def cdn_latency(ag_id1):
neighbors1 = csr.Neighbors(ag_id1)
sumlatency = 0
for n_id1 in neighbors1:
sumlatency += ne_latency(ag_id1, n_id1)
# return 5*int(sumlatency/(len(neighbors1)))
return 5 * float(sumlatency / (len(neighbors1)))
def mincost(self, agent, world, f_id, action):
# 得到agent的所有邻居 ndarray
neighbors = csr.Neighbors(agent.ag_id)
# 提供最小开销的近邻/cdn
# 几个近邻都一样，选择id最小的近邻
min_n = -1
# Cost = param.MAX_INT
Cost = param.MAX_FLOAT
isnehit = False
isreplace = 0 # f_id是否需要替换
for n_id in neighbors:
n_id_lochit = self.neighbors_id_loc(n_id, world)
cost = 0
for ii in n_id_lochit:
if ii == f_id:
isnehit = True
cost = float(param.alpha *
param.ne_latency(agent.ag_id, n_id) +
param.beta * param.traffic_cost_neighbor)
if Cost > cost:
Cost = cost
min_n = int(n_id)
break
d_f_e_t = world.requests.Time_t_Requests_of_Agent_i(
agent.ag_id, world.t - 1)[f_id]
alpha = param.alpha
beta = param.beta
# 近邻命中
if isnehit == True:
l_e_j_t = param.ne_latency(agent.ag_id, min_n)
p_e_j_t = param.beta * param.traffic_cost_neighbor
else: # cdn命中
l_e_j_t = param.cdn_latency(agent.ag_id)
p_e_j_t = param.beta * param.traffic_cost_cdn
# 是否需要加入替换开销
for ff_id in action:
if ff_id == f_id:
isreplace = 1
break
# cost1 = int(alpha*d_f_e_t*l_e_j_t + beta*d_f_e_t*p_e_j_t + beta*isreplace*p_e_j_t)
cost1 = float(alpha * d_f_e_t * l_e_j_t + beta * d_f_e_t * p_e_j_t +
beta * isreplace * p_e_j_t)
return 0 - cost1
def observation(self, agent, world):
# 近邻agent的id从小到大排列
re = []
if world.t >= 0 and world.t <= 167:
neighbors = csr.Neighbors(agent.ag_id)
agent_cache_state = agent.state.cache_state
agent_request = world.requests.Time_t_Requests_of_Agent_i(
agent.ag_id, world.t)
re.append(agent_cache_state)
re.append(agent_request)
for n_id in neighbors: # 近邻id从小到大
re.append(world.agents[n_id].state.cache_state)
re.append(
world.requests.Time_t_Requests_of_Agent_i(n_id, world.t))
re = np.asarray(re)
return re
def observation(self,agent,world):
# 近邻agent的id从小到大排列
re = []
if world.t >=0 and world.t <=167:
neighbors = csr.Neighbors(agent.ag_id)
agent_cache_state = agent.state.cache_state
agent_request = world.requests.Time_t_Requests_of_Agent_i(agent.ag_id,world.t)
re.append(agent_cache_state)
re.append(agent_request)
for n_id in neighbors: # 近邻id从小到大
re.append(world.agents[n_id].state.cache_state)
re.append(world.requests.Time_t_Requests_of_Agent_i(n_id,world.t))
# if len(re)<18:
# for j in range(18-len(re)):
# re.append(list(np.zeros(100,dtype=np.int32)))
re = np.asarray(re)
return re
def cdn_hit(self, agent, world, f_id):
neighbors = csr.Neighbors(agent.ag_id)
min_n = -1
Cost = param.MAX_FLOAT
isnehit = False
for n_id in neighbors:
n_id_lochit = self.neighbors_id_loc(n_id, world)
cost = 0
for ii in n_id_lochit:
if ii == f_id:
isnehit = True
cost = float(param.alpha *
param.ne_latency(agent.ag_id, n_id) +
param.beta * param.traffic_cost_neighbor)
if Cost > cost:
Cost = cost
min_n = int(n_id)
break
# 近邻命中
if isnehit == True:
return False
else:
return True