Data Structure Alternatives - Commenting on efficiency

# Bounty Points: 75

## Branch Instructions
push changes to a new *'ds'* branch

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**Explanation:**

Current clock list management is **inefficient**
Removes items while iterating: `for i in clock: if i[2]==t: clock.remove(i)`
$O(n^2)$ complexity due to `list.remove()` in loop
No priority queue for event scheduling

Current problematic code:
```python
for i in clock:
if i[2] == t:
clock.remove(i) # $O(n)$ operation in $O(n)$ loop
```
### Possible fix/approach:

* Replace list with **`heapq`**-based **priority queue**
* Use event-based scheduling with `(time, event\_data)`
* Alternative: **dictionary** with time as key
* Benchmark before/after improvements
* Document complexity improvements

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### Maintainer notes:

* Current approach causes **performance issues** with large graphs
* Focus on **clock/event management data structure**
* Should maintain same simulation logic

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### Resources:

* Python `heapq` documentation
* Event-driven simulation patterns

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