suggested scheme uses zero-forcing and cached interference subtraction, and hence, allows each user to be served at the rate of its own channel. Thus, this scheme is better than the previously published schemes that are compromised by the poorest user in the communication group. We also consider a special case of the parameters for which we can derive a closed form solution and formulate the optimal power, rate, and cache optimization. This special case shows that the gain of MIMO coded caching goes beyond the throughput. In particular, it is shown that in this case, the cache is used to balance the users such that fairness and throughput are no longer contradicting. More specifically, in this case, strict fairness is achieved jointly with maximizing the network throughput.

A low-complexity resource allocation scheme for OFDMA multicast systems with proportional fairness

A low-complexity optimization scheme is proposed to balance the tradeoff between system capacity and proportional fairness in orthogonal frequency division multiple access (OFDMA) based multicast systems. The major challenge is to solve the non-convexity optimization problem with strict proportional fairness. Constrained team progress algorithm (CTPA) solves this non-convexity problem by allocating sub-channels to each group based on sub-channel gains and proportional fairness constraint. Mapping power algorithm (MPA) guarantees strict proportional fairness with efficient power allocation which utilizes the mapping relation between power and throughput. CTPA-MPA is analyzed in three aspects: complexity, fairness and efficiency. We numerically show that when the system capacity is slightly increased in lower power region compared with several previous approaches, CTPA-MPA improves the proportional fairness in a typical scenario with 4 groups over 16 sub-channels, while reducing the complexity from exponential to linear in the number of sub-channels. It is also proved available in a more complicated system.

Rochester Elastic Cache Utility (RECU): Unequal Cache Sharing is Good Economics

When renting computing power, fairness and overall performance are important for customers and service providers. However, strict fairness usually results in poor performance. In this paper, we study this trade-off. In our experiments, equal cache partitioning results in 131 % higher miss ratios than optimal partitioning. In order to balance fairness and performance, we propose two elastic, or movable, cache allocation baselines: elastic miss ratio baseline (EMB) and elastic cache space baseline (ECB). Furthermore, we study optimal partitions for each baseline with different levels of elasticity, and show that EMB is more effective than ECB. We also classify programs from the SPEC 2006 benchmark suite based on how they benefit or suffer from the elastic baselines, and suggest essential information for customers and service provider to choose a baseline.

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.

Performance of Reactive Relay Selection Based on Cumulative Distribution Function of SNRs for Two-Way Relay Networks

In this letter, we propose the reactive relay selection scheme based on cumulative distribution function (CDF) of SNRs for two-way relay networks over Nakagami- $m$ fading channels. We analyze average relay fairness by deriving the exact integral and asymptotic expressions for relay selection probability. Also, we obtain diversity order by deriving outage probability. Numerical results show that the analytical results of the proposed scheme match the simulation results well. It is shown that the proposed scheme guarantees strict fairness among relays and achieves full diversity.

Performance Analysis of CDF-Based Relay Selection in Dual-Hop Wireless Networks

In this paper, we propose the proactive and the reactive cumulative distribution function (cdf)-based relay selection schemes with a control parameter (CP) in a dual-hop wireless network to easily control the fairness among relays such as the probability of selecting each relay and achieve full diversity. We analyze the probability of selecting a relay and the average fairness among relays for the proposed schemes. The upper bound of outage probability is derived in closed form for the proactive cdf-based relay selection (CRS) scheme. Exact outage probability is derived in closed form for the reactive CRS scheme. Moreover, we derive the asymptotic outage probabilities at a high-signal-to-noise-ratio region. Numerical results show that the analytical results of the proposed CRS schemes match the simulation results well. It is shown that both the proactive and reactive CRS schemes achieve the same diversity as the channel-gain-based relay selection (CGRS) scheme, which is outage optimal. It is shown that when all CPs are equally set for all relays, the proposed CRS schemes guarantee strict fairness among relays. It is shown that when CPs are set based on the channel condition, the outage probability of the proposed CRS schemes is close to that of the CGRS scheme.

