Temporal workload analysis and its application to power aware scheduling

International Journal of Embedded Systems and Applications (IJESA) Vol.3, No.3, September 2013

TEMPORAL WORKLOAD ANALYSIS AND ITS
APPLICATION TO POWER-AWARE SCHEDULING
Ye-In Seol1, Jeong-Uk Kim1 and Young-Kuk Kim2,
1

2

Green Energy Institute, Sangmyung University, Seoul, South Korea
Dept. of Computer Sci. & Eng., Chungnam Nat‟l University, Daejeon, South Korea

ABSTRACT
Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic
voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and
reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal
workload of a system which specifies how much busy its CPU is to complete the tasks at current time.
Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline
first scheduling and an effective method to identify and distribute slacks generated by early completed
tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70%
over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be
devised using the proposed algorithm.

KEYWORDS
Power-aware Scheduling, Real-time Scheduling, Embedded Systems

1. INTRODUCTION
Energy consumption issues are becoming more important for mobile or battery-operated
embedded systems. Since the energy consumption of CMOS circuits, used in various
microprocessors, has a quadratic dependency on the operating voltage(
)[2], it is a very
useful method for reducing energy consumption to lower the operating voltage of circuits. But,
lowering the operating voltage also decreases its clock speed or frequency, so the execution times
of tasks are prolonged. This makes problem more complex for hard real-time embedded systems
where timing constraints of tasks should be met.
There has been significant research effort on Dynamic Voltage Scaling(DVS) for real-time
systems to reduce energy consumption while satisfying the timing constraints[1,4-6,8-11,13]. The
chance to lower its voltage occurs when there are slacks for the current executing real-time task.
Generally there are two sources of slack, i.e., when the sum of worst case execution times of tasks
is below the CPU‟s processing capacity and when a task completes early without consuming its
worst case execution time. Main concern of DVS algorithms is how to identify those slacks and
how to distribute them.
DVS algorithms also depend on the scheduling policy, task model, and processor architecture. In
this paper, we adopt Early-Deadline First(EDF) scheduling policy, periodic or sporadic task
model and uniprocessor system. EDF assigns dynamic priority for ready tasks and it is known as
optimal for uniprocessor system[7]. Periodic task model assumes tasks are released periodically
and their relative deadlines are the same as their respective periods. Sporadic task model allows
DOI : 10.5121/ijesa.2013.3301

1


tasks are released randomly, but there is a restriction on the minimum inter-arrival time of the
same task. In those task models, we require a priori knowledge of tasks, i.e., period, worst-case
execution time, or minimum inter-arrival time, etc. Some works don‟t assume these kinds of
information[5], or adopt aperiodic task model[11,12]. But, many hard real-time applications are
classified as periodic or sporadic, so considering periodic and/or sporadic task model is practical.
In this paper, we introduce a notion of temporal workload which reflects how much busy the
system is. It will be showed that analyzing temporal workload provides a useful method for realtime scheduling, especially EDF. We analyze the behaviors of EDF scheduling using temporal
workload, and present some interesting results by which we understand more deeply the features
of EDF scheduling.
Also we apply the analysis results into power-aware scheduling, and present an algorithm which
adopts the results of CC-EDF[9] and temporal workload analysis. The simulation results show
that the proposed algorithm with affordable algorithmic complexity reduces more energy
consumption than previous work.
The rest of the paper is organized as follows. In section 2, we present the system model and
notations adopted in this paper and introduce some previous works which motivate the work done
in this paper. In section 3, we define the temporal workload and analyze EDF scheduling using it.
In section 4, we present a power-aware scheduling algorithm derived from the temporal workload
analysis. In section 5, simulation results will be provided and section 6 will conclude and discuss
the future directions of this paper.

2. MOTIVATION
In this section we present the system model and introduce the result of the related work.

2.1. System Model
We consider preemptive hard real-time system in which all tasks are periodic or sporadic and
mutually independent. The target processor is DVS enabled uniprocessor and its supply voltage
and frequency are varied continuously between [vmin, vmax] and [fmin, fmax], respectively. Let
*
+ be a set of periodic or sporadic tasks. Each task is represented as
(
) where












is period for periodic task or minimum inter-arrival time for sporadic task;
is work-case computation time for task Ti at the maximum frequency;
Di is relative deadline of a task Ti.
If a instance or job of task released at , then its absolute deadline( ) is
. We
(
). Also the
will consider tasks only with
, so task could be represented as
following notations will be used.
or
: the worst-case utilization of task at the maximum frequency, i.e.,
⁄ .
∑ .
or : the worst-case total utilization of all tasks in the system, i.e.,
: task‟s actual computation time which should be less than . For uncompleted
tasks,
.
: actual utilization of task, i.e.,
.
∑
: actual total utilization of system, i.e.,
: task‟s remaining computation time.
: jth instance or job of task .
2







: current frequency ratio, i.e., fcur/fmax
: task‟s execution time, i.e.,
: task‟s remaining execution time, i.e.,
release time of task

.

