1. A POWER MANAGEMENT ALGORITHM FOR THE WIRELESS
SERVICE
Edwin Hernandez, Arun Ayyagari
CISE department, PO BOX 116120 Microsoft Corporation
University of Florida One Microsoft Way
Gainesville, FL, 32611 Redmond, WA, 98052
(352) 392 1200 (425) 703 6185
ehernand@cise.ufl.edu aruna@microsoft.com
Abstract
Power management is an important subject for mobile computing devices. Power savings
in battery-operated equipment can be improved by scheduling the active duty cycles on
the network card depending upon the current network traffic statistics. We conducted
analytical experiments using exponential arrivals to simulate mobile host requests and
Pareto service rates for the network responses. The results showed that the buffer size
must be carefully chosen every time the management algorithm schedules a short duty
cycle being several orders of magnitude greater than deterministic and exponential
values expected. A tradeoff between the average power savings and the probability of
congestion has to be considered especially for heavily tailed network traffic patterns.
1. Introduction
Power management in network cards is a very important issue for battery-operated
equipment [1]. In general this type of equipment requires tight constraints in terms of
energy consumption. Ideally, a power management algorithm would allow the network
card, hard-drive, or any other device to power off, during idle periods of time; and power
it on during periods of utilization. Although, the many available methods for power
saving in portable computers are based upon timeout periods rather than wisely
determining idle periods of time. An average of 20% of the total power consumed by
many laptops is due to the wireless LAN interface therefore an intelligent method for
power management would improve the life in the battery. This paper proposes a new
power management algorithm targeting wireless network cards and how operating system
services, such as Microsoft Windows, would interact with the network card.
Many algorithms have been proposed to improve the power utilization in wireless
devices. Initially T. Simunic, et. al., [1] proposed a power management algorithm based
upon renewal processes, this algorithm optimizes a control policy by monitoring the
number of elements in the queue at the mobile host and the transition probabilities while
in the doze and off states. Additional steps should also be taken to improve power
utilization by defining a power control algorithm that monitors the signal strength value
at the access point (AP) and the mobile host, as well as the bit error rate (BER). In this
approach, the AP performs the calculations for the optimal transmission power to be used
by the client. Alternatively, in a distributed computing option, the AP could provide the
information for the power control algorithm to the mobile host to enable the mobile host

2. to independently determine the optimal transmission power. Their results indicated that
for web-browsing applications the savings could be of around 60% and telnet
applications resulted on savings of up to 80% on the power used by the card.
Additionally, Simunic showed that by using Pareto distributions and assuming a self-
similar network service times, a greater penalization value was observed in the range of
2-10 seconds.
On the other hand, the industry has also made efforts to improve asynchronous medium
access control protocols like the MAC in IEEE 802.11 [2] and make them more efficient.
Although, as presented by E. Takahashi [3], the one-fits all power management policy
found on the IEEE 802.11 standard still has many energy inefficiencies. Takahashi also
approached the issue of power consumption and delay introduced in IEEE 802.11
networks in order to propose a new MAC protocol. Takahashi’s protocol avoids the
unnecessary receiver idle times by approximation of the ideal fluid model and guarantee
communication services. The combination of a modified version of the Point
Coordination Function (PCF) and an explicit traffic re-shaper function was the key factor
for the improvements shown in this paper. The results presented indicate that it is
possible to reach savings between 50-90% within the higher and lower throughput
bounds used by the mobile unit.
Some other studies such as [4,5] try to predict the power consumption on the card by
using stochastic methods and neural networks. C. Hwong, et. al., implemented an event-
driven application which introduced two mechanisms for prediction: prediction-miss
correction and pre-wakeup. Both approaches required of an exponential predictor to
determine the upcoming idle period of time. Similar work has been also conducted on
prediction methods involving neural networks [5]. Even though both prediction
initiatives were used to determine the code-length and the Signal-to-Noise Ratio (SNR)
on a DS/CDMA system, the same experiments could be used to indirectly determine the
predicted power consumptions.
Finally, many researchers have proposed power management algorithms as part of the
solution of an optimization problem using Markov decision processes. L. Benini, et. al.,
[6] provided a novel approach to optimally find the policies that were the solution for a
well defined stochastic problem. Their findings indicate that higher queue lengths lead to
smaller power consumptions and that at higher throughput rates the power savings are
minimal. The difficulties encountered in real implementation applications of a stochastic
optimization are a weak supporter for this type of solution.
Contrary to predictive and stochastic optimization methods, our approach determines the
values of the idle time by reviewing historic information of the distribution function of
the number of elements in the queue, at the access point and client levels. Assuming that
there is a probability of congestion, the algorithm finds the optimal value of power while
minimizing the expected congestion.
This paper will, at first, introduce the wireless service in Windows XP and followed by a
brief description of the most popular wireless card characteristics, in Sections 2 and 3.

