Anonymity in packet scheduling under the max-min fairness criterion

Self-Clocked Fair Queueing (SCFQ) algorithm has been considered as an attractive packet scheduling algorithm because of its implementation simplicity, but it has unbounded delay property in some input traffic conditions. In this paper, we propose a Rate Proportional SCFQ (RP-SCFQ) algorithm which is a rate proportional version of SCFQ. If any fair queueing algorithm can be categorized into the rate proportional class and input is constrained by a leaky bucket, its delay is bounded and the same as that of Weighted Fair Queueing (WFQ) which is known as an optimal fair queueing algorithm. RP-SCFQ calculates the timestamps of packets arriving during the transmission of a packet using the current value of system potential updated at every packet departing instant and uses a starting potential when it updates the system potential. By doing so, RP-SCFQ can have the rate proportional property. RP-SCFQ is appropriate for high-speed packet-switched networks since its implementation complexity is low while it guarantees the bounded delay even in the worst-case input traffic conditions.

Convergence Complexity of Optimistic Rate-Based Flow-Control Algorithms

Considering the fairness between the users in physical-layer multigroup multicasting, the Max-Min Fairness (MMF) precoding problem is investigated under the per-antenna power constraints. A suboptimal precoding algorithm is proposed by decoupling the problem into a precoding direction vector design problem and a power allocation problem. Particularly, the SLNR (Signal to Leakage plus Noise Ratio) metric is considered in the precoding direction vector design to properly mitigate the inter-group interference. Moreover, aiming at the MMF criterion, the power allocation procedure can guarantee fairness between users. The simulation results show that the proposed algorithm has less complexity than the primary MMF algorithm at the cost of some performance loss, while has better performance than a low complex algorithm, and consequently provides a solution for the tradeoff between complexity and performance.

Packet scheduling constitutes the core problem in efficient fair allocation of bandwidth to competing flows. To date, numerous algorithms for packet scheduling have been suggested and tested. However, only a few of them are currently deployed. One of the key reasons for rarity of applied packet scheduling methods lies in the complexity of their implementation. This paper describes a family of randomized algorithms for packet scheduling. These algorithms are simple to implement and require small amounts of computation time. Specifically we present an O(1) probabilistic weighted fair queuing algorithm that emits packets from flows with an improved delay jitter. Experimental results of the proposed randomized algorithms suggest that the randomized approach is a viable alternative to the currently deployed deterministic fair queuing algorithms.

Fairness comparison of FAST TCP and TCP Reno

The performance of two wireless power transfer scheduling schemes, time sharing (TS) and spatial multiplexing (SM), in terms of provisioning fairness among energy receivers (ERs) is studied and compared while taking into account the nonlinearity of the energy harvesters circuits. In the network, the multiple-antenna energy transmitter (ET) attempts to maximize the harvested energy by the ER which has accumulated the minimum amount of energy among all single-antenna ERs during the wireless power transfer block T-hence the max-min fairness criterion. Two network scenarios are studied: homogeneous, where the channels power coefficients are assumed to be the same for all the well-apart ERs, and heterogeneous, where the said coefficients can take arbitrary values. It is proven analytically that in the homogeneous scenario, the optimal scheduling policy is to allocate each ER the full transmit power P with uniform distribution of charging times among ERs, rather than to allocate the full power transfer block T with uniform distribution of the power among ERs. Generalization of the network to the heterogeneous scenario aims to find the optimal beamforming vector for the SM scheme and the optimal time sharing vector for the TS scheme. It is shown through extensive Monte Carlo simulations that again TS outperforms SM in terms of max-min fairness as a result of taking into account the inherent non-linear characteristics of the harvesting devices.

It has been analytically proved that both MaxNet and SumNet can achieve max-min fair allocation in static networks. In SumNet networks, such as the TCP algorithm of the current Internet, the source rate is controlled by congestion signal which is the sum of signals from all of the congested links along the path from the source to the destination. In MaxNet networks, only the most congested link generates the control signal to dictate the source rate. This paper investigates the practical aspects of both schemes, specifically, the convergence speed and fairness tracking capability under transient network conditions. We have shown that the stability of SumNet's max-min fairness heavily depends on the network load. Within the stable ranges of operating points, SumNet's max-min fair allocation is obtained at the cost of response speed. An enhanced approach is proposed to improve the stability of SumNet to meet the max-min fairness criterion. We have shown both analytically and by simulation that under stable conditions, Maxnet has faster convergence speed and better fairness tracking capability than SumNet in a highly volatile environment.