2.2. Related Work
EDF scheduling has been extensively investigated in the area of real-time and power-aware
scheduling[1,3-5,7-12]. But, some dynamic natures of EDF were not fully exploited, for example
dynamic density function introduced in [6]. Dynamic density of a job is defined as its remaining
execution time divided by the time to deadline. They deal with the case of unit execution time and
multi-processor system using dynamic density function. Temporal workload introduced in this
paper is similar or identical to dynamic density function, so we can say that we extended their
results into more general task model, but uniprocessor system.
While devising a new power-aware scheduling algorithm based on the temporal workload
analysis, we especially considered the results presented by Pillai and Shin[9]. They introduced a
cycle-conserving method to real-time DVS. This method reduces the operating frequency on each
task completion and increases on each task release. When a task completes its current invocation
after using
computation time, they treat the task as if its worst-case execution time were
.
So processor speed could be set as the actual total utilization
which is always less than or
equals to worst-case total utilization .
Mei et al.[8] integrated the above cycle-conserving method and the result of Qadi et al.[10] for
sporadic task set. But, these method doesn‟t fully utilize the slack generated. Let‟s see the
following figure. If a task completed at , then during the time interval ,
- the system
operated at higher frequency than required. This observation provides a clue to more slow down
the processor when a task completes. We will show later that the amount of slack which could be
used for lowering processor frequency is related with temporal workload of the completed task.

(

)

Figure 1. CC-EDF[9] or CC-DVSST[8] Schedule

3. TEMPORAL WORKLOAD ANALYSIS
In this section, we introduce some definitions and analyze behaviors of EDF scheduling. This
section will provide a concrete theoretical basis for the power-aware scheduling algorithm
presented in the next section.

3.1. Temporal Workload
Definition 1: Temporal workload of a task at time ,
( ) or
if not confusing, is defined
as
(
) or 0 if it is completed before or at . Temporal workload of a system at time t,
( ) or
, is the sum of temporal workloads of all tasks in the system at time t.
Temporal workload of a task is similar or identical to dynamic density of a job[6], but we
introduce new notion of temporal workload to distinguish remaining execution time and

3


remaining computation time, so more applicable to power-aware scheduling. Following notations
are also used throughout this paper.
or
( ) : temporal workload of task set
or
( ) : temporal workload of a task in task set
)(
( (
)) :
(
) when executes during ,
), and executes
during ,
).
For the time being, R
because we schedule tasks with full speed of CPU, i.e.,
.
Lemma 1:
is monotonically decreasing during a time interval ,
) if no task was released
()
during that interval,
, and we schedule with EDF.
was executed during ,

Proof: Let task
()

(

,

()

(

)

.

)(

)

∑

=

(

(
)(

)
)

)(

(

)

()

By assumption,

)

.

/
(

(

), then by definition of temporal workload,

∑

0
(

.

1

)(

/

)

∑(

))

(1)

, and for we schedule with EDF,

is always less than

or equals to 1, so Equation (1) is always larger than or equals to 0 which implies
monotonically decreasing.

is

Now we consider which task we schedule at time t effects on temporal workload of a system.
))
Lemma 2:
( (
( (
))
.
)) and
Proof:
( (
( (
)) are the temporal workloads of a system at time
when we schedule and respectively at time t. So,
( (

))

. (

)/

( (

))

. (

(

)/

) (2)

By assumption, Eq. 2 is less than 0 which implies Lemma 2 is true.
Lemma 2 provides another clue that EDF scheduling policy is optimal in preemptive uniprocessor
scheduling.
( (

Corollary 1:

))

( (

))

.

Corollary 1 states that temporal workload of a system doesn‟t depend on which task execute at
time t when tasks‟ deadlines are the same.
Lemma 3:

(

(

)(

))

(

(

)(

))

.

4


)(
Proof:
( (
)) is the temporal workload of a system when we schedule at
,
) and at ,
) and
( (
)(
)) is the temporal workload
of a system when we schedule at ,
) and at ,
) , but at time
(
), has the same
because
is the same for the two cases, and also. So,
Lemma 3 holds.
Lemma 3 implies that the order of execution has no effect on the last temporal workload of a
system if there is no deadline miss. Which task executed and how much it executed during a time
interval concern only the calculation of the last temporal workload of a system.
At Lemma 1, we proved the monotonic decreasing property of temporal workload under EDF
scheduling. More investigation shows that when a task‟s deadline expires, then the temporal
workload of a system decreases as least as the temporal workload of the task if there is no task
release during the time interval. Following Lemma proves the above discussion.
(
)
()
Lemma 4:
and we schedule using EDF.

( ) if there is no task release during (

),

()

Proof: Let‟s consider a ideal CPU that executes every tasks concurrently proportional to their
initial temporal workload. This could be accomplished by minimizing δ as small as possible in
Fig. 2.

