TreeOnlyBlindLP module¶

class panco.blind.treeOnlyBlindLP.TreeOnlyBlindLP(network: Network, foi: int, filename='tree_blind.lp')¶

Bases: object

Class for the analysis for tree networks with blind multiplexing using linear programming techniques.

Parameters:

network (Network) – the network to analyze (must be a well-numbered tree)
foi (int) – the flow of interest for computing the performance bound
filename (str) – the name of the file where the linear program is written

arrival_constraints(f)¶

Writes the arrival constraints for each flow of the network

Parameters:: f – file to write the constraints
Returns:: nothing

arrival_shaping_constraints(f)¶

Writes the arrival shaping constraints for each flow of the network

Parameters:: f – file to write the constraints
Returns:: nothing

property backlog: float¶

Computes the worst-case backlog of the flow of interest

Returns:: the backlog of the flow of interest
Return type:: float

>>> flows = [Flow([TokenBucket(1, 1)], [3, 4]), Flow([TokenBucket(2, 2)], [0, 3]),
...          Flow([TokenBucket(3, 3)], [2, 4]), Flow([TokenBucket(4, 4)], [1, 3, 4])]
>>> servers = [Server([RateLatency(10, 1), RateLatency(5, 0)], []), Server([RateLatency(20, 2)], []),
...            Server([RateLatency(30, 3)], []), Server([RateLatency(40, 3)], [TokenBucket(1, 40)]),
...            Server([RateLatency(50, 5)], [])]
>>> arrival_shaping = [(3, [0], [TokenBucket(0, 10)]), (1, [3], [TokenBucket(0, 20)]),
...                    (2, [2], [TokenBucket(0, 30)]), (0, [1], [TokenBucket(0, 40)])]
>>> tree = Network(servers, flows, arrival_shaping)
>>> TreeOnlyBlindLP(tree, 0, 'test_tree_blind_backlog.lp').backlog
11.03419973

backlog_objective(file)¶

Writes the backlog objective of the flow of interest.

Parameters:: file – file where constraints are written
Returns:: nothing

causality_constraints(f)¶

Writes the causality constraints for each arrival process of the network

Parameters:: f – file to write the constraints
Returns:: nothing

property delay: float¶

Computes the worst-case delay of the flow of interest

WARNING: Flows must have a single token-bucket. otherwise, put them in the arrival shaping.

Returns:: the delay of the flow of interest
Return type:: float

>>> flows = [Flow([TokenBucket(1, 1)], [3, 4]), Flow([TokenBucket(2, 2)], [0, 3]),
...          Flow([TokenBucket(3, 3)], [2, 4]), Flow([TokenBucket(4, 4)], [1, 3, 4])]
>>> servers = [Server([RateLatency(10, 1), RateLatency(5, 0)], []), Server([RateLatency(20, 2)], []),
...            Server([RateLatency(30, 3)], []), Server([RateLatency(40, 3)], [TokenBucket(1, 40)]),
...            Server([RateLatency(50, 5)], [])]
>>> arrival_shaping = [(3, [0], [TokenBucket(0, 10)]), (1, [3], [TokenBucket(0, 20)]),
...                    (2, [2], [TokenBucket(0, 30)]), (0, [1], [TokenBucket(0, 40)])]
>>> tree = Network(servers, flows, arrival_shaping)
>>> TreeOnlyBlindLP(tree, 0, 'test_tree_blind.lp').delay
10.06361149

delay_objective(file)¶

Writes the delay objective and some additional constraints for the flow of interest

Parameters:: file – file where constraints are written
Returns:: nothing

greedy_shaping_constraints(f)¶

Writes the greedy-shaping constraints for each server of the network

Parameters:: f – file to write the constraints
Returns:: nothing

monotony_constraints(f)¶

Writes the monotony constraints for each arrival process of the network

Parameters:: f – file to write the constraints
Returns:: nothing

service_constraints(f)¶

Writes the service constraints for each server of the network

Parameters:: f – file to write the constraints
Returns:: nothing

time_constraints(f)¶

Writes the time constraints for the network.

Parameters:: f – file to write the constraints
Returns:: nothing

property time_horizon: float¶

Computes the worst-case delay of the flow of interest

WARNING: Flows must have a single token-bucket. otherwise, put them in the arrival shaping.

Returns:: the time-horizon of the flow of interest
Return type:: float

time_horizon_objective(file)¶

Writes the time-horizon for the flow of interest

Parameters:: file – file where constraints are written
Returns:: nothing

write_constraints(file)¶

Writes all the constraints of the flow of interest.

Parameters:: file – file to write the constraints
Returns:: nothing