Downstream-based Scheduling for
Energy Conservation in Green EPONs
Item Type
Article
Authors
Chen, Shen; Dhaini, Ahmad R.; Ho, Pin-Han; Shihada, Basem;
Shen, Gangxiang; Lin, Chih-Hao
Citation
Downstream-based Scheduling for Energy Conservation in Green
EPONs 2012, 7 (5) Journal of Communications
Eprint version
Post-print
DOI
10.4304/jcm.7.5.400-408
Publisher
Engineering and Technology Publishing
Journal
Journal of Communications
Rights
Archived with thanks to Journal of Communications
Download date
06/05/2020 01:17:31
Link to Item
http://hdl.handle.net/10754/348537
1
Downstream-based Scheduling for Energy
Conservation in Green EPONs
Shen Chen∗ , Ahmad R. Dhaini∗ , Pin-Han Ho∗‡ , Basem Shihada† , Gangxiang Shen‡ , and Chih-Hao Lin§
∗ Dept. of Electrical and Computer Engineering, University of Waterloo, Canada
{s92chen, adhaini, p4ho}@uwaterloo.ca
† Dept. of Computer Science, KAUST, KSA
‡ Dept. of Electronic and Information Science, Suzhou University, China, gxshen@suda.edu.cn
§ Dept. of Information Management Science, Chongyuan University, Republic of China, chlin@cgu.edu.tw
Abstract—Maximizing the optical network unit’s (ONU) sleep
time is an effective approach for achieving maximum energy
conservation in green Ethernet passive optical networks (EPONs).
While overlapping downstream and upstream ONU transmissions
can maximize the ONU sleep time, it jeopardizes the quality
of service (QoS) performance of the network, especially for
downstream traffic in case the overlapping is based on the
upstream time slot. In this paper, we study the downstream
traffic performance in green EPONs under the limited service
discipline and the upstream-based overlapped time window.
Specifically, we first derive the expected mean packet delay, and
then present a closed-form expression of the ONU sleep time,
setting identical upstream/downstream transmission cycle times
based on a maximum downstream traffic delay requirement.
With the proposed system model, we present a novel downstream
bandwidth allocation scheme for energy conservation in green
EPONs. Simulation results verify the proposed model and highlight the advantages of our scheme over conventional approaches.
Index Terms—Green EPON, Dynamic Bandwidth Allocation,
Queuing Theory, Energy Efficiency.
I. I NTRODUCTION
E
Thernet Passive Optical Network (EPON) has been considered as one of the most attractive broadband access
solutions. It comprises an Optical Line Terminal (OLT) that
resides in a Central Office (CO) and connects through a single
fiber a set of Optical Network Units (ONUs) located at the
customer premises. In recent years, EPON has been subject
to extensive research efforts related to dynamic bandwidth
allocation (DBA), quality-of-service (QoS) support, and lately
energy efficiency.
To achieve efficient energy conservation in EPON, each
ONU should be put into sleep mode as long as the predefined QoS constraints are not violated. To meet this goal,
most previous studies manipulate DBA schemes such that
an ONU can be put into sleep while the OLT is receiving
data from other ONUs [1], [2], [3], [4]. Previous studies are
based on a common assumption that the OLT can buffer the
downstream traffic of a “sleeping” ONU until the ONU wakes
up. Their common design goal is to minimize the ONU idle
time as much as possible while allowing the OLT to schedule
collision-free ONU transmissions over the shared media.
Two energy management mechanisms for EPONs have been
reported in [2], namely Upstream Centric Scheduling (UCS)
and Downstream Centric Scheduling (DCS). Using UCS, the
OLT buffers the downstream traffic of an ONU and starts
transmitting it only in the designated upstream time slot of
the ONU so as to reduce the ONU active time. Conversely
with DCS, the OLT launches downstream traffic to the ONU
whenever available. Thus, an ONU switches to the active mode
whenever it needs to transmit upstream traffic or to receive
downstream traffic. Clearly, UCS saves more energy by overlapping the downstream and upstream transmissions, while
DCS can achieve better QoS performance since the upstream
and downstream transmissions are scheduled independently
and hence can be manipulated separately.