3. Section 4 provides the metrics and the modeling concepts used to determine the
simulation parameters. Section 5 presents the probabilistic power management algorithm
. While Section 6 outlines the implementation issues required in Windows at the Network
Device Interface Specification (NDIS) level. Finally we draw some conclusions and
present future work items in Section 7.
2. The Wireless service in Windows
The wireless service [6] provides the layer-2 functionality aimed at seamlessly
connecting to infrastructure and ad hoc networks. The service provides a polling
mechanism to detect new available networks every 60 seconds (Tscan). In other words, in
between those scan periods communication can take place depending upon the request
made by applications from the upper layers. Our main assumption is based upon the
ability to reduce power cycle during those periods of time in between scan periods where
the network utilization is probabilistically “low”. The wireless service is in charge of the
card configuration, establishing ad hoc and infrastructure network connections and
minimizes the user intervention in the process of wireless configuration, authentication,
and security.
3. Power states on wireless cards
As mentioned earlier, the wireless service takes care of the process of setting on and off
states of the network card. In order to understand the tradeoff of setting the wireless card
from “awake” state or maximum power consumption towards a “doze” state or “off”
state, several statistical analysis have been made in WaveLAN cards [1]
Figure 1. shows a sketch of the transition functions and how much time is invested
between different state transitions. In general “doze” to “off” transitions are expected to
take between 30 to 90 ms, while “off” to “doze” 10 to 50ms [1]. While, the time between
state transitions from “doze” to “ON” and vice-versa is less than 10 ms and can be
considered as negligible [2].
During power savings mode the card will be set from “on” to “doze” state and from
“doze” to “off” state. The minimum delay introduced by switching the card into doze
mode is 100 ms.
While doze mode represents power savings of more than 90%, testing conducted at
different service rates or network throughputs indicates that 300 kbps the network card
behaves as no power management policies are being in place. In addition, when the card
is in doze mode, there is a high-probability to observe packet delay is at least of a 100 ms.
As a consequence many applications will nor behave properly under these conditions.
[3].

4. Max current
Current
doze
time
{
{
∆Τ1 ∆ Τ2
Figure 1. Doze to max power transition function for a wireless card
4. Communication model and power optimization
Once we understand the behavior of the network cards and as depicted in Figure 2., the
ideal case scenario, data transmissions occur exactly during the ON cycles of the card
being powered. Only for the period of time where the number of packets received or
transmitted by the card is very small or zero, the card could be placed into doze mode or
turned off to minimize power consumption.
Max current
Current
doze
{
{
time
Ton Tidle
Figure 2. Matching service of data requests to the power cycles for a wireless card
For the wireless service in Windows, the transitions from “off” to the “on” state occur
less frequently and the power management algorithm should adaptively select the
appropriate duty cycle times and behave somewhat similar to the duty cycle profile
depicted in Figure 2.
The state diagram shown in Figure 3 is a simplified version of the wireless service using
the power management feature. By default, the wireless card is set to be in the “on” state
and it switches into scan mode every 60 seconds. The shaded state depicts a probabilistic
mechanism where the network traffic statistics determines the appropriate time to place

5. the card in “doze” or “off” states, depending on the characteristics of the network and
different security and authentication issues such as IEEE 802.1X.
Tidle=0 Stochastic decider
Activity
Tscan ON Tidle
If (Tidle==up)
DOZE
WZC scan
If (Tidle==up)
No 802.1X auth
OFF
Figure 3. State machine for the power manager for WZC
In order to study the probabilistic behavior of the card at different rates and arrivals we
will assume that the arrivals or request made by the user to the card reflect an exponential
or Poisson process with λ as the arrival rate. Meanwhile, prior research conducted in
network traffic characterization has determined that service time is self-similar and
therefore network traffic is fractal [7,8,9,10]. One of the main implications of such
findings is the infinite variance for the distribution probabilitysee Eq.1 and Eq.2
F ( x) = P[ X ≤ x] = 1 − (α / x) β , α , β ≥ 0, x ≥ α (1)
f ( x ) = βα β x − β −1 (2)
The average power is defined by the function of RMS current and voltage, although we
used the values found in [3] for power “doze” and “on” states. In fact, we can calculate
theoretical power values for the different cards available in the market as shown in Table
1.
Pave = I rms × Vrms (3)

