Design and Evaluation of a Weighted Sacrificing Fair Queueing Algorithm for Wireless Packet Networks

Fair scheduling or Fair Queueing (FQ) algorithms have received much attention in recent years because of their ability to provide a wide range of Quality-ofService (QoS) guarantees to end users. In this paper, we present a new analysis method that can statistically model such fair scheduling algorithms under Poisson arrivals and a general packet length distribution. We coin the new term M/G/FQ to describe this analysis method, which fits a broad range of scheduling policies including WFQ, SCFQ, SFQ and SPFQ.

Analysis of temporal and throughput fair scheduling in multirate WLANs

Partitioning and global scheduling are two approaches for scheduling real-time tasks in multiprocessor environments. Partitioning is the more favored approach, although it is sub-optimal. This is mainly due to the fact that popular uniprocessor real-time scheduling algorithms, such as EDF and RM, can be applied to the partitioning approach with low scheduling overhead. In recent years, much research has been done on global real-time multiprocessor scheduling algorithms based on the concept of “proportionate fairness”. Proportionate fair (Pfair) scheduling [5],[6] is the only known optimal algorithm for scheduling real-time tasks on multiprocessor. However, frequent preemptions caused by the small quantum length for providing optimal scheduling in the Pfair scheduling make it impractical. Deadline Fair Scheduling (DFS) [1] based on Pfair scheduling tried to reduce preemption-related overhead by means of extending quantum length and sharing a quantum among tasks. But extending quantum length causes a mis-estimation problem for eligibility of tasks and a non-work-conserving problem. In this paper, we propose the Enhanced Deadline Fair Scheduling (E-DFS) algorithm to reduce preemption-related overhead. We show that E-DFS allows us to decrease quantum length by reducing overhead and save wasted CPU time that is caused by preemption-related overhead and miss-estimation of eligibility.

Fairness amongst multiple users sharing a common resource has been an important criterion in the evaluation of scheduling algorithms in networks. Anonymous networking, where sources of transmitted packets are undecipherable to an eavesdropper, requires that packets from multiple sources are randomly reordered prior to transmission which works against the notion of fair scheduling. Consequently, it is important to understand the relationship between fairness and achievable anonymity in networking. In this paper, this relationship is characterized for the class of fair scheduling axioms defined by considering the equal treatment ex ante and demand mono-tonicity, under which the proportional method is known to be the unique scheduling algorithm that achieves the desired fairness. Using an information theoretic quantitative framework, the anonymity of this scheduling algorithm is characterized and proven to be asymptotically optimal with increase in buffer size. The anonymity achieved by the proportional method is also shown to be significantly better than conventional fair scheduling algorithms such as first come first serve and round robin, thus making a case for its application in data networks.

Scheduling algorithms are fundamental components in the process of resource management in mobile communication networks with heterogeneous QoS requirements such as delay, delay jitter, packet loss rate or throughput. The random characteristics of the propagation environment and the use of complex physical layers in order to combat this random behavior further complicates the design of simple, efficient, scalable and fair scheduling algorithms. This paper presents the main criteria used in the design of scheduling algorithms for 3G/4G mobile communications networks and provides a survey of scheduling mechanisms proposed for use in TDMA and CDMA based systems.Keywordsscheduling4GGPSutility functions

Packet scheduling is key to the provision of quality of service (QoS) differentiation and guarantees in a wireless network. Unlike its wireline counterpart, wireless communication poses special problems such as time-varying link capacity and location-dependent errors. These special problems make designing efficient and effective scheduling algorithms for wireless networks very challenging. Although many wireless scheduling algorithms have been proposed in recent years, some issues remain unresolved. This paper introduces a new wireless scheduling algorithm called BGFS-EBA (bandwidth-guaranteed fair scheduling with effective excess bandwidth allocation), which addresses these issues. It is shown that BGFS-EBA distributes excess bandwidth effectively, strikes a balance between effort-fair and outcome-fair, and provides delay bound for error-free flows and transmission effort guarantees for error-prone flows. The new algorithm is compared with some recent wireless scheduling algorithms.

We consider schemes for enacting task share changes - a process called reweighting - on real-time multiprocessor platforms. Our particular focus is reweighting schemes that are deployed in environments in which tasks may frequently request significant share changes. Prior work has shown that fair scheduling algorithms are capable of reweighting tasks with minimal allocation error and that partitioning-based scheduling algorithms can reweight tasks with better average-case performance, but greater error. However, preemption and migration overheads can be high in fair schemes. In this paper, we consider the question of whether global scheduling techniques can improve the accuracy of reweighting relative to partitioning-based schemes and provide improved average-case performance relative to fair-scheduled systems. Our conclusion is that, for soft real-time systems, global scheduling techniques provide a good mix of accuracy and average-case performance.

In this paper we propose a fair-scheduling algorithm for the transmission of video frames over wireless links. The scheme is based on the occupancy of the video decoder buffer. When the occupancy of the decoder buffer of a given video client becomes increasingly small and approaches a starvation threshold, it gets treated with a higher priority and gets serviced more frequently than other clients who may not be approaching the starvation point. When compared to other commonly-used scheduling algorithms, ours shows a better performance in terms of the end-to-end delay experienced by the video frames.

