We consider two different settings of EWA input parameters: (i)
,
, and
; and (ii)
,
, and
. As a consequence, lightpaths are attempted to be released when their utilization is relatively low (i.e., less than 10%). On the other hand, when the lightpath utilization is greater than 50% (90%), additional lightpaths are established in order to avoid network overload. Moreover, the utilization of the last active lightpath on each logical link should not exceed 50% (90%) when a lightpath is released for energy saving. The simulation framework begins with all LCs in AM. For each set of traffic data, we perform a simulation of two days assuming the same traffic during both days. Results of the first day (transient period) of simulation are discarded in order to avoid its influence on final results. Differently from [
6], we neglect the initial switching of LCs from the AM into SM in the final results.
6.1. Influence of Temporal Traffic Variation
We first look at the influence of the ratio
on energy consumption of LCs (
Figure 3) and
(
Figure 4). As expected, energy consumption
increases when the traffic scaling factor is increased. For all the three scaling factors, energy consumption
increases also when the ratio
increases (see
Figure 3a–c). When
the energy consumption tends to be equal to the one experienced by the SBN, since in this case no temporal traffic variation is imposed. As a result, the EWA is less effective in saving energy. This is particularly true for the most conservative configuration of EWA (i.e., a maximum utilization of the last lightpath
ψ set to 0.5). In this case, the energy consumption of the network using EWA is identical to the energy consumption of the SBN already when the ratio
is set to 0.75 for all the three scaling factors. Moreover,
decreases when the ratio
is increased (
Figure 4). For example,
when the ratio is set to 0.75 or higher in
Figure 4c. This means that, in such cases, all the LCs are always powered on. As expected,
can be also observed for the SBN, since all LCs are in AM. Moreover, when the scaling factor is equal to 688.57 and the EWA parameter
ψ is set to 0.9,
drops below 1 when
= 1.0. This effectively means that the lifetime of LCs is increased due to adoption of SM, thanks to the fact that different LCs are always kept in SM. In this case, in fact, a high lightpath utilization (even higher than the one used to design the SBN) is allowed. On the contrary, when the traffic variation is high (
), the energy consumption is reduced w.r.t. the one of SBN (
Figure 3). However, this comes at the cost of increasing the AF (
Figure 4) and therefore a lifetime decrease. Finally, we also confirm based on
Figure 3 that more energy is consumed with the first EWA setting (
,
) than with the second EWA setting (
,
). This is due to the fact that with the second setting, the lightpaths can sustain higher utilization.
In the next part, we vary the length of the set of low-demand time periods
in the range 0–7.5 h. Energy consumption
and
are shown in
Figure 5 and
Figure 6, respectively. As expected, the longer the period when traffic is low, the lower the energy consumption. This can be observed for all the three scaling factors and the two EWA settings (with lower energy consumption achieved with the more liberal EWA setting). The length of the low-demand periods does not affect
as shown in
Figure 6. This is due to the fact that traffic increases and decreases only once during the set of all considered time periods
T (see
Figure 1) even for
equal to 0. However,
values are consistently higher than 1. It is interesting to note that the
values are higher for the more conservative EWA setting. This is due to the fact that the targeted overprovisioning (
) leaves lower lightpath capacity for transporting traffic (with no unexpected variations). Given the same traffic and lightpath capacity
C as input, the lightpath (and corresponding LCs) have to be more frequently activated/deactivated when
(corresponding to usable lightpath capacity 0.5·40 Gbps) than when
(corresponding to usable lightpath capacity 0.9·40 Gbps).
We varied also the lengths of periods when traffic increases and decreases (
and
) in the range 0–3.75 h. Results in terms of energy consumption and
are shown in
Table 2 for the scaling factor 1147.62. Results for other scaling factors are similar, however we chose the highest scaling factor due to the peculiarity of high energy consumption and low
for the instance where
and the conservative EWA setting is used. This is explained by the fact that (small) traffic loss (1%) occurs in this (and only this) instance. The empty set
corresponds to an immediate transition from
to
(and vice-versa) for all
. Focusing on energy consumption, the longer the transition period is, the lower
is. However, differences between energy consumption values are very small. Regarding the AF, it remains stable over all considered sets
and
unless traffic loss occurs.
