Tradeoffs Between Low Complexity, Low Latency, and Fairness with Deficit Round-Robin Schedulers 683
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Queue Scheduling Disciplines
White Paper Supporting Differentiated Service Classes: Queue Scheduling Disciplines Chuck Semeria Marketing Engineer Juniper Networks, Inc. 1194 North Mathilda Avenue Sunnyvale, CA 94089 USA 408 745 2000 or 888 JUNIPER www.juniper.net Part Number:: 200020-001 12/01 Contents Executive Summary . 4 Perspective . 4 First-in, First-out (FIFO) Queuing . 5 FIFO Benefits and Limitations . 6 FIFO Implementations and Applications . 6 Priority Queuing (PQ) . 6 PQ Benefits and Limitations . 7 PQ Implementations and Applications . 8 Fair Queuing (FQ) . 9 FQ Benefits and Limitations . 9 FQ Implementations and Applications . 10 Weighted Fair Queuing (WFQ) . 11 WFQ Algorithm . 11 WFQ Benefits and Limitations . 13 Enhancements to WFQ . 14 WFQ Implementations and Applications . 14 Weighted Round Robin (WRR) or Class-based Queuing (CBQ) . 15 WRR Queuing Algorithm . 15 WRR Queuing Benefits and Limitations . 16 WRR Implementations and Applications . 18 Deficit Weighted Round Robin (DWRR) . 18 DWRR Algorithm . 18 DWRR Pseudo Code . 19 DWRR Example . 20 DWRR Benefits and Limitations . 24 DWRR Implementations and Applications . 25 Conclusion . 25 References . 26 Textbooks . 26 Technical Papers . 26 Seminars . 26 Web Sites . 27 Copyright © 2001, Juniper Networks, Inc. List of Figures Figure 1: First-in, First-out (FIFO) Queuing . 5 Figure 2: Priority Queuing . 7 Figure 3: Fair Queuing (FQ) . 9 Figure 4: Class-based Fair Queuing . 11 Figure 5: A Weighted Bit-by-bit Round-robin Scheduler with a Packet Reassembler . 12 Figure 6: Weighted Fair Queuing (WFQ)—Service According to Packet Finish Time . 13 Figure 7: Weighted Round Robin (WRR) Queuing . 15 Figure 8: WRR Queuing Is Fair with Fixed-length Packets . 17 Figure 9: WRR Queuing is Unfair with Variable-length Packets . -
Scheduling Algorithms
Scheduling in OQ architectures Scheduling and QoS scheduling Andrea Bianco Telecommunication Network Group [email protected] http://www.telematica.polito.it/ Andrea Bianco – TNG group - Politecnico di Torino Computer Networks Design and Management - 1 Scheduling algorithms • Scheduling: choose a packet to transmit over a link among all packets stored in a given buffer (multiplexing point) • Mainly look at QoS scheduling algorithms – Choose the packet according to QoS needs N OUTPUT inputs BUFFER Andrea Bianco – TNG group - Politecnico di Torino Computer Networks Design and Management - 2 Pag. 1 Scheduling in OQ architectures Output buffered architecture • Advantage of OQ (Output Queued) architectures – All data immediately transferred to output buffers according to data destination – It is possible to run QoS scheduling algorithms independently for each output link • In other architectures, like IQ or CIOQ switches, problems become more complex – Scheduling to satisfy QoS requirements and scheduling to maximize the transfer data from inputs to outputs have conflicting requirements Andrea Bianco – TNG group - Politecnico di Torino Computer Networks Design and Management - 3 QoS scheduling algorithms • Operate over multiplexing points • Micro or nano second scale • Easy enough to be implemented in hardware at high speed • Regulate interactions among flows • Single traffic relation (1VP/1VC) • Group of traffic relations (more VC/1VP o more VC with similar QoS needs) • QoS classes • Strictly related and dependent from buffer management -
Fairness in a Data Center
University of New Hampshire University of New Hampshire Scholars' Repository Doctoral Dissertations Student Scholarship Winter 2012 Fairness in a data center MIkkel Hagen University of New Hampshire, Durham Follow this and additional works at: https://scholars.unh.edu/dissertation Recommended Citation Hagen, MIkkel, "Fairness in a data center" (2012). Doctoral Dissertations. 694. https://scholars.unh.edu/dissertation/694 This Dissertation is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. Fairness in a data center BY Mikkel Hagen M.S. Computer Science, University of New Hampshire (2008) B.S. Computer Science/Biology/Psychology, St. Bonaventure University (2004) DISSERTATION Submitted to the University of New Hampshire in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science December 2012 UMI Number: 3537816 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dissertation Publishing UMI 3537816 Published by ProQuest LLC 2013. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. uest ProQuest LLC 789 East Eisenhower Parkway P.O. -
Hardware Implementation of a Scheduler for High Performance Switches with Quality of Service Support
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/253896547 Hardware implementation of a scheduler for high performance switches with Quality of Service support Article in Proceedings of SPIE - The International Society for Optical Engineering · May 2009 DOI: 10.1117/12.821522 CITATIONS READS 0 52 4 authors, including: Félix B. Tobajas Valentín de Armas Sosa Universidad de Las Palmas de Gran Canaria Universidad de Las Palmas de Gran Canaria 81 PUBLICATIONS 212 CITATIONS 63 PUBLICATIONS 151 CITATIONS SEE PROFILE SEE PROFILE Roberto Sarmiento Universidad de Las Palmas de Gran Canaria 202 PUBLICATIONS 1,075 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: NAutILES(Novel Autonomous Intelligent Localisable Endoscopy System) Link: https://nautilesorg.wordpress.com View project All content following this page was uploaded by Félix B. Tobajas on 21 January 2015. The user has requested enhancement of the downloaded file. Hardware implementation of a scheduler for high performance switches with Quality of Service support R. Arteaga, F. Tobajas, V. De Armas and R. Sarmiento Institute for Applied Microelectronics (IUMA) Departamento de Ingeniería Electrónica y Automática (DIEA) University of Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria. ABSTRACT In this paper, the hardware implementation of a scheduler with QoS support is presented. The starting point is a Differentiated Service (DiffServ) network model. Each switch of this network classifies the packets in flows which are assigned to traffic classes depending of its requirements with an independent queue being available for each traffic class. -