Green Bandwidth Allocation (GBA) has been reported in
[4] as an effective framework for energy saving in PON. It
is featured with a hybrid cyclic/deep sleep mode enabled at
the ONUs, batch-mode transmission at OLT and ONUs, and
UCS-based bandwidth allocation. With GBA, the ONU goes
into the sleep mode for a certain amount of time before waking
up to send/receive a batch of buffered upstream/downstream
traffic. Due to the batch mode transmission, an ONU can
avoid frequent switching between on and off state in order to
minimize the overhead time during the switching. The authors
of [3] further developed an analytical formulation for the delay
of upstream traffic based on which the maximum sleep time
is derived for each ONU.
To the best of our knowledge, all the previously reported
energy saving schemes based on the UCS protocol have only
considered the upstream packet delay constraint to compute
the maximum ONU sleep time; while the QoS constraints of
downstream traffic has never been considered. Note that under
UCS, the downstream transmission of an ONU occurs during
the upstream transmission period, thus the downstream QoS
may not be ensured. Therefore, when the downstream traffic is
much heavier than the upstream traffic (which is very common
in practical PON systems), the maximum sleep time of each
ONU by merely analyzing the upstream direction using the
upstream traffic delay constraint will be very far from accurate.
We envision the importance of a downstream scheduling
scheme at the OLT that ensures the packet delay constraint
for each ONU. Thus, the paper investigates the maximum
sleep scheduling for both downstream and upstream directions
in the green EPON design under the GBA framework. We
firstly consider a generic scheduling mechanism by allowing
2
each ONU to wake up and start downstream and upstream
transmissions in a round-robin manner, in which a mean packet
delay expression is derived with the limited service discipline.
To the best of our knowledge, delay analysis based on the
limited service discipline has only been reported in [5], strictly
for upstream bandwidth allocation due to its complexity.
Hence, the novelty of our work is that we consider it for
downstream bandwidth allocation, and for ONU sleep time
computation. Moreover, in legacy EPONs, the downstream
traffic is broadcast in nature, and hence downstream scheduling has never been considered as a research problem before;
whereas in terms of energy conservation, it is considered as a
main research challenge for the design of green EPONs [3].
With the proposed analytical model, we develop a novel
scheduling algorithm, which runs at the OLT in order to
compute the maximum ONU sleep time without violating
the delay constraints for both the upstream and downstream
transmissions. Extensive simulations are conducted to verify
the proposed analytical model and highlight the effectiveness
of the proposed scheduling algorithm.
The rest of the paper is organized as follows. Section II
provides a survey on the related work in green EPONs. The
system model is discussed in Section III. Section IV presents
the downstream delay analysis along with the closed-form
expressions of mean packet delay and maximum ONU sleep
time. Section V presents the proposed energy efficient downstream traffic scheduling protocol. Simulation and numerical
results are given in Section VI. Section VII concludes the
paper and discusses our future work.
II. R ELATED W ORK
Although putting ONUs into sleep has been the most
promising energy saving methods in PONs, an efficient and
effective implementation of the ONU sleep mode is still
subject to many open challenges. The study in [6] provided
feasible implementations of the ONU sleep mode in PONs
and discussed related issues such as ONU-OLT clock synchronization and QoS requirements of delay sensitive applications.
With the introduction of fast clock recovery circuit in [7],
energy can be saved in PONs using the proposed Just-In-Time
(JIT) sleep control scheme, which allows the ONU to sleep
within the DBA cycle (typically equal to 2 ms). Note that the
active period and the subsequent sleep period, including the
overhead time that is due to the switching from sleep to active
mode, form the ONU sleep cycle. It is clear that the sleep
time in the sleep cycle at an ONU is at least the time when
other ONUs are transmitting or receiving traffic. Thus the
synchronization and traffic detection functions are essential.
