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MPVR v1 - Multi-Path Vector Routing
Preuve de concept MTTV-flp
Simule Raft vs MPVR avec routage énergétique et mémoire (4e dimension)
"""
import random, itertools
def simulate(rounds=100, failure=50):
nodes=[0,1,2,3,4]
failed=1
memory={n:1.2 for n in nodes}
results=[]
random.seed(101)
for r in range(1,rounds+1):
alive=[n for n in nodes if not (n==failed and r>=failure)]
followers=[n for n in [1,2,3,4] if n in alive]
costs={}
for i,j in itertools.combinations(nodes,2):
if i in alive and j in alive:
c=random.uniform(0.5,2.0)
costs[(i,j)]=costs[(j,i)]=c
def best(a,b):
best=costs.get((a,b),9)
for m in alive:
if m in (a,b): continue
c=costs.get((a,m),9)+costs.get((m,b),9)
if c<best: best=c
return best
raft=sum(costs.get((0,f),0) for f in followers)+sum(sorted(costs.get((f,0),0) for f in followers)[:3])
cand=sorted([(best(0,t),t) for t in followers])
v3=sum(c for c,_ in cand[:3])
for t in followers: memory[t]=0.8*memory[t]+0.2*best(0,t)
avg=sum(memory[t] for t in followers)/len(followers)
quorum=2 if avg<1.25 else 3
chosen=[]
for c,t in cand:
if c<=memory[t]*1.15: chosen.append((c,t))
if len(chosen)>=quorum: break
if len(chosen)<quorum: chosen=cand[:quorum]
v4=sum(c for c,_ in chosen)
results.append((r,raft,v3,v4))
return results
if __name__=="__main__":
for r,raft,v3,v4 in simulate()[:5]:
print(r, raft, v3, v4)
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