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I-Hong Hou, Jing Zhu, and Rath Vannithamby

Incentive-Oriented Downlink Scheduling for Wireless Networks with Real-Time and Non-Real-Time Flows. I-Hong Hou, Jing Zhu, and Rath Vannithamby. Motivation. Wireless networks are increasingly used to serve real-time flows VoIP, video streaming, online gaming

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I-Hong Hou, Jing Zhu, and Rath Vannithamby

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  1. Incentive-Oriented Downlink Scheduling forWireless Networks with Real-Time andNon-Real-Time Flows I-Hong Hou, Jing Zhu, and RathVannithamby

  2. Motivation • Wireless networks are increasingly used to serve real-time flows • VoIP, video streaming, online gaming • In addition to throughput, these flows require strict per-packet delay guarantees • Most current mechanisms belong to the paradigm of DiffServ

  3. DiffServ • Serve different flows differently • Usually, real-time flows get higher priorities than non-real-time ones • Can be unfair to non-real-time flows • Non-real-time flows may lie about its category to gain more service • Solution: charge real-time flows more

  4. I am a real-time flow. I need small delay You need to pay more I require small throughput. Can I sacrifice throughput for delay?

  5. Goal of the paper Design a scheduling policy that allows flows to tradeoff between high throughput and low delay by themselves

  6. Desired Properties • Incentive-compatibility: Flows optimize their own performance by reporting true category • Versatility: The policy can work with various protocols in other layers • Different traffic patterns, different MAC, etc. • Deadline awareness: The policy respects the deadlines of real-time flows • Work conservation

  7. Incentives of clients • Non-real-time clients: Aim to maximize throughputs • Real-time clients: Aim to maximize timely-throughput • Timely-throughput: throughput of packets with delay < D

  8. Basic Idea of Design • Assume each client n has a weight of wn • Each client n is entitled to have wn/Σwn channel time • Allocating channel time proportional to wn maximizes and achieves proportional fairness • Deficit of client n: (The amount of channel time that it is entitled) – (actual channel time)

  9. Joint Deficit-Deadline (JDD) Policy • A thin layer between Network layer and MAC layer • Provides two functions: enqueue and dequeue • Interface defined by ns-2 • enqueue: a packet is labeled with deadline and put in the queue when it arrives from Network layer • dequeue: forward a packet to MAC

  10. Architecture of JDD

  11. Enqueue • When a packet arrives from Network layer • Mark the deadline of the packet • Deadline of real-time flow = current time + D • Deadline of non-real-time flow = current time + a large value (~ TCP timeout) • Place the packet in the queue

  12. Dequeue • Delete all expired packets • Forward the packet with the earliest deadline with the constraint that the corresponding client has positive deficit • Earliest deadline: real-time packets usually got served first, and non-real-time packets need to wait • Positive deficit: real-time packets receive smaller channel time

  13. Ns-2 Simulation • 10 clients, 5 of them are real-time ones, and the other 5 are non-real-time ones • Real-time flows require a delay bound of 100ms • All flows are generated by TCP • Use IEEE802.11 for the MAC • Weight of non-real-time clients = 1 • Weight of real-time clients varies

  14. Simulation Results

  15. Delay Distributions

  16. Performance Comparison • Compare against two other policies: • Earliest deadline first (EDF) • Weighted round robin (WRR) • 10 clients, 5 real-time and 5 non-real-time • The distance between clients and the base station is evenly distributed • Performance metric: • = throughput/timely-throughput

  17. Simulation results for TCP

  18. Simulation results for TFRC

  19. Simulation results for Interfering Network • Add an additional link that causes interference

  20. Conclusion • We propose the JDD scheduling policy • The policy allows strategic clients to choose between high throughput and low delay • The policy does not make any assumptions on other layers, and can work with a wide range of different systems • Simulation results show that JDD outperforms other policies

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