To ensure precise synchronization, the study in [8] proposed a
fast ONU sleep mode which enables an ONU to automatically
wake up and start transmitting/receiving traffic in the allocated
time slot. In [9], a new Medium Access Control (MAC)
protocol was presented for the ONUs to periodically wake up
within the designated time slots when the system is operating
at low network load. Here, the ONU can turn off its transmitter
and receiver to reduce its power consumption. Although the
study showed the potential to achieve energy saving by putting
the ONUs into sleep, it does not mention how to determine the
sleep time to achieve better performance. From the literature
review in [3], it is clear that the previously reported studies on
green PONs have neither provided a comprehensive approach
to determine the maximum ONU sleep time, nor investigated
the downstream packet scheduling problem, which is crucial
for meeting the QoS constraints of downstream and upstream
traffics.
A. Overview of UCS-based GBA Framework
The study in [3] investigated a UCS-based framework for
achieving energy-efficient EPON design, based on a green
bandwidth allocation (GBA) framework, which aims to maximize the ONU sleep time and reduce the total overhead time
required to switch the ONU from the sleep mode to the active
mode. Specifically, the authors proposed a batch mode-enabled
transmission at the ONUs and OLT, which enlarges the DBA
cycle so as to reduce the impact of the ONU on-off overhead.
This is nonetheless at the expense of QoS impairment in
terms of packet delay. To address this problem, the authors
proposed a new analytical model to determine the optimal
sleep time of each ONU based on a maximum upstream packet
delay constraint. Simulation results demonstrated that GBA
is able to achieve up to 90% energy saving compared to
conventional legacy DBA schemes. The main advantage of the
GBA framework in [3],[4] is that each ONU can have longer
sleep than that with any other reported approach thanks to the
developed analytical model and the batch mode transmission,
which is vital in achieving the best energy saving without
violating the QoS constraints. However, since the downstream
QoS was not considered, a remaining challenge is to design
a sleep control mechanism that meets the delay constraints
of downstream traffic in addition to the ones considered for
upstream traffic.
Fig. 1: The OLT model for the downstream traffic scheduling.
III. S YSTEM M ODEL
With UCS, the OLT and ONUs buffer the downstream and
upstream traffic, respectively, and overlap their transmissions
for a shorter (or longer) ONU active period (sleep time). By
assuming the downstream traffic will be way heavier than that
the upstream, the maximum sleep time should be determined
according to the downstream traffic. Thus, it turns out to be
an important problem in the evaluation of the downstream
traffic buffer delay. Without loss of generality, we assume
that the OLT is equipped with a downstream queue for each
3
ONU. Fig.1 shows the OLT system considered in this study,
which can be taken as a queuing system and a multi-access
channel, such that the ONUs queues are the ”accessing” users.
In other words, each queue has packets that are buffered and
waiting to be transmitted to the corresponding ONU, and only
one queue can have access to the shared media. Thus, it is
feasible to take the channel as a common server accessed
by the downstream queues. By considering the heterogeneous
nature of downstream traffic, a fixed channel assignment
that allocates fixed time slots or wavelength bands is not
efficient. To schedule transmissions of several data streams
into a statistical multiplexing system, the paper introduces
a quasi-leaved polling model in which downstream traffic is
scheduled so as to avoid collisions. We consider the quasileaved polling method because it allows the OLT to wait until
all the REPORT messages (defined in the multi-point control
protocol [MPCP]) are received before running the DBA. It is
essential to the proposed scheduling algorithm which requires
the complete knowledge of traffic demand of each ONU. Note
that interleaved polling with adaptive cycle time (IPACT) [10]
and any other version of interleaved polling schemes may
have better network resource utilization, but they cannot enable
general sleep time analysis since the OLT can only learn about
one ONU’s status at a time.
The OLT is the central controller of the network, which
decides the scheduling of upstream and downstream transmissions for all the ONUs. Once a scheduling decision is
made, MPCP’s GATE control message is used to inform the
ONU of both its allocated transmission size and scheduled
sleep time within each DBA cycle. Let Tcycle denote the
periodic transmission cycle in which the OLT completes the
downstream transmissions for all the ONUs, and without loss
of generality let each ONU be served in a round-robin manner.