Performance Evaluation of Different Scheduling Algorithms in Wimax
PERFORMANCE EVALUATION OF DIFFERENT SCHEDULING ALGORITHMS IN WIMAX Ala’a Z. Al-Howaide 1, Ahmad S. Doulat 2, Yaser M. Khamayseh 3 Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan 1 2 3 [email protected] , [email protected] , [email protected] ABSTRACT Worldwide Interoperability for Microwave Access (WiMAX) networks were expected to be the main Broadband Wireless Access (BWA) technology that provided several services such as data, voice, and video services including different classes of Quality of Services (QoS), which in turn were defined by IEEE 802.16 standard. Scheduling in WiMAX became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users; this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, we presenedt a simulation study to measure the performance of several scheduling algorithms in WiMAX, which were Strict Priority algorithm, Round-Robin (RR), Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Self-Clocked Fair (SCF), and Diff-Serv Algorithm. KEYWORDS WiMAX,Quality of Services (QoS), Strict Priority (SP), Round Robin (RR), Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Self-Clocked Fair (SCF) Queuing, Diff-Serv (DS). 1. INTRODUCTION WiMAX is a telecommunication protocol that provides fixed and mobile internet access, in which this protocol combines a number of wireless technologies that have emerged from IEEE to face the rapid demand of higher data rate and longer transmission range in wireless access and to enable a high speed connection to the Internet in terms of multimedia service, trade, commerce, education, research and other applications. -
Packet Scheduling in Wireless Systems by Token Bank Fair Queueing Algorithm
Packet scheduling in wireless systems by Token Bank Fair Queueing Algorithm by WILLIAM KING WONG B.A.Sc, The University of Western Ontario, 1991 M.A.Sc, The University of Western Ontario, 1993 M.Sc., The University of Western Ontario, 1994 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDffiS ( ELECTRICAL AND COMPUTER ENGINEERING ) THE UNIVERSITY OF BRITISH COLUMBIA July 2005 © William King Wong, 2005 ABSTRACT The amalgamation of wireless systems is a vision for the next generation wireless systems where multimedia applications will dominate. One of the challenges for the operators of next generation networks is the delivery of quality-of-service (QoS) to the end users. Wireless scheduling algorithms play a critical and significant role in the user perception of QoS. However, the scarcity of wireless resources and its unstable channel condition present a challenge to providing QoS. To that end, this thesis proposes a novel scheduling algorithm Token Bank Fair Queuing (TBFQ) that is simple to implement and has the soft tolerance characteristic which is suitable under wireless constraints. TBFQ combines both token- based policing mechanism and fair throughput prioritization rule in scheduling packets over packet- switched wireless systems. This results in a simple design and yet achieves better performance and fairness than existing wireless scheduling strategies. The characterization of the algorithm, which leads to its tight bound performance, is conducted through numerical analysis that recursively determines the steady-state probability distribution of the bandwidth allocation process and ultimately the probability distribution of input queue. -
Multi-Queue Fair Queuing Mohammad Hedayati, University of Rochester; Kai Shen, Google; Michael L
Multi-Queue Fair Queuing Mohammad Hedayati, University of Rochester; Kai Shen, Google; Michael L. Scott, University of Rochester; Mike Marty, Google https://www.usenix.org/conference/atc19/presentation/hedayati-queue This paper is included in the Proceedings of the 2019 USENIX Annual Technical Conference. July 10–12, 2019 • Renton, WA, USA ISBN 978-1-939133-03-8 Open access to the Proceedings of the 2019 USENIX Annual Technical Conference is sponsored by USENIX. Multi-Queue Fair Queueing Mohammad Hedayati Kai Shen Michael L. Scott Mike Marty University of Rochester Google University of Rochester Google Abstract ric within a few microseconds. GPUs and machine learning Modern high-speed devices (e.g., network adapters, storage, accelerators may offload computations that run just a few mi- accelerators) use new host interfaces, which expose multiple croseconds at a time [30]. At the same time, the proliferation software queues directly to the device. These multi-queue in- of multicore processors has necessitated architectures tuned terfaces allow mutually distrusting applications to access the for independent I/O across multiple hardware threads [4, 36]. device without any cross-core interaction, enabling through- These technological changes have shifted performance bot- put in the order of millions of IOP/s on multicore systems. tlenecks from hardware resources to the software stacks that Unfortunately, while independent device access is scalable, manage them. In response, it is now common to adopt a multi- it also introduces a new problem: unfairness. Mechanisms queue architecture in which each hardware thread owns a that were used to provide fairness for older devices are no dedicated I/O queue, directly exposed to the device, giving longer tenable in the wake of multi-queue design, and straight- it an independent path over which to send and receive re- forward attempts to re-introduce it would require cross-core quests. -