Opportunistic
Service Centric
Scheduling with
Strict Deadlines
DAVID RAMIREZ
26/JAN/2015
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Service Centric
•Network focuses on servicing
• Fast is generally good, but why?
• Consider service requirements
•Care for quality and quantity of services
• Quality – information transmitted in a timely manner
• Quantity – the more the merrier
•All services move information, but some movements are
more important than others
• Different services should be prioritized differently
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Opportunistic Scheduling
•Deterministic Scheduling
• Plan in advance
• Central scheduler (likely)
• Predetermined
•Opportunistic Scheduling
• Actively plan
• Distributed decisions
• Leverage network variations
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Strict Deadlines
F
c
c
T
•Each link has distinct L bits of information
•Reward of wi for transmitting before T
•Learning a channel costs c time units
•Channels are i.i.d.
•Channel defines rate, thus time to transmit
Should a node who has learned it’s channel
transmit or not?
Goal: Maximize  wi for completed services
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Case with Limited Knowledge
•User only knows channel distribution (i.i.d. for others)
•User knows other links want to use the channel
•No knowledge of wi or how many other links might want to use F
Minimize time channel is used = Maximize remaining time
=>
More transmissions (hopefully)
When should node n decide to transmit?
tn = time to transmit L bits (depends on channel distribution)
Xn = T – tn = remaining time (w/o o.h.)
Yn = Xn - nc = time left after transmission
max Y
n
n
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Solving Limited Knowledge
Yn  X n  nc
max Yn
n
Optimal stopping at…
N  min{n  1 : X n  V }
*
*
From optimality equation…
V  E [min{ X 1 ,V }]  c
*
*
With some math….


V
*
( x  V )dF ( x )  c
*
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Example with t~U(0,1) & T=1
Penalty for not transmitting = T, this explains tendency to 1
At low c, optimal outperforms all others
At high c, nobody finds an ‘appropriate’ channel
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More Knowledge More Problems
Assume a genie tells us ‘everything’ from the future…
N
max  si wi
i 1
N
s.t.
s t   c  T
i 1
i i
s
si - binary decision variable (1 if Tx, 0 if not)
 s - number of nodes that probe
Possible to solve by solving N problems with  fixed
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Knowledge and Responsibility
No genies in real life, so…
Users know w for all users and their
ti
Node 1 transmits if…
w1  E[ f (T  t1  c)] ≥ E[ f (T  c)]
E[ f (T  c)]
E[ f (T  t1  c)]
E[ f (t1 )]
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Dynamic Programming
Traditional knapsack problem
3 kg
2 USD
1 kg
1 USD
5 kg
10 USD
5 kg
10 USD
3 kg
2 USD
1 kg
1 USD
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Pruning further
Decision remains ‘if’ optimal, but not iff
◦ Still optimal to not transmit if not met
◦ Not necessarily optimal if condition met
w1 ≥ E[ f (t1 )]
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Results
At under-resourced case (N>3) schemes differentiate
Scheduled pays price of commitment
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Results
Better than optimal (remember obj. function)
Not ‘our metric’ either, but hey!
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Results
Deadlines and future networks want to ‘shorten’ the
roundtrip time (5G Andrews)
Scheduled becomes ‘bumpy’
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Conclusion
Opportunistic Service-centric framework for shared single
channel
Found solution for limited NI and broad NI
◦ Relates to stopping and knapsacks respectively
Solutions vary in practicality and complexity
Schemes differ in under resourced scenario (the interesting
scenario)
Future directions: Multiple channels, admission control,
physical model type rates (i.e. interference is ok), ???
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