1 |
8/15 |
M |
Chpt 1: Motivating Examples on Queueing Theory. |
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2 |
8/17 |
W |
Chpt 2: Queueing Theory Notation/Vocabulary. |
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3 |
8/22 |
M |
Chpt 4: Simulating Random Variables |
HW 1 Due |
4 |
8/24 |
W |
Chpt 5: Convergence of Random Variables and Time Average versus Ensemble Average. |
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5 |
8/29 |
M |
Chpt 6: Operational Laws (Little’s Law) |
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6 |
8/31 |
W |
Chpt 7: Modification Analysis |
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|
9/5 |
M |
LABOR DAY, NO CLASS |
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7 |
9/7 |
W |
Chpt 8: Discrete-time Markov Chains |
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8 |
9/12 |
M |
Chpt 9: Ergodicity - Finite-state DTMCs |
HW 2 Due |
9 |
9/14 |
W |
Chpt 10: More DTMCs |
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10 |
9/19 |
M |
Chpt 11: Exponential Distribution |
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11 |
9/21 |
W |
Chpt 11: Poisson Process |
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|
9/26 |
M |
NO CLASS |
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12 |
9/28 |
W |
Chpt 12,13: M/M/1 |
HW 3 Due |
13 |
10/3 |
M |
Chpt 14: M/M/k |
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14 |
10/5 |
W |
Chpt 15: Capacity Provisioning |
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15 |
10/10 |
M |
Chpt 20: Pareto Distribution |
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|
10/12 |
M |
NO CLASS |
|
16 |
10/17 |
W |
Chpt 21: Phase-type distributions + start Chpt23 |
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17 |
10/19 |
M |
Chpt 23: M/G/1 |
HW 4 Due |
18 |
10/24 |
W |
Towards Optimality in Parallel Job Scheduling (Berg et al.) |
|
19 |
10/26 |
M |
Paper Discussions |
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20 |
10/31 |
W |
- |
|
21 |
11/2 |
M |
- |
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22 |
11/7 |
W |
- |
|
23 |
11/9 |
M |
- |
|
24 |
11/14 |
M |
- |
|
25 |
11/16 |
W |
- |
|
26 |
11/21 |
M |
- |
|
|
11/23 |
W |
THANKSGIVING, NO CLASS |
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27 |
11/28 |
M |
- |
|
28 |
11/30 |
W |
- |
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