6. N
1
I rms =
N ∑I
i =1
i
2
Vrms = Vo (4)
Table 1. Current values consumed by the PCMCIA wireless card during different modes of
operation.
Card Mode Average Current
Cisco Aironet Transmission 450 mA
Reception 250 mA
Power Saving 15 mA
LucentWaveLAN Transmission 285 mA
Reception 185 mA
Power Savings 9 mA
The average value for a service time following Pareto distribution as shown in Eq. 5,
represents the discrete time average, whereas the continuous average is presented in Eq.
6. This value provides the expected value for the service time. Eq. 6 shows that the Pareto
distribution is only applicable for values of si greater than α in other words the minimum
service time used for the simulation. Since Tscan>>α we could approximate Eq. 6 to the
integral to infinite and reduce the number of calculations.
N N
s ave = ∑ s i p ( s i ) = ∑ si βα s i
β − β −1
(5)
i =1 i =1
∞ αβ
s ave = ∫ βα β s − β −1ds = (6)
α β −1
The algorithm optimizes the average power consumption on the card by determining the
probability of the service time to equal an expected value of save , used to determine the
number of average packets arriving at the access point during “off” mode and as the
maximum number of packets arriving during an interval in “doze” mode.
q(i ) = 1 / s aveToff |doze (7)
This queue size can be set to a maximum value to buffer the data before it could be sent
to the network card during the duty cycle of the card (Eq. 7). The main drawback of this
approach relies on the associated delay affecting real-time applications where delay can
be an inconvenient. This delay will be exactly equals to the idle value of time calculated
by the power management algorithm.
Now in terms of power, we would expect that:
• Poff in “off” or “doze” state , if q (i ) = ε , where, ε represents a very small queue
size
• Poni in transmitting or receiving mode if ε < q( i ) ≤ Qmax

7. In order to determine the average power using Eq. 8 and Eq. 9, we can use different
network traffic models and determine the probability of n=0 and the complement of that
probability determines the time to be in the ON state. The analysis could also be done
with real traffic data and by traversing the sequence of inputs by keeping counters of
different bins of ni and calculating Pr(ni) in linear time. This analysis can be performed
wit the the driver or the Windows Management Instrumentation (WMI). The Windows
Operaing system uses the WMI and its structures to store network management
information, hence the counters of ni can be implemented onto the WMI seamlessly [11]
Tdoze T − Tdoze
Pave = Pdoze + Pon scan (8)
T scan Tscan
Pave = Pdoze Pr( n = 0) + Pon Pr( n > 0) = Pdoze (1 − Pr( n > 0)) + Pon Pr( n > 0 )) (9)
Using Pareto for the services and exponential distribution for the arrivals, we can
determine the values for Eq. 9. Table 2. shows the models of arrival and service during
the “on” and “off” states as shown in Figure 4. For a Pr(n>0), we need to consider the
probability of one or more packets arriving at the access point (or base station) or the
packets generated by the user at the mobile host at any time between 0 to Tscan . In
addition, one needs to add the probability of packets being serviced longer than the duty
cycle Ton but before Tscan.
Exp Exp
Arrival Arrival
Power (W)
Pr(n) Pr(n)
Pareto : Pareto :
Pr(n) Pr(n)
T on Tscan
Time (s)
Figure 4. Pr(n) during the duty cycles
The wireless channel will have a Pr[arrivals within 0 to Ton ] which is an exponential
distribution. From Ton to Tscan the probability function provided by Pr[service time Ton to
Toff] represents the elements queued at the access point and at the network card.

8. Table 2. Model used at each interval of time
Time Model
0 < T < Ton Exponential arrival at both wireless channel
and mobile unit buffer
Pareto Service at both wireless channel and
mobile unit buffer
Ton < T ≤ Tscan No arrivals at the wireless channel
But exponential arrival at the mobile unit
buffer
No arrivals at the wireless channel (only if
doze mode is used, Tb = 100 ms).
Elements serviced and buffered at the access
point follow Pareto distribution.
Therefore at the wireless channel with a duty cycle less than Tscan:
Pr( n > 0) = Pr(0 < Tarrival < Ton ) Pr(0 < s n ≤ Ton ) (10)
= (1 − e − λTON ) * (1 − (α / Ton ) β ) (11)
1 1
0.8 0.8
Pr [n=0]
Pr [n=0]
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 10 20 30 40 50 60 0 10 20 30 40 50 60
Time (s) Time (s)
(a) α=0.1 sec,β=1.8, λ=0.1 p/sec (a) α=1,β=1.8, λ=1 p/sec
1 1
0.8 0.8
Pr [n=0]
Pr [n=0]
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 10 20 30 40 50 60 0 10 20 30 40 50 60
Time (s) Time (s)
(a) α=1 sec,β=1.8, λ=10 p/sec (a) α=10,β=1.8, λ=1000 p/sec
Figure 5. Probability of n=0 at different values of λ, TON and α
The first term is exponential, while the second one is a Pareto distribution. The
complement of Eq. 11 is depicted on Figure 5 to indicate the probability of the number of
arrivals due to a host’s request or for a service still taking place initiated by the access
point.