Fairness and system throughput, influenced by wireless interference, are major objectives of resource allocation in wireless networks. Whereas traditionally max-min fairness protocols have been developed for wired networks (where interference is not a factor for network performance), in this paper we investigate the problem of flow routing and fair bandwidth allocation under the constraints of the physical interference model for multihop wireless networks. A max-min fair (MMF) routing algorithm under physical interference constraints is proposed where multiple candidate paths coexist for each user to the base station. The algorithm is formulated as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors. We compare our approach with two MMF interference routing algorithms in the literature that use the protocol interference model to garner fair bandwidth allocation. We show that our algorithm performs better in terms of blocking probability, bandwidth usage and link load.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.

In this work, we consider employing the NOMA (Non-Orthogonal Multiple Access) technique in downlink VLC (Visible Light Communication) for performance enhancement. In particular, focusing on a typical NOMA scenario with two users, we optimize the power allocation strategies under both sum-rate maximization and max-min fairness criteria, where practical optical power and QoS (Quality of Service) constraints are included. As our main contribution, we achieve optimal power allocation solutions in semi-closed forms via mathematical analysis, which, to the best of our knowledge, have not been reported in literature. Simulation results demonstrate that NOMA can provide remarkable performance gains over OMA (Orthogonal Multiple Access) in the context of VLC downlinks.

We propose joint spatial division and multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional channel state information at the transmitter (CSIT). JSDM achieves significant savings both in the downlink training and in the CSIT uplink feedback, thus making the use of large antenna arrays at the base station potentially suitable also for frequency division duplexing (FDD) systems, for which uplink/downlink channel reciprocity cannot be exploited. In the proposed scheme, the multiuser MIMO downlink precoder is obtained by concatenating a prebeamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional “effective” channel matrix. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is approached in the large number of antennas limit. For this case, we use Szego's asymptotic theory of Toeplitz matrices to show that a DFT-based prebeamforming matrix is near-optimal, requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a 2-D base station antenna array, with 3-D beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the prebeamforming optimization and calculate the system spectral efficiency under proportional fairness and max-min fairness criteria, showing extremely attractive performance. Our numerical results are obtained via asymptotic random matrix theory, avoiding lengthy Monte Carlo simulations and providing accurate results for realistic (finite) number of antennas and users.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.

In order to build a mobile ad hoc network, only some battery powered nodes are required to be deployed; no additional infrastructure is necessary. Routing, scheduling etc. algorithms for this kind of networks have to be designed in such a manner that battery power in nodes can be saved, that is, algorithms should be energy efficient. In most cases, an external priority is assigned to data packets which we have to take care. Along with that, in WEEP, we consider residual lifetime of source and destination nodes to estimate whether the current packet can be sent to destination. Moreover, if possibility of completion of a communication session is less, then more weight is given to data packets belonging to those sessions. The present article aims at reducing the number of route discovery sessions through energy efficient scheduling policy. Therefore, packets travelling through paths consisting of delicate links, are blessed with more priority than those travelling through strong wireless links. Simulation results show that performance improvement produced by WEEP is very significant compared to other protocols used to scheduling in ad hoc networks. As far as the authors know, WEEP is a unique scheduling algorithm in ad hoc network that considers expected residual lifetime of destination along with performance and fragility of a path.

A good crossbar switch scheduler should be able to sustain full bandwidth and maintain fairness among competing flows. A pure input-queued (IQ) non-buffered switch requires an impractically complex scheduler to achieve this goal. Common solutions are to use crossbar speedup and/or buffered crossbar. In this paper, we explore this issue in a buffered crossbar without speedup. We first discuss the conflict between fairness and throughput and the fairness criteria in crossbar switch scheduling, and justify that a desirable scheduler should sustain full bandwidth for admissible traffic and ensure max-min fairness for non-admissible traffic. Then we describe an adaptive max-min fair scheduling (AMFS) algorithm and show by analysis and simulation that it can provide both 100% throughput and max-min fairness. Finally we briefly discuss the hardware implementation of the AMFS algorithm.

To efficiently trade off system sum-rate and link fairness, this paper is dedicated to maximizing the sum of $\alpha$ -fair utility in spectrum-sharing networks, where multiple interfering links share one channel. In the literature, three special cases, including $\alpha=\mbox{0}$ (sum-rate maximization), $\alpha=\mbox{1}$ (proportional fairness), and $\alpha=\infty$ (max-min fairness), have been investigated; the complexity for cases $\mbox{1} and $\mbox{0} is still unknown. In this paper, we prove that the problem is convex when $\mbox{1} and is NP-hard when $\mbox{0} . To deal with the latter case, we transform the objective function and represent it by the difference of two concave functions (D.C.). Then, a power allocation algorithm is proposed with fast convergence to a local optimal point. Simulation results show that the proposed algorithm can obtain global optimality in two-link cases when $\mbox{0} . In addition, we can get a flexible tradeoff between sum-rate and fairness in terms of Jain's index by adjusting $\alpha$ .