Start-time fair queueing

We present a start-time fair queueing (SFQ) algorithm that is computationally efficient and achieves fairness regardless of variation in a server capacity. We analyze its single server and end-to-end deadline guarantee for variable rate fluctuation constrained (FC) and exponentially bounded fluctuation (EBF) servers. To support heterogeneous services and multiple protocol families in integrated services networks, we present a hierarchical SFQ scheduler and derive its performance bounds. Our analysis demonstrates that SFQ is suitable for integrated services networks since it: (1) achieves low average as well as maximum delay for low-throughput applications (e.g., interactive audio, telnet, etc.); (2) provides fairness which is desirable for VBR video; (3) provides fairness, regardless of variation in server capacity, for throughput-intensive, flow-controlled data applications; (4) enables hierarchical link sharing which is desirable for managing heterogeneity; and (5) is computationally efficient.

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.

Common channel multi-hop Ad Hoc networks have some inherent constraints related to throughput and Quality of Service (QoS). Multiuser detection (MUD) based Medium Access Control (MAC) can relax some of these constraints and provide significant gains in throughput and Quality of Service (QoS). These gains can be realized by implementing a distributed neighborhood scheduling algorithm that needs to choose one from several possible transmission configurations in each frame. This feature allows formulating different scheduling performance objectives such as delay minimization or throughput maximization. In this paper we focus on analysis and comparison of the system performance under different objectives including multi-objective formulations. First we implement a scheduling scheme that minimize delay using Start Time Fair Queuing (STFQ) algorithm and compare its performance with scheduling that maximises the throughput. Then we formulate multi-objective functions that are used to achieve a trade-off between delay and throughput performance. One of these formulations is based on the Nash arbitration scheme from cooperative game theory. The numerical results demonstrate the flexibility and efficiency of the proposed approach.

Low latency and efficient packet scheduling for streaming applications

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.

Analysis of temporal and throughput fair scheduling in multirate WLANs

Fair scheduling algorithms have been proposed to tackle the problem of bursty and location-dependent errors in wireless packet networks. Most of those algorithms ensure the fairness property and guarantee the QoS of all sessions in a large-scale cellular network such as GSM or GPRS. In this paper, we propose the Weighted-Sacrificing Fair Queueing (WSFQ) scheduling algorithm for small-area and device-limited wireless networks. WSFQ slows down the growth of queue length in limited-buffer devices, still maintains the properties of fairness, and guarantees the throughputs of the system. Moreover, WSFQ can easily adapt itself to various kinds of traffic load. We also implement a packet-based scheduling algorithm, the Packetized Weighted Sacrificing Fair Queueing (PWSFQ), to approach the WSFQ. WSFQ and PWSFQ are evaluated by comparing with other algorithms by mathematical analysis and simulations.

Fair queueing algorithms based on Generalized Processor Sharing (GPS) provide sessions with instantaneous fair sharing of server capacity, as well as guaranteed service rate and delay. The instantaneous fair sharing distributes server capacity to currently back-logged sessions in proportion to their weights regardless of the amount of service that the sessions received in the past. From a long-term perspective, however, this type of sharing leads to a different quality of service to sessions even if they have the same weight, since it provides only the instantaneous capacity sharing without considering the accumulated service delay and bandwidth. To minimize such long-term unfairness, we propose a delay and bandwidth normalized scheduling model in which the concept of Value of Service (VoS) is defined from the aspects of both delay and bandwidth. Performance comparisons between the proposed algorithm and traditional fair queueing algorithms show that the proposed algorithm provides better long-term fairness among sessions and is more adaptive to sessions with dynamic traffic patterns without compromising the guarantees of service rate and delay.

Fair queuing (FQ) algorithms, which have been proposed for QoS wireline-wireless networking, rely on the fundamental idea that the service rate allocated to user m is proportional to a positive weight /spl phi//sub m/. Targeting wireless data networks with a multicode CDMA-based physical layer, we develop FQ with time-varying weight assignments in order to minimize the queuing delays of mobile users. Applying dynamic programming, we design a computationally efficient algorithm which produces the optimal service rates while obeying (i) constraints imposed by the underlying physical layer, and (ii) QoS requirements. Simulations illustrate the merits of our designs.

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.

The Internet is facing two problems simultaneously: there is a need for a faster switching/routing infrastructure and a need to introduce guaranteed qualities-of-service (QoS). Each problem can be solved independently: high performance routers can be made faster by using input-queued crossbars instead of shared memory systems; QoS can be provided using packet fair queueing (PFQ) algorithms. Until now, however, the two solutions have been mutually exclusive-all of the work on PFQ algorithms has required that routers use output-queueing or centralized shared memory. In this paper, we design and implement a packet scheduling algorithm, DF/sup 2/Q (distributed feedback fair queueing), on the basis of CIOQ (combined input output queueing) architecture. The most important feature of this scheduling algorithm is the introduction of a feedback mechanism. We analyze and discuss the performance of DF/sup 2/Q, and the experimental results show that it can avoid internal congestion effectively and improve the efficiency of resource utilization.

For pt.I see ibid., p.509-18 (1997). The total implementation complexity of cell schedulers which aim at approximating the generalized processor sharing (GPS) policy is the combination of the complexity of their system-potential function and the complexity involved in sorting the timestamps in order to select the cell with minimum timestamp for transmission. Given that several scheduling algorithms which use a system-potential function of O(1) complexity have been introduced (among them, the minimum-delay self-clocked fair queueing (MD-SCFQ) algorithm achieves optimal delay and excellent fairness properties), the major contribution to the total complexity comes from the task of sorting the timestamps every time a cell is transmitted or received, which is common to all GPS-related schedulers. In this paper, we present a technique, called the logarithmic calendar queue (LCQ), that can achieve a dramatic reduction of the implementation complexity of a GPS-related scheduler, at the cost of a very small and controllable degradation of the delay bounds. We then use the LCQ to implement MD-SCFQ with reduced complexity. By applying the general methodology for the delay analysis presented in the first part of this paper (Chiussi et al. 1997), we are able to provide a closed-form expression of the delay bounds as a function of the LCQ design parameters, which can be used to optimize the design. The LCQ is not exclusively related to MD-SCFQ, but can be applied to any GPS-related scheduler to simplify implementation complexity.

In order to address the challenge of providing quality of service guarantees in today's network processing systems, the use of efficient scheduling algorithms is required. The efficiency of a scheduler is determined by several factors including its fairness, capability to operate at high speeds, as well as the resources required for its implementation. We present an architecture to support fair scheduling (gigabit FS) considering variable length packets (i. e. for packet forwarding/switching networks) over gigabit links. This high speed scheduler is designed to manage 32 K flows based on an algorithm that yields efficient implementation in hardware by avoiding the complexity of computing the system virtual time function that many packet fair queueing (PFQ) algorithms have proposed. Further, we demonstrate the critical factors in designing an effective scheduling engine at gigabit rates and we present several enhancements together with their associated cost.

Controllable fair queuing for meeting performance goals

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.