Guarantee Strict Fairness and UtilizePrediction Better in Parallel Job Scheduling

As the most widely used parallel job scheduling strategy, EASY backfilling achieved great success, not only because it can balance fairness and performance, but also because it is universally applicable to most HPC systems. However, unfairness still exists in EASY. Our simulation shows that a blocked job can be delayed by later jobs for more than 90 hours on real workloads. Additionally, directly employing runtime prediction techniques in EASY would lead to a serious situation called reservation violation. In this paper, we aim at guaranteeing strict fairness (no job is delayed by any jobs of lower priority) while achieving attractive performance, and employing prediction without causing reservation violation in parallel job scheduling. We propose two novel strategies, namely, shadow load preemption (SLP) and venture backfilling (VB), which are integrated into EASY to construct preemptive venture EASY backfilling (PV-EASY). Experimental results on three real HPC workloads demonstrate that PV-EASY is more attractive than EASY in parallel job scheduling, from both academic and industry perspectives.

Improving Fairness without Outage Performance Deterioration in Selection Cooperation

The traditional selection cooperation scheme selects the relay with best instantaneous receive signal-to-noise ratio to forward the message and achieves good outage performance, which may however cause poor fairness among relays. In this letter, we propose two practical selection cooperation schemes in Decode-and-Forward (DF) fashion to improve the fairness of relay selection. Numerical results suggest that both of the proposed schemes can achieve fairness close to the strict fairness scheme without outage performance deterioration. It is also validated that these schemes have lower complexities than traditional ones and therefore are practical for real networks.

A Throughput Enhancement under a Strict Fairness Constraint in OFDMA System

In this paper, we mathematically derive a matrix-form solution named resource allocation matrix (RAM) for sub-band allocation in an orthogonal frequency division multiple access (OFDMA) system. The proposed scheme is designed to enhance throughput under a strict user fairness condition such that every user has an equal number of sub-bands per frame. The RAM designates the most preferable sub-band for every user. The proposed scheme is evaluated in terms of throughput and user fairness by comparison with the proportional fairness (PF) scheme and greedy scheme. Numerical results show that the proposed scheme has overwhelming superiority to other schemes in terms of fairness and tight competitive in terms of throughput.

Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.

A Novel Approach to Fair Routing in Wireless Mesh Networks

Multiradio wireless mesh network (WMN) is a feasible choice for several applications, as routers with multiple network interface cards have become cheaper. Routing in any network has a great impact on the overall network performance, thus a routing protocol or algorithm for WMN should be carefully designed taking into account the specific characteristics of the network. In addition, in wireless networks, serious unfairness can occur between users if the issue is not addressed in the network protocols or algorithms. In this paper, we are proposing a novel centralized routing algorithm, called Subscriber Aware Fair Routing in WMN (SAFARI), for multiradio WMN that assures fairness, leads to a feasible scheduling, and does not collapse the aggregate network throughput with a strict fairness criterion. We show that our protocol is feasible and practical, and exhaustive simulations show that the performance is improved compared to traditional routing algorithms.

Design of a fair scheduler exploiting multiuser diversity with feedback information reduction

In this letter, we propose a new downlink fair scheduling scheme exploiting the multiuser diversity to enhance the transmission capacity. In the proposed scheme, only the MSs (mobile stations) whose normalized SNR (signal-to-noise ratio) values are larger than a given threshold feedback one-bit information to the BS (base station). As a result, while achieving the strict fairness, the proposed scheme can efficiently utilize the spectrum by reducing the considerable amount of the feedback information, compared to the proportional fair scheduling scheme where all the MSs feed back the normalized SNR values to the BS. Numerical studies show that the transmission capacity in the proposed scheme with a suitable value of the threshold is very close to that in the proportional fair scheduling scheme.