With this regards, the upstream and downstream transmissions
of the ONUs have an identical transmission cycle time Tcycle .
Fig. 2: Overlapped upstream and downstream traffic scheduling.
Each downstream queue at the OLT buffers the traffic
destined for the corresponding ONU until the reserved downstream transmission time slot arrives, which is also used as
the upstream transmission slot as shown in Fig. 2. Upon
the arrival of the downstream time slot, the OLT transmits
the data in queue i to the corresponding ONU i. Note that
Tcycle in the figure may vary from one cycle to the other
depending on the traffic load, the number of ONUs (N ),
and the maximum transmission window size. The processing
time of a GATE message plus the guard interval between two
contiguous downstream queues serves as a fixed reservation
interval, denoted as V .
With each ONU being served in a round robin manner, the
sleep cycle can be seen as the time for all the other ONUs to
finish the channel access once. As mentioned earlier and as
demonstrated in Fig. 2, the sleep cycle of ONU i comprises
three components: the sleep time, the active transmission time,
and the overhead time required to switch the ONU from the
sleep mode to the active mode. The overhead time accounts for
the free running clock drifts, the ONU clock recovery time,
and the synchronization to the network after recovering the
OLT clock [8].
During the sleep mode, the ONU turns off both its transmitter and receiver so as to save energy. There are two main sleep
modes defined in the gigabit-PON standard: deep sleep and
fast sleep. In the former, the downstream or upstream traffic
could be lost because all the ONU functions are shut down.
In the latter, on the other hand, the ONU maintains the timing
and traffic detection functions in order to wake up whenever
new traffic arrives. With the proposed system model, the ONU
goes into the sleep mode for a given amount of time (which
is pre-scheduled and manipulatively calculated) before waking
up to send/receive the buffered upstream/downstream traffic.
Therefore, it allows an ONU to go into deep sleep and save
the maximum energy during the sleep mode.
IV. D OWNSTREAM D ELAY A NALYSIS
In this section, we analyze the mean packet delay of
downstream traffic arriving at the OLT, which will then serve
as an important basis for the determining the optimal ONU
sleep time, as well as the proposed downstream scheduling
scheme The notations used in the analysis are summarized in
Table I.
We first start by modeling the system as a gated N - user
reservation system, in which the packets that arrive prior to
the user’s reservation interval are all transmitted, as shown
in Fig. 3. Without loss of generality, to make our analysis
tractable, we assume that downstream data packets arrive at
the OLT according to a Poisson distribution at a mean rate of
λ/N . Hence, the utilization factor of the total ingress traffic
load is ρ = λX, such that X denotes the mean service time
of each packet, and X 2 is the second moment of service time.
Let V and V 2 denote the first two moments of the reservation
interval, respectively. The corresponding packet delay analysis
of a gated1 reservation system is given as follows [11]:
E(W )
= E(R) + E(Q) + E(Y )
λX 2
(N + 2 − ρ)V
σ2
=
+
+ V
2(1 − ρ)
2(1 − ρ)
2V
(1)
(2)
where E(.) denotes the expected value of the operand. Hence,
E(W ) denotes the expected packet delay in the queue, E(R)
is the expected mean residual time, E(Q) is the expected
1 Note that the gated service in EPON is different from the commonlyknown gated service in queueing theory. For more details on EPON’s gated
service, we refer the reader to [10].
4
TABLE I: Notations used in the analysis
Notation
Description
i
the queue index and the related ONU index
Di,ξ
maximum allowable packet delay at ONU i
in the downstream transmission
Si
Sleep cycle of ONU i
s
Ti,d
downstream sleep time at ONU i
a
Ti,d
active period of the downstream transmission
at ONU i
o
Ti,d
overhead time at ONU i for downstream
Xi,ξ
service time of packet ξ to ONU i
Xi,ξ
mean service time of packet ξ to ONU i
2
Xi,ξ
the second moment of service time
Wi,ξ
the mean packet queuing delay destined for
ONU i
Di,ξ
total delay of a packet to ONU i
Tprop
Packet propagation time
ξ
a packet of the downstream transmission
λi
arrival packet rate to queue i at OLT
Qi
the average queue size of queue i at OLT
C
the line rate for upstream/downstream e.g.
