Upload
fisk
View
54
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Bounded Delay Scheduling with Packet Dependencies. Michael Markovitch Joint work with Gabriel Scalosub Department of Communications Systems Engineering Ben-Gurion University. Real Time Video Streaming. Sandvine, “Global Internet phenomena report – 1H 2013”. Real Time Video Streaming. - PowerPoint PPT Presentation
Citation preview
Michael MarkovitchJoint work with Gabriel Scalosub
Department of Communications Systems Engineering Ben-Gurion University
Bounded Delay Scheduling with Packet Dependencies
Real Time Video Streaming
2 Sandvine, “Global Internet phenomena report – 1H 2013”
Real Time Video Streaming
• Video streams are comprised of frames– Bursty traffic
• Video frames can be large (>>1500B)– Fragmentation
• Interdependency between different packets– Dropping some packets -> drop frame
• Packets MUST arrive in a timely manner
3
Current situation & Related work• Best practices:
– DiffServ AF queue for video streams– Admission control (average throughput)
• Number of streams can be large– Average throughput < channel access rate– Overlapping bursts >> momentary channel rate
• Related work– FIFO queuing with dependencies– Deadline scheduling without dependencies
[MPR, 2011] [MPR, 2012] [EHMPRR, 2012] [KPS, 2013] [SML, 2013] [EW, 2012] [AMS, 2002]
4
Deadline scheduling
• Every packet has a deadline• Focus on scheduling• Queue size assumed unbounded• More information (than FIFO)
5
Buffer and Traffic Model
• Single non-FIFO queue of infinite size (one hop)• Discrete time:
• Every packet :– One of multiple packets in a frame– Has arrival time, deadline, size and value
• Goal: Maximize value of completed frames
Arrival substep
Delivery substep
Cleanup substep
Packets arrive One packet delivered Packets may be dropped
6
Buffer and Traffic Model
• Frames of uniform size – k• No redundancy• Packets of uniform size and value – WLG
7
k = 12
Buffer and Traffic Model
• Uniform slack – d• Packets can be scheduled on arrival
8
Arrival sequence
schedule
t
t
Arrival(p) Deadline(p)d
d
Buffer and Traffic Model
• Finite burst size – b
9
Arrival sequencet
b
Buffer and Traffic Model
• Recap:– Frames of uniform size - – Uniform slack – d– Finite burst size – b– No redundancy– Packets of uniform size and value – WLG 1
• Goal: Maximize number of completed frames• NP-hard off-line problem
10
Competitive analysis
• Worst case performance of online algorithms
• – instance• – problem
11
A proactive greedy algorithm
• Ensures completion of at least one frame– Holds packets of only one frame
12
Arrival substep
Delivery substep
Cleanup substep
Packets arrive One packet delivered Packets may be dropped
Proactive greedy - example
Arrival sequence
Proactive greedy schedule
13
Proactive greedy – competitiveness
• Competitive ratio – Details in the paper
• Not far off from the lower bound
14
A better greedy algorithm
15
Why?
Greedy algorithm - analysis
• Competitive ratio – Details in the paper
• We have a matching lower bound
• Reminder: For proactive greedy –
16
What about the deadlines?
• Deadlines not used explicitly• Bad news?
– Worst case performance matches lower bound• Good news
– There is space for more interesting algorithms– Improve general performance
• How can deadlines be utilized?– Several approaches presented in the paper
17
Simulation
• Three online algorithms:– “Vanilla” greedy algorithm– Greedy algorithm with slack tie breaker– Opportunistic algorithm
• And the best current offline approximation
18
Simulation
• Simulation details:– Average throughput = channel access rate– 50 streams at 30FPS– Each stream starts at a random time
• Between 0 and 33ms– Random (short) time between successive packets
• “jitter” between packets of a single frame
19
Simulation results
20
To sum up
• First work considering both deadline scheduling and packet dependencies
• Very simplified model– Yet hard
• Improvements to the model– Non uniform slack– Randomization– Redundancy
21