Figure 2. Ideal CPU execution

a
b

Figure 3. Real CPU execution

The execution of ideal CPU could be relocated as the right side of Fig. 2. Because only the
amount of execution time contributes to last temporal workload by Lemma 3, temporal workload
of both side of Fig. 2 is the same. Also, the execution of real CPU could be relocated as the right
side of Fig. 3. At the right side of Fig. 3, the areas of „a‟ and „b‟ equal for total computation time
could not be changed. By Lemma 2, temporal workload of the real CPU is less than or equals to
that of the ideal CPU. The temporal workloads of tasks in ideal CPU don‟t change during the time
interval ,
) and the temporal workload of the task whose deadline is
sets to zero at
. So,
(

()

) of the real CPU
()

(

) of the ideal CPU
(3)

5


In the process of proving Lemma 4, we presented a useful high limit of temporal workload under
EDF scheduling. It is that temporal workload of ideal CPU system provides an intuitive high limit
as Eq. (3) states.

3.2. Temporal Workload Isomorphic
Now we introduce new notion to compare temporal workloads of different systems.
Definition 2: Task systems,
and , are temporal-workload-isomorphic if
()
( ) for all time t.

such that

If temporal workload value of system is always the same or constant multiples of that of system
, then we can easily assume that schedulability conditions of two systems are very close or the
same and schedule behaviors are very similar to each other.
Lemma 5: Following two periodic task systems,
worst-case execution scenarios.
*
*
such that

(
(

and

are temporal-workload-isomorphic under

), where * + could be multiset. EDF scheduling}
∑
) . * + is distinct set of * +, i.e.,
if
. EDF scheduling} .

.

is for all

of A

Proof: Let’s consider in and its cousin tasks * + in , i.e.,
for . Then release times
and deadlines of all above tasks are the same. Under EDF scheduling, deadline of task is the only
criterion of scheduling, then we can make the following condition hold that if we schedule a job
of , then a job of some corresponding ‟s is scheduled. If not, then there could be one or more
jobs whose deadline is coincided with . But, despite the existence of another job of the same
deadline and a different schedule could be done at that moment,
by Lemma 3 and
Corollary 1. Also, the last time at which all jobs of the same deadline are completed is the same
for and . So, Lemma 5 holds.
Now, we consider the schedulability condition of task system using temporal workload.
Following Lemma is directly implied from the definition of temporal workload because temporal
workload identifies the amount of work to be done until deadline of each task.
Lemma 6: If two finite task system
and
are temporal-workload-isomorphic, then
schedulability conditions of two systems are identical.
Proof: We prove this Lemma by contradiction. If task system
and not, then there exists a task of such that

has violated its timing constraints

.
But temporal workload of system could not reach infinite because it has finite tasks and each
task of system has finite value of temporal workload. This contradicts with assumption of this
Lemma.

3.3. Upper Bound of Temporal Workload
Definition 3: Task system is temporal-workload-upper-bounded if
will denote the upper bound .

()

for all time t.

6


Because temporal workload of a system denotes the ratio of overhead to CPU capacity, we may
schedule all the tasks of the system without violating its timing constraints if
is below or
the same as 1. Following theorem shows above discussion is true.
()
Theorem 1: If
for all time t, i.e.,
, then task set is schedulable using
EDF.
Proof: We will prove it by induction on time t. Let‟s assume that this theorem holds until time t,
i.e., we successfully scheduled the tasks until time t. At time t, we can still schedule the highest
priority task, i.e., the shortest deadline task, without violating its deadline if there is no task
()
release during (
) for
( ). If there is a task release at time
where
, then we can insist that this theorem still hold until time
because there is no
deadline during (
). From the assumption, at
, the temporal workload of task set
should be less or equals to 1. So, we proved that this theorem still holds until
or
which are larger than t if it holds at t.
Theorem 1 states that if we preserve the upper bound of temporal workload below or equal to 1,
then it is always schedulable. Also theorem 1 provides a sufficient condition for the
schedulability of a system. Now we investigate the temporal workload upper bounds of periodic
task systems.
). EDF+,
Theorem 2: For a periodic or sporadic task system
* (
s∑
.
Proof: We already said that ideal CPU with EDF scheduling policy provides an upper bound of
temporal workload. For a periodic or sporadic task system of ideal CPU, clearly its temporal
workload is always less than or equals to its total utilization. So theorem 2 holds.

3.3. Temporal Workload at Lower Processing Speed
Now, we consider the case of
, i.e., CPU‟s processing speed is not always 1.
Lemma 7: Following two periodic task systems, and are temporal-workload-isomorphic under
worst-case execution scenarios.
*
(
)∑
, CPU‟s processing speed is (
), EDF},
*
(
)
, CPU‟s processing speed is 1, EDF}.
()
Proof: Clearly at time t = 0,
( ) by definition 1. And when we execute a task
of system , at time t, following holds.
(

)

()

(

)

()

.

()

()

(

)

(4)

Eq. (4) states that if we execute tasks of the same deadline at the same time for system and ,
then the ratio of temporal