1Gbps
ρi
utilization factor of queue i at OLT
ρ
utilization factor of total traffic load at OLT
mean service delay, E(Y ) is the expected mean reservation
delay, and σV2 is the variance of each reservation time. For
consistency, the expected value notation will be replaced by
the mean value one (e.g., E(Y ) will be referred to as Y ).
can be expressed as:
P W max
ρ
.
=
1−ρ
CV
(3)
CV ρ
.
− ρ)
(4)
Thus,
P =
W max (1
Lemma 4.1: The OLT can only transmit W max bytes for
each queue per downstream cycle, thus the additional mean
reservation delay for a N -user reservation system with the
limited service discipline can be expressed as:
λW V XCP
.
(5)
W max
Proof: Let Ł be the total queue size at the OLT (of all
queues). Hence the mean queue size is Ł/N . Using Little’s
Law, we have Ł = λW . Furthermore, using the limited
service discipline, we assume that the OLT can only transmit
a maximum of Lmax packets per cycle. Hence, the number
of groups NλW
Lmax leads to additional reservation cycles with
the limiting probability P , such that every cycle has N V
reservations. Thus, we get:
∆Y =
∆Y =
λW V
λW
× P × N V = max × P.
max
NL
L
Also note that, Lmax = W max /(XC). Thus, ∆Y =
λW V XCP
, which ends the proof.
W max
Theorem 4.1: Under UCS-based GBA, with limited service
scheduling discipline, the mean downstream packet delay is
determined by:
W =
λX 2 + (N + 2 − ρ)V + (1 − ρ)σV2 /V
2(1 − ρ −
(CV )2 λXρ
(W max )2 (1−ρ) )
Proof: Following Lemma 4.1, the mean reservation delay
of a N -user reservation scheduling system under the limited
service discipline, is given by:
(N + 1)V
λW V XCP
+
2
W max
From (1) and (6), we get:
W =
Fig. 3: Downstream traffic scheduling with N queues at the OLT.
The following analysis is based on the limited service
discipline, which is the bulk of our work. Let the maximum
transmission window be denoted as T max , and the maximum
number of bytes transmitted in T max be denoted as W max . Let
the probability that a reservation interval is followed by a data
interval of size W max in steady-state be denoted as P , and the
requested window size (in bytes) that is less than W max be
denoted as ν. Consequently, the time required to transmit ν
bytes is given as T ν . Clearly we have T max = W max /C and
T ν = ν/C.
The ratio between data intervals and reservation intervals
.
Y =
(6)
λX 2 + (N + 2 − ρ)V + (1 − ρ)σV2 /V
2(1 − ρ − λV P XC/W max )
(7)
and from (4) and (7), we get:
W =
λX 2 + (N + 2 − ρ)V + (1 − ρ)σV2 /V
2(1 − ρ −
(CV )2 λXρ
(W max )2 (1−ρ) )
(8)
Remark 4.1: If W max is sufficiently large, expression (8)
approaches expression (2). This is reasonable because, in
EPON systems, a limited service with a very large window
size W max converges to a gated service discipline.
Using Little’s Law, the average size of queue i at the OLT is
given as follows:
Qi = λW /N
(9)
5
Fig. 4: Sleep cycle at ONU i for the downstream traffic scheduling.
A. Maximum ONU Sleep Time
2
2 , ρ = λ X , and
Let E(Xi,ξ ) = Xi,ξ , E(Xi,ξ
) = Xi,ξ
i
i i,ξ
λi = λ/N . In steady-state, we have:
ρ=
N
X
ρi = λXi,ξ < 1.