Existing packets scheduling algorithms designed for energy-efficient wireless networks ignore important features of periodic packets, thereby being inadequate for periodic packets with energy constraints. To remedy this problem, I present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. I propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, I develop an algorithm to schedule periodic packets (or ESPP for short) over a wireless link. The ESPP algorithm aims at minimizing energy dissipation of periodic packets without missing deadlines of periodic packets. I show through simulation studies that ESPP can significantly reduce energy consumption of wireless networks by an average of 46.4% while guaranteeing timing constraints of periodic packets.

Although weighted fair queueing (WFQ) has been regarded as an ideal scheduling algorithm in terms of its combined delay bound and proportional fairness properties, its asymptotic time complexity increases linearly with the number of sessions serviced by the scheduler, thus limiting its use in high-speed networks. An algorithm that combines the delay and fairness bounds of WFQ with O(1) timestamp computations had remained elusive so far. In this paper we present two novel scheduling algorithms that have O(1) complexity for timestamp computations and provide the same bounds on end-to-end delay and buffer requirements as those of WFQ. The first algorithm, frame-based fair queueing (FFQ), uses a framing mechanism to periodically recalibrate a global variable tracking the progress of work in the system, limiting any short-term unfairness to within a frame period. The second algorithm, starting potential based fair queueing (SPFQ), performs the recalibration at packet boundaries, resulting in improved fairness while still maintaining the O(1) timestamp computations. Both algorithms are based on the general framework of rate-proportional servers (RPSs) introduced by Stiliadis and Varma (see ibid., vol.6, no.2, p.164-74, 1998). The algorithms may be used in both general packet networks with variable packet sizes and in asynchronous transfer mode (ATM) networks.

Long Term Evolution(LTE) 시스템은 멀티미디어 서비스와 같은 다양한 서비스를 이동 사용자에게 제공할 수 있다. 실제로 HSPA+(High Speed Packet Access plus)는 다운링크의 비트 율(bit rate)을 크게 향상 시켰으나, 다양한 멀티미디어 서비스에서 요구하는 서비스 품질을 어떻게 만족시킬 것인가에 대한 연구는 매우 미비한 상태이다. 본 논문에서는 proportional fair(PF) 알고리즘을 기반으로 하여 LTE를 위한 효율적인 패킷 스케쥴링 알고리즘을 제안하였다. 제안된 알고리즘에서는 패킷 스케쥴링을 할 때 패킷의 우선순위를 결정하는 가장 중요한 요소인 트래픽 클래스의 가중치와 순간 사용자 데이터 전송율을 최적화 하였다. 또한 다중 셀(multi-cell) 환경에서 LTE를 통해서 사용자에게 전송되는 멀티미디어 트래픽을 시뮬레이션 함으로써 제안된 알고리즘의 성능이 향상되었음을 보였다. The Long Term Evolution (LTE) system is already able to provide a background of variety services for mobile users with multimedia services such as audio, video, and data. In fact, the High Speed Packet Access plus (HSPA+) solution can greatly enhance bit rates on down-link. However, the supporting for multimedia applications with different QoS (Quality of Service) requirements is not devised yet. Hence, in this paper we propose an effective packet scheduling algorithm based on Proportional Fairness (PF) scheduling algorithms for the LTE. In this proposed packet scheduling scheme, we optimized instantaneous user data rates and the traffic class weight which prioritize user's packets. Finally, we evaluated and showed the performance of the proposed scheduling algorithm through simulations of multimedia traffics being transmitted to users over LTE links in a multi-cell environment.

The effective reuse of the radio resources in D2D communication underlaying cellular system is pivotal for enhancing the total system capacity. Developing practical and effective scheduling schemes is critical to the success of D2D-based cellular systems. An optimal proportionally fair (PF) scheduling scheme that maximizes the logarithmic sum of the user data rates is proposed in this paper. Considering the fact that the computational complexity of the optimal PF scheduling is significantly high, low-complexity heuristic algorithm has also been proposed. It has been shown that the heuristic algorithm achieves high logarithmic sum of the average user data rates while maintaining low-computational complexity. Simulation results are used to verify the performance of the proposed algorithm, and to compare it with the optimal PF scheduling algorithms.

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We consider the downlink of a multiuser multiple-input multiple-output (MIMO) system, where the base station and the mobile receivers are equipped with multiple antennas. We propose simplified algorithms for channel-aware multiuser scheduling in conjunction with receive antenna selection for two downlink multiuser orthogonal space-division multiplexing techniques: block diagonalization and successive optimization. The algorithms greedily maximize the weighted sum rate. The algorithms add the best user at a time from the set of users that are not selected yet to the set of selected users until the desired number of users has been selected. To apply the proportional fairness criterion, simplified user scheduling metrics are proposed for block diagonalization and successive optimization. Two receive antenna selection algorithms are also proposed, which further enhance the power gain of the equivalent single-user channel after orthogonal precoding by selecting a subset of the receive antennas that contributes the most toward the total power gain of the channel. A user grouping technique is used to further lower the complexity of the selection algorithms. We compare various multiuser MIMO scheduling strategies that are applied to block diagonalization and successive optimization transmission techniques through simulation. Simulation results demonstrate the effectiveness of the proposed algorithms in ensuring throughput fairness among users. Results also show that when the number of users is large, the proposed scheduling algorithms perform close to the exhaustive search algorithms and previously proposed greedy scheduling algorithms, but with much lower complexity.

The deployment of federated learning in a wireless network, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated edge learning</i> (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.