1 / 28

Scheduling Heterogeneous Real-Time Traffic over Fading Wireless Channels

Scheduling Heterogeneous Real-Time Traffic over Fading Wireless Channels. I-Hong Hou P.R. Kumar. University of Illinois, Urbana-Champaign. Background: Wireless Networks. There will be increasing use of wireless networks for serving traffic with QoS constraints:

carsyn
Download Presentation

Scheduling Heterogeneous Real-Time Traffic over Fading Wireless Channels

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Scheduling Heterogeneous Real-Time Traffic over Fading Wireless Channels I-Hong Hou P.R. Kumar University of Illinois, Urbana-Champaign

  2. Background: Wireless Networks • There will be increasing use of wireless networks for serving traffic with QoS constraints: • Example: VoIP, Video Streaming, Real-time Monitoring, Networked Control, etc. • Client requirements include • Specified traffic patterns • Delay bounds • Timely throughput bounds

  3. Previous Work and Challenges • Prior work [Hou et al] [Hou and Kumar]: • Clients have hard throughput requirements • Static but unreliable wireless channels • All clients require the same delay bounds • Optimal packet scheduling policies are proposed • Q: How to deal with more complicated scenarios? • Rate adaptation may be applied • Channel qualities can be time-varying • Clients may require different delay bounds • This work extends the model in prior work and proposes a guideline for these scenarios

  4. Client-Server Model • A system with N wireless clients and one AP • Time is slotted • AP schedules all transmissions 2 1 AP 3

  5. More General Traffic Model • Group time slots into periods with T time slots • Clients may generate packets at the beginning of each period {1,2,3} {1,.,3} {.,2,.} {.,2,.} {1,2,3} {1,.,3} T 2 1 AP {1,.,3} {.,2,.} {1,2,3} 3

  6. Different Delay Bounds • Deadline for client n = τn τ2=5 2 1 arrival deadline τ1=4 arrival AP deadline 3 τ3=3 arrival deadline

  7. Channel Model • Channel changes from period to period • Channels are static within a period • System may or may not support rate adaptation • With Rate Adaptation • Transmission takes sc,n time slots under channel c • Transmissions are error-free • Without Rate Adaptation • Transmission takes 1 time slot • Transmissions succeeds with probability pc,n

  8. Timely Throughput Requirements • Timely throughput = • Client n requires timely throughput qn • Q: How to design a scheduling policy to fulfill requirements of all feasible sets of clients? • Feasibility optimal scheduling policy

  9. Pseudo-debt • Delivery debt: deficiency of timely throughput • Time debt: deficiency of time spent on a client • Pseudo-debt • rn(t) quantifies the behavior of client n up to time t • The set of clients is fulfilled  converges to 0 in probability

  10. Sufficient Condition for Optimality • Let μn be the reduction on debt for client n • Theorem: A policy that maximizes for each period is feasibility optimal. • Analogous to Max-Weight scheduling in wireline networks

  11. Rate Adaptation with Different Delay Bounds • Scenario: • Rate adaptation used • Clients may have different per packet delay bounds, τn • Modified Knapsack Policy: • Find an ordered set S={m1,m2,…} to maximize total debt • A variation of knapsack problem and can be solved by DP S1 = 3 τ1=4 τ2=7 τ3=10 S2 = 5 S1 = 3 S3 = 4 S3 = 4

  12. Rate Adaptation with Different Delay Bounds • Scenario: • Rate adaptation used • Clients may have different per packet delay bounds, τn • Modified Knapsack Policy: • Find an ordered set S={m1,m2,…} to maximize total debt • A variation of knapsack problem and can be solved by DP S1 = 3 τ1=4 τ2=7 τ3=10 S2 = 5 S2 = 5 S3 = 4 S3 = 4

  13. Rate Adaptation with Different Delay Bounds • Scenario: • Rate adaptation used • Clients may have different per packet delay bounds, τn • Modified Knapsack Policy: • Find an ordered set S={m1,m2,…} to maximize total debt • A variation of knapsack problem and can be solved by DP S1 = 3 τ1=4 τ2=7 τ3=10 S2 = 5 S1 = 3 S2 = 5 S3 = 4

  14. Time-Varying Channels • Scenario: • Same delay bounds for all clients, τ≡τn • Time-varying channels, pn(t) • Applicable to Gilbert-Elliot fading Model • Joint Debt-Channel Policy: • Let rn(t) be delivery debt • Clients with larger rn(t) pn(t) get higher priorities • Theorem: The Joint Debt-Channel policy is feasibility optimal

  15. Heterogeneous Delay Bounds • Scenario: • Static channels, pn≡pn(t) • Different delay bounds for all clients, τn • Adaptive-Allocation Policy: • Let rn(t) be time debt • Estimate the # of slots needed by client n for a successful transmission, ηn • Dynamically allocate slots to maximize

  16. Evaluation Methodology • Evaluate four policies: • Proposed policies for each scenario • PCF with randomly assigned priorities (random) • Two policies proposed by [Hou, Borkar, and Kumar] • Time debt first policy • Weighted-delivery debt first policy • Metric: Total delivery debt

  17. Rate Adaptation: VoIP Setup • Period length = 20 ms • Two groups of clients: • 66 Group A clients and 44 Group B clients

  18. Rate Adaptation: VoIP Results

  19. Time-Varying Channels: VoIP Setup • Period length = 20 ms • Two groups of clients: • 57 Group A clients and 38 Group B clients

  20. Time-Varying Channels: VoIP Result

  21. Heterogeneous Delay Bounds: VoIP Setup • Two groups of clients: • 57 Group A clients and 38 Group B clients

  22. Heterogeneous Delay Bounds: VoIP Result

  23. Conclusion • Extend previous model for more complicated scenarios • With or without rate adaptation • Time-varying channels • Heterogeneous delay bounds • Identify a sufficient condition for optimal scheduling policies • Design policies for several cases • Time-varying channels, heterogeneous delay bounds with rate adaptation • Time-varying channels without rate adaptation • Heterogeneous delay bounds without rate adaptation

  24. Thank You

  25. Rate Adaptation: MPEG Setup • Period length = 6 ms • Two groups of clients: • 6 Group A clients and 6 Group B clients

  26. Rate Adaptation: MPEG Results

  27. Time-Varying Channels: MPEG Setup • Period length = 6 ms • Two groups of clients: • 4 Group A clients and 4 Group B clients

  28. Time-Varying Channels: MPEG Setup

More Related