On the Capacity-Fairness Tradeoff in Multiuser Diversity Systems

Conventional multiuser diversity maximizes the system throughput but results in an unfair scheduling of the system resources across users. Proportional fair scheduling types of systems achieve strict fairness among different users, but this fairness comes at the cost of a significant system capacity penalty. In this paper, we propose two forms of a hybrid multiuser scheduling scheme that provides a flexible balance/tradeoff between the system achievable capacity and the fairness among users. Our results show that the capacity versus fairness tradeoff can be achieved by grouping users and then using a two-step selection process.

Optimization-based congestion control for multicast communications

This article outlines an approach for multicast congestion control based on an economic model that has been successfully applied to unicast congestion control. In this model, congestion signals are interpreted as prices and congestion-controlled sessions as utility maximizing agents. A naive extension of the unicast model fails to achieve a reasonable balance between providing the incentives necessary to promote the use of multicast and ensuring that multicast sessions do not interact too aggressively with unicast sessions. We extend the model by introducing a rational definition of multicast utility. The revised model provides a basis for multicast congestion control protocols that provide incentives to use multicast but are necessarily unfair to unicast traffic. We show, however, that the degree of unfairness can be controlled by appropriately setting a design parameter with a limiting case of strict fairness.

Fair and efficient packet scheduling using Elastic Round Robin

Parallel systems are increasingly being used in multiuser environments with the interconnection network shared by several users at the same time. Fairness is an intuitively desirable property in the allocation of bandwidth available on a link among traffic flows of different users that share the link. Strict fairness in traffic scheduling can improve the isolation between users, offer a more predictable performance and improve performance by eliminating some bottlenecks. This paper presents a simple, fair, efficient, and easily implementable scheduling discipline, called Elastic Round Robin (ERR), designed to satisfy the unique needs of wormhole switching, which is popular in interconnection networks of parallel systems. In spite of the constraints of wormhole switching imposed on the design, ERR is also suitable for use in Internet routers and has better fairness and performance characteristics than previously known scheduling algorithms of comparable efficiency, including Deficit Round Robin and Surplus Round Robin. In this paper, we prove that ERR is efficient, with a per-packet work complexity of O(1). We analytically derive the relative fairness bound of ERR, a popular metric used to measure fairness. We also derive the bound on the start-up latency experienced by a new flow that arrives at an ERR scheduler. Finally, this paper presents simulation results comparing the fairness and performance characteristics of ERR with other scheduling disciplines of comparable efficiency.

A Christian View of Justice

It should be of supreme interest to anyone who is trying to come to a true Christian understanding of justice to discover as far as possible the attitude of Jesus to matters of justice. There are a number of gospel passages which suggest that he was not at all interested in the concept of justice as fairness. In his teaching there are strong indications that, with God, people do not get what they deserve, that one’s recompense does not bear any relation to one’s effort or merit. It is indicated moreover that such a rejection of justice as fairness should be adopted by men in their dealings with one another because that is the way God deals with men. I am not putting it this way merely to shock. I think that a critique of justice as strict fairness is explicit in the teachings of Jesus and that we must pay attention to it if we are to arrive at the notion of justice which his teachings do uphold, in continuity with the rest of Scripture.Some of the sayings which suggest Jesus’s indifference to strict fairness are these: The parable of the workers in the vineyard (Matt. 20:1-16a); The parable of the prodigal son (Luke 15: 11-32); the refusal of Jesus to divide the inheritance between two brothers (Luke 12:13-15); the sayings in the sermon on the mount, Matt. 5:44 (par. Luke 6:32-36), about loving your enemies, “so that you may be sons of your Father who is in heaven, for he makes his sun to rise on the good and the evil and sends rain on the just and the unjust.”

Discussion: On Falling Short of Strict Coherence

Abner Shimony called it coherence; John Kemeny called it strict fairness; today many people speak of strict coherence. According to Shimony's definition, a set of betting rates on a series of propositions hi and ei is strictly incoherent, when “there exists a choice of stakes Si such that, if X accepts the series of bets at these stakes, then no matter what the actual truth values of hi, and ei may be, X can at best lose nothing, and in at least one possible eventuality he will suffer a positive loss” ([3], p. 9).