9. In terms of queues, we will use Qmax to represent the maximum number of packets at the
systems queue. The Queue of the system as a whole will be seen as both queues
represented by Q, the size of this queue will depend on the values used by the Eq. 12 and
13.
−1
N mobile = k p + λTs − s aveTon (12)
N ap = s −1 Ts − λTon
ave (13)
−1
Q = N ap + N mobile = λToff − s ave Toff + k p (14)
Now in order to minimize Eq. 14 we will find the value(s) for Tidle = Ts-Ton which
minimizes the power consumption under an average inter-arrival rate registered in the
previous sample measured by the card.
Power (W)
1.6
1.4
1.2
1
Power (W)
0.8
0.6
0.4
0.2
0
0 10 20 30 40 50 60 70
Time (Sec)
Figure 6. Average power and duty cycle set by the algorithm (Eq. 8)
In order to measure the performance of the algorithm and determine the validity of the
assumptions made, we used a mathematical model to measure the probability of
congestion as well as determine the appropriate value, depending upon the values of λ
and µ. The literature indicates that the M/G/∞, closely represents the system under study.
The process of arrival is modeled as Poisson, whereas the service is modeled as Pareto
with a rate λ. The average service time for the Pareto distribution is βα ( β − 1) ,
for β =1 [8, 9].
The count process {X t }t =0,1, 2,... represents the number of elements in the queues in the
system at time t. In fact, it can be proved that {Xt} has a Poisson marginal distribution
with mean λβα ( β − 1) . Therefore this value can be used in Eq. 12 to determine the
average number of elements in the buffers at the access point and mobile unit.
Figure 7 shows the queue size of a self-similar model and the values expected if a
deterministic model were used, as expected the estimation made by the D/D/1 model

10. would lead to severe congestion.
7000 7000
6000 6000
5000 5000
Queue Size
Queue Size
4000 4000
3000 3000
2000 2000
1000 1000
0 0
0 10 20 30 40 50 60 70 0 10 20 30 40 50 60
Time (Sec) Time (S)
(a) D/D/1 λ=100, µ=1000 (b) Self-similar λ=100, µ=1000
30000 30000
25000 25000
20000 20000
Queue Size
Queue Size
15000 15000
10000 10000
5000 5000
0 0
0 10 20 30 40 50 60 70 0 10 20 30 40 50 60
Time (Sec) Time (S)
(c) D/D/1 λ=400, µ=1000 (d) Self-similar λ=400, µ=1000
Figure 7. Expected queue size using deterministic and self-similar models
at different rates of service and arrival vs. Ton.
The associated delay for a case shown in Fig 7.c, when t=58 s is zero, although the power
consumption in this scenario cannot be improved since more than 90% of the duty cycle
is required to obtain a probability of congestion equals to zero. Although feasible, the
range if we set a buffer size of 5000 bytes will yield an average delay of 10 seconds with
the duty cycle of 75%.
5. Power management algorithm.
Under the assumption that Pareto distributions are scale-invariant [9], in that the
probability that the wait is at leas 2x seconds is a fixed fraction of the probability that the
wait is at least x, for any value of x=α, we can extrapolate the results presented in Fig. 7
to any time-scale and bandwidth since they will reflect a very similar behavior.
Now the idea, in the real implementation tone should maintain a vector of inter-arrival
rates and the probability of those such that:
 λ1   µ1   n1 
     