Eventually, we come back to the variation of ratio
keeping the total load fixed (as explained in
Section 5.1). Energy consumption
and
are shown in
Figure 7 and
Figure 8, respectively. First, we point out that the figures are similar to
Figure 3 and
Figure 4. This is due to the fact that although the total load is fixed for the results shown in
Figure 7 and
Figure 8, it is only slightly different than the load for the results shown in
Figure 3 and
Figure 4. The original ratio
based on measurements is equal to 0.41. Regarding energy consumption, the most significant difference can be observed in
Figure 7b for
equal to 1.0 (compare with
Figure 3b). Not only is the energy consumption lower with both EWA settings, but also the more conservative EWA setting does not encounter capacity problem in
Figure 7b. Furthermore, when we move to the analysis of AF, we can see that the corresponding
achieves values below 1.0 in
Figure 8b. This is explained by the fact that there is no temporal traffic variation (
) and effectively the maximum traffic demand
takes lower value than the one from measurements in order to keep total demand over the whole set
T fixed. Eventually, we point out also the increased values of
for
(
Figure 8). This is a result of increased traffic amplitude without decreasing total load. Lower load in
Figure 4 leads to lower values of
.
6.2. Influence of Spatial Traffic Variation
We focus on the spatial traffic variation fixing
,
, and
as well as both
and
based on traffic measurements (as explained in
Section 5.1). EWA is executed on the scenarios with real TZM Θ for different values of
k, different values of traffic scaling factor
s, and 5 randomly generated SVMs. In particular, we choose different values of
k between 0 (corresponding to the case in which no time shift is set) and 144 (in which a maximum time shift of 12 h is enforced). Moreover, we consider the case of zero TZM (i.e., the case when all network nodes are assumed to be located in the same time zone and
) denoted as “NO TZM”.
Figure 9 reports the obtained results in terms of energy consumed by all LCs in the network over a day
.
Figure 10 reports the obtained results in terms of
over all LCs in the network after 14 days of network operation. In both cases, averages over all considered SVMs are reported (bar plots) and complemented with the maximum and minimum values (error bars).
Interestingly, it is possible to save energy for most values of the time displacement parameter
k. This can be clearly observed in the case of a scaling factor 688.57 (
Figure 9b). Average energy consumption increases when traffic is scaled with the factor 1147.62 and when
k becomes high (
Figure 9c). This is caused by the existence of multiple shortest paths between some node pairs. In particular, EWA sometimes finds other shortest paths than the ones used in the SBN and therefore occasionally has problems with satisfying all the traffic demands. We have verified that unsatisfied demands never exceed 6%. EWA does not put LCs into SM in such cases, and therefore energy consumption is identical with that in the SBN (compare SBN in
Figure 9c with the maximum energy consumption values for
72). This effect of occasional routing incompatibilities is even stronger when the scaling factor is low (see high and intermediate values of
k in
Figure 9a). On the contrary, it is always possible to save energy when no TZM is used and when
k is equal to 0, i.e., when all demands have an off-peak zone occurring during the same hours (cf.
Figure 2a) or when the peak times only depend on the time zone where nodes are located.
Considering
(
Figure 10), it is interesting to note that these figures are complementary to the energy figures, i.e., the lower the energy consumption, the higher the AF.
usually takes values between 1.2 and 2.5 meaning that energy saving reduces devices lifetime. For the cases in which EWA encounters routing incompatibilities,
decreases. This happens more frequently with increasing
k. In the cases when most LCs are constantly powered on (i.e., the energy consumption of the network using EWA is close to the one of the SBN), few power state reconfigurations are introduced, resulting in an AF close to 1 (see, e.g., the case for
in
Figure 10a for
and
).
6.3. Influence of HW Parameters
In the following, we vary the HW parameters
and
χ, which are used in the computation of the AF of the LCs. Specifically, since EWA does not consider the lifetime of the LCs to take power state decisions, the variation of such parameters has no influence on the total energy consumption
. On the contrary, the experienced LCs lifetime is heavily impacted by the variation of
and
χ, as reported in
Figure 11. Clearly, the frequency of AM-SM-AM cycles
χ significantly affects the results. For example, when
χ is varied and
,
falls in the ranges 2.52–17.67, 2.08–14.23, and 2.06–14.21 units for the scaling factors 229.52, 688.57, and 1147.62 respectively. On the other hand, the influence of
on
is minor. When
h/cycle and
is varied,
falls in the ranges 2.42–2.62, 1.92–2.24, and 1.88–2.23 units for the scaling factors 229.52, 688.57, and 1147.62 respectively.