(10)
i=1
A diagram of the sleep cycle Si under the downstream traffic
scheduling for ONU i is shown in Fig. 4. The sleep cycle is
given as follows:
Si =
s
Ti,d
+
o
Ti,d
+
a
Ti,d
.
(11)
Packet, ξ, will be subject to a total delay Di,ξ , such that:
Di,ξ (ξ) = Wi,ξ (ξ) + Tprop + Xi,ξ (ξ).
(12)
As ξ → ∞, the expected total delay of a packet is given as
follows:
Di,ξ = Wi,ξ + Tprop + Xi,ξ ,
(13)
where Tprop is the propagation delay [12]. From this expression, the maximum allowable packet delay can be expressed
as
Wi,ξ ≤ Di,ξ − Tprop − Xi,ξ .
(14)
Hence, Di,ξ − Tprop − Xi,ξ is the upper bound of the expected
packet delay Wi,ξ in the system.
Theorem 4.2: Under UCS-based GBA, using the limited
s
service discipline, the sleep time Ti,d
, in which ONU i can
sleep during downstream transmissions, is determined by the
following expression:
s
o
Ti,d
= Si − Ti,d
− Nρ ×
2 +(N +2−ρ)V +(1−ρ)σ 2 /V
λXi,ξ
V
(CV ρ)2
2(1−ρ− (W max )2 (1−ρ) )
.
Proof: Using the delay expression (8), the expected
downstream waiting time for packet ξ destined for ONU i
is given as:
Wi,ξ =
2 + (N + 2 − ρ)V + (1 − ρ)σ 2 /V
λXi,ξ
V
2(1 − ρ −
(CV ρ)2
(W max )2 (1−ρ)
.
(15)
)
By expressions (9) and (15), we have:
λWi,ξ Xi,ξ
,
(16)
N
which provides an expression for ONU i’s reserved downstream data interval. From (11), (15), and (16), we get:
which ends our proof.
Finally, using the above analysis, we can extract the minimum sleep time for every ONU i as follows:
ρ
s,min
o
× (Di,ξ − Tprop − Xi,ξ ).
Ti,d
= Si − Ti,d
−
(18)
N
s
Remark 4.2: The maximum ONU sleep time Ti,d
is derived
under the assumption that the downstream traffic is heavier
than the upstream one. Nonetheless, in case the traffic on both
directions is “balanced”, the maximum sleep time based on the
upstream traffic (such as the one in [4]) can be applied. Hence,
the maximum ONU sleep time Tis,max will then be set as
the minimum between downstream-based and upstream-based
values as follows:
s
s
Tis,max = min(Ti,d
, Ti,u
)
(19)
V. P ROPOSED P ROTOCOL FOR E NERGY-E FFICIENT
S CHEDULING
Algorithm 1 Energy efficient downstream traffic scheduling
1: while (1) do
2:
ONU i sends a request to go to sleep;
3:
OLT sends a GATE message to ONU i telling it to sleep;
4:
OLT stops sending data to OUN i;
5:
T=50 ms;
{/* Set GATE timeout and REPORT timeout */}
6:
ONU i goes to sleep;
7:
while (Timer t < Tis,max ) do
8:
ONU i sleeps;
9:
ONU i buffers all upstream data packets;
10:
OLT buffers downstream data packets;
11:
if (t == T) then
12:
ONU i wakes up;
13:
OLT sends an empty GATE message to ONU i;
14:
ONU i sends an empty REPORT message to OLT;
{/* Empty GATE and REPORT messages are sent
every 50 ms to keep ONU i alive. */}
15:
ONU i sends a request to remain in sleep mode;
16:
end if
17:
T = T + 50 ms; {/*for the next timeout */}
18:
OLT instructs ONU i to go back to sleep;
19:
end while
20:
ONU i quits sleep mode and enters the active mode;
21:
ONU i executes clock recovery and synchronization;
22:
OLT sends a GATE message specifying the upstream
time slot to ONU i;
23:
OLT and ONUs transmit in the upstream and downstream directions simultaneously (as per UCS);
24: end while
a
Ti,d
= Qi × Xi,ξ =
s
Ti,d
o
a
o
= Si − Ti,d
− Ti,d
= Si − Ti,d
−
2 + (N + 2 − ρ)V + (1 − ρ)σ 2 /V
λXi,ξ
ρ
V
×
,
2
ρ)
(CV
N
)
2(1 − ρ −
max 2
(W
) (1−ρ)
(17)
The pseudo-code of the proposed protocol for energyefficient scheduling under limited service discipline is given
in Algorithm 1. To make the developed technique applicable
to practical EPON systems, a scheduling protocol is provided
in this subsection, aiming to achieve the maximum sleep time
at each ONU according to the analysis result as in (19). It
is understood that the proposed protocol should comply with
MPCP (Multi-Point Control Protocol) which defines a timeout
6
interval of 50 ms [13], after which an ONU will be fully
deactivated. Thus, the proposed protocol has each ONU wake
up every 50 ms just to exchange empty REPORT and GATE
messages with the OLT in order to function properly.