 λ2  µ   n2 
λ=  µ= 2  n= 
Μ Μ Μ
     
λ  µ  n 
 N  N  N

11. Both matrices are size 1xN and represent the distribution of the inter-arrival rates
measured form the card as well as the number of elements in the queue. Henceforth,
λi < λi+1 , µ i < µ i+1 and n i < ni+1 , for all values of i. The vector n should define nN-1 =
Qmax while, nN measures the number of elements in the queue whose value is greater than
Qmax.
As detailed earlier, WMI will be in charge of updating this elements by executing
network card queries at certain intervals of time.
The vectors λ and n also provide the depended vectors for Pr(λ) , Pr(µ), and Pr(n) and
compute the different parameters for the power management algorithm. Hence, the
process of finding the appropriate value of Tidle can be found by using:
0. Initialize(WMI, “Exponential”, “Pareto”)
1. Determine(µave, λave, nave)
2. Toff = 1/λave
3. ε = 1/λave2
4. Ton = 1/µave
5. if Toff+Ton ≥ Tscan then
6. find Toff | Pr[n=(Toff + ε)λave) is minimum otherwise Toff = 0
7. else
8. Toff = Tscan - Ton
9. Wait_Timer(Update statistics, Tj)
10. return Toff
By determining the average service times, arrival, and number of elements in the system
both received and transmitted, we can estimate Toff depending upon the chosen values for
service and inter-arrival times. This value will be used as a starting point to further
determine the proper value Toff which provides the minimum power consumption while
minimizing the congestion probability, especially when the Ton and Tpff values
determined are greater than the scan period. Once this case is found, using the Pr(n) table
it is easy to determine if congestion can occur if the value previously measured by the
statistics vector is above a threshold. This threshold value depends upon the Qmax value
and the associated average delay supported by the application.
Moreover, we can add that the associated average delay supported by the application in
place to the algorithm and within a range of values determine if Tpff has to be zero. One
of the main assumptions in this algorithm is the ability of WMI to update the statistic
counters appropriately.
6. Implementation
The algorithm presented in section 5. is application dependent, and the values for Qmax
and the threshold for congestion, as well as expected maximum packet delay depends
upon the constraints of the upper layer sources. Some real-time applications might not be

12. suitable for a power management algorithm; in fact the buffering and delay introduced by
the wireless service might disrupt and diminish user experience in those scenarios. Power
management should focus on scenarios pertaining non-real time applications, such as e-
mail, web-browsing, chatting, and asynchronous downloads.
In addition, the proposed algorithm will require the definition of data structures to capture
the different values for the required parameters during collection of network statistics
phase.
typedef struct _NDIS_WLAN_PowerElements
{
ULONG NumberOfItems; // in list below, at least 1
ULONG NQueue[i];
ULONG Arrival[i]
ULONG Service[i]
ULONG TimeGranularity;
} NDIS_802_11_STATISTICS, *PNDIS_802_11_STATISTICS;
The data structure presented above holds the counters corresponding to the number of
packets in different bins registered by the wireless card. The structure could be accessed
by the wireless service at least in a network scan basis and the wireless service must also
update and monitor the structure.
Many other issues are required to be studied, for instance if only IEEE 802.11
open/shared authentication method is used with IEEE 802.1X port based access control
protocol, the power savings could be increased by switching off the card completely. We
have to take into account that during the transition from doze to off state there is an
associated delay, but this drawback might not be as important as saving battery life.
Home networking scenarios might not require IEEE 802.1X authentication turning the
card “on” and “off” will not add any inconveniences. Although the use of IEEE 802.1X
might require the reduction in the period of time the card can be switched off by
transitioning between the doze-off states as part of the “power savings” mode, depicted in
Figure 3.
7. Conclusions and future work
The efficient use of the duty cycle of power on wireless cards has a potential benefit of
great power savings. The power savings are tightly coupled to the associated delay and
probability of congestion on the network. We were able to demonstrate that by using a
probabilistic model with self-similar network traffic, the queue size and associated
latency can be under-estimated using simpler traffic models.
Although it is feasible to provide a power management algorithm based upon the
statistical information of network traffic, the feasibility of applying those policies
depends greatly on the upper-layer application. Many isochronous applications could be

13. negatively affected by the process of scheduling the duty cycle of the network card,
although many other applications such as email and web-browsing could efficiently
provide enough statistical information to reduce the duty cycle, thereby, save power and
with a properly sized buffer, also decrease the probability of congestion at the access
point and the mobile host.
Detecting the network traffic type, whether it follows a deterministic, exponential, or
Pareto distributions is an important factor to improve the power management strategy.
Further studies are required to optimize the algorithm presented in Section 5 and refine
the specification and implementation details to define the structures presented in Section
6.
We conclude that we can save as much power as we want by controlling the duty cycle,
but this must be driven by the network traffic statistics. Fuzzy-logic controllers and
neural networks could be able to provide more adaptive approaches that may have a
greater potential for improvement.
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