bounded delay scheduling with packet dependencies
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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 PresentationTRANSCRIPT
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
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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]
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Deadline scheduling
• Every packet has a deadline• Focus on scheduling• Queue size assumed unbounded• More information (than FIFO)
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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
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Buffer and Traffic Model
• Frames of uniform size – k• No redundancy• Packets of uniform size and value – WLG
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k = 12
Buffer and Traffic Model
• Uniform slack – d• Packets can be scheduled on arrival
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Arrival sequence
schedule
t
t
Arrival(p) Deadline(p)d
d
Buffer and Traffic Model
• Finite burst size – b
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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
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Competitive analysis
• Worst case performance of online algorithms
• – instance• – problem
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A proactive greedy algorithm
• Ensures completion of at least one frame– Holds packets of only one frame
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Arrival substep
Delivery substep
Cleanup substep
Packets arrive One packet delivered Packets may be dropped
Proactive greedy - example
Arrival sequence
Proactive greedy schedule
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Proactive greedy – competitiveness
• Competitive ratio – Details in the paper
• Not far off from the lower bound
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A better greedy algorithm
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Why?
Greedy algorithm - analysis
• Competitive ratio – Details in the paper
• We have a matching lower bound
• Reminder: For proactive greedy –
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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
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Simulation
• Three online algorithms:– “Vanilla” greedy algorithm– Greedy algorithm with slack tie breaker– Opportunistic algorithm
• And the best current offline approximation
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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
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Simulation results
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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
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