As shown in Algorithm 1, all the downstream and upstream
packets of ONU i are firstly stored in the buffer at the OLT
and ONU, respectively, within the sleep period of the ONU.
Step 22 in the algorithm is arranged to transmit all the packets
buffered at OLT and ONU i, immediately after the ONU quits
sleeping and enters the active mode.
Fig. 6 shows that with a typical number of ONUs and
utilization factor ρ, the average packet delay increases as the
transmission window size W max decreases, which is under
our expectation due to the system behavior of the limited
service discipline (see the expression (15)). Although it can be
observed via simulations, the optimal maximum transmission
window size under the limited service discipline via the analytical derivation is obviously an interesting topic to explore,
which is nonetheless left for future studies.
VI. S IMULATION AND N UMERICAL R ESULTS
In this section, simulations are conducted to verify the
proposed analytical model for downstream traffic delay and
demonstrate the effectiveness of the proposed scheduling
algorithm. We are particularly interested in observing the
impact on the scheduling performance due to the limited
service discipline and the increase on the number of ONUs in
the system. The section comprises three parts: (1) analytical
model observation, (2) validation of the analytical model using
simulations, and (3) performance measurement of presented
scheme versus counterparts. MATLAB is used to simulate the
proposed scheme and the scheduling algorithm in all three
parts. In (2) and (3), the inter-arrival time of downstream
packets destined for an ONU is generated according to an
exponential distribution. A gated UCS-based upstream bandwidth allocation scheme is employed with a pre-defined GATE
message size of W max . Each ONU has an equal share of the
cycle time, Tcycle .
Fig. 5: Average Packet Delay with W max =15,500 bytes.
TABLE II: System Parameters
Parameters
Description and Values
N
Number of ONUs: 8,16 and 32
C
Line rate of downstream 1 Gbps [13]
G
Guard interval: 1 µs [13]
Tcycle
W
max
Cycle time (N=16): 2 ms [13] and 1.3 ms
Maximum transmission window size: 15,500
bytes [13], 15,000 bytes, etc.
V
Mean reservation interval: 1.512 µs
Xi,ξ
Mean service time: 5.08976 µs
2
Xi,ξ
The second moment of service time: 51.1604
(µs)2 1
o
Ti,d
Overhead for downstream at ONU i: 0.125
ms [3]
1
1
Fig. 6: Average Packet Delay with W max =10,000 bytes.
: The values are calculated based on the data provided in [4].
B. Validation of Analytical Model
A. Analytical Model Observation
The developed analytical model for the delay expression
is examined using the system parameters given in Table II.
In Fig. 5, it is clear that the average packet delay increases
as the traffic load increases and when the number of ONUs
increases; while the delay is almost the same regardless of the
traffic load variation when the traffic load is very low.
The second set of experiments aims to validate the developed analytical model on the maximum sleep time of each
ONU.
