import numpy as np
PUE = 2
The edge network contains the B-RAS.
$$I_E = R \cdot PUE ⋅ \eta_{E} \cdot \left( I_{E_S} + I_{E_R} \right) $$I_E = R * PUE *eta_E * ( I_E_S + I_E_R )
Here we estimate the cumulated energy intensity for metro transmission from bottom-up parameters. This is then used in the metro model as a single parameter.
$$ I_{M_{TM}} = R \cdot n_{M_R} \left( c_{ON} \cdot I_{ON} + n_{M_{OA}} I_{OA} \right) $$The number of optical amplifiers depends on the distance between routers s_R. If amplifiers are placed each s_A km then $ n_{OA}= \lceil s_R/s_A \rceil $
I_M_TM_BU = R * n_M_R * c_ON * ( I_ON + n_M_OA * I_OA)
Given the cumulative energy intensity from the bottom-up model, we can factor in the empirical results from Coroama et al.
$$ I_{M_{TM}} = u ( I_{M_{TM_{BU}}}, I_{M_{TM_{EMP}}} ) $$I_M_TM = np.random.uniform(I_M_TM_BU,I_M_TM_EMP)
Here we model transmission as one component. We still apply PUE and allocate overcapacity but not redundancy as these are in the bottom-up model and in the empirical values.
$$I_M = PUE ⋅ \eta_{M} ( R \cdot n_{M_R} \cdot I_{M_R} + I_{M_{TM}} ) $$I_M_IP = PUE * eta_M * (R * n_M_R * I_M_R)
I_M_O = PUE * eta_M * I_M_TM
I_M = I_M_IP + I_M_O
Analog to metro: we estimate the cumulated energy intensity for core transmission from bottom-up parameters. This is then used in the core model as a single parameter.
$$ I_{C_{TM_{BU}}} = R \cdot n_{C_R} \left( I_{ON} + n_{C_{OA}} I_{OA} \right) $$The number of optical amplifiers depends on the distance between routers s_R. If amplifiers are placed each s_A km then $ n_{OA}= \lceil s_R/s_A \rceil $
I_C_TM_BU = R * n_C_R * ( I_ON + n_C_OA * I_OA)
Given the cumulative energy intensity from the bottom-up model, we can factor in the empirical results from Coroama et al.
$$ I_{C_{TM}} = u ( I_{C_{TM_{BU}}}, I_{C_{TM_{EMP}}} ) $$I_C_TM = np.random.uniform(I_C_TM_BU,I_C_TM_EMP)
Analog to core: we model transmission as one component. We still apply PUE and allocate overcapacity but not redundancy as these are in the bottom-up model and in the empirical values.
$$I_C = PUE ⋅ \eta_{C} ( R \cdot n_{C_R} \cdot I_{C_R} + I_{C_{TM}} ) $$I_C_IP = PUE * eta_C * ( R * n_C_R * I_C_R)
I_C_O = PUE * eta_C * I_C_TM
I_C= I_C_IP + I_C_O
I_S= s_T/s_SA * I_OSA + R * PUE * 2 I_ST + s_T / C_U
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