In these experiments, variable bit rate (VBR) and best effort
(BE) packets have a payload size that varies from 64 to 1518
bytes with the distribution based on [14] as follows: 64 bytes
(47%), 300 bytes (5%), 594 bytes (15%), 1300 byte(5%),
7
and 1518 bytes (28%). Fig. 7 and Fig. 8 verify the derived
maximum sleep time on different ingress traffic loads via
simulations. Fig. 7 examines the scenarios of different window
sizes (14,500 bytes, 15,000 bytes, and 15,500 bytes) with 16
ONUs in the system while Fig. 8 emphasizes on the impacts
of the different number of ONUs in the system as well as the
different transmission window size.
Firstly, we can see a close match between the simulation
and analytical results, which validates the proposed sleep time
analysis. Fig. 7 demonstrates that with increased ingress traffic
load for each ONU or the decreased window size, the ONU
sleep time gets shorter. On the other hand, Fig. 8 shows that
the maximum window size imposes noticeable effects on the
maximum allowed ONU sleep time, where a larger window
size yields longer sleep time. This meets our expectation
and understanding on batch mode transmission, in which a
large transmission window allows each ONU to have smaller
percentage of overhead due to switching between active and
sleep modes. Note that this figure also implies that there is a
minimum sleep time at a certain window size subject to the
upper bound of packets’ delay when the traffic load is very
high during the downstream traffic scheduling.
Fig. 7: Sleep time versus ingress load for each ONU.
Tactive includes the overhead time in the formula. In the
comparison we take Pactive as 3.85W and Psleep as 0.75W
during deep sleep [3].
Fig. 9 shows the energy consumption for different numbers
of ONUs under the maximum transmission window of 15, 500
bytes over a 10 minutes simulation run-time. Note that under
IPACT, each ONU can only sleep based on the DBA at
the OLT, and may be switched to active at random times
depending on the scheduled transmission slot. As observed,
the energy consumption of an ONU without sleeping is 2310
Joules while the energy consumptions of 8, 16, and 32
ONUs in the system are approximately 295, 235, and 207
Joules respectively, depending on the traffic load. The figure
also shows that the proposed downstream traffic scheduling
scheme can achieve approximately 90% reduction in energy
consumption compared with the scheme without sleep mode.
On the other hand, the JIT-based IPACT scheme can only
achieve a reduction up to 60%-70% as shown in the figure. All
these results clearly demonstrate the merits of our proposed
downstream scheduling protocol in being pushed into practical
EPON deployments.
VII. C ONCLUSIONS
C. Comparison with Counterparts
In this section, we compare the proposed scheduling algorithm with a legacy active DBA (i.e., no ONU sleep), and
the Just-in-time (JIT)-based IPACT [8], in terms of energy
consumption. Assuming that the downstream traffic load on
each ONU is sufficiently larger than that of the upstream, the
allowed sleep time duration of each ONU is indifferent of the
upstream traffic load under the UCS approach. The energy
consumption of an ONU can be calculated by the following
expression [6]:
EONU = Tactive × Pactive + Tsleep × Psleep
Fig. 8: Sleep time versus ingress load for each ONU.
(20)
where Pactive and Psleep denote the active power consumption
and the sleep power consumption respectively. Note that
This paper presents a novel downstream traffic scheduling
protocol for energy conservation in EPONs. The proposed
scheme is based on a new analytical model in which we
analyze the expected downstream packet delay and the ONU
sleep time under the Green Bandwidth Allocation (GBA)
framework. To the best of our knowledge, our model is the
first analytical study that investigates the maximum ONU
sleep time under downstream-based delay constraints, which
is expected to perfectly meet the practical EPON system
requirement. Simulation results highlight the advantages of the
proposed scheme and provide insights to the relation between
maximum transmission window size and maximum ONU sleep
time. Results also show that, unlike existing energy saving
schemes in EPON, our scheme is able to ensure almost 90%
8
Fig. 9: The energy consumption of the ONU in Joules (W max =
15, 500 bytes).
reduction in the ONU energy consumption, while maintaining
the QoS requirements for both the upstream and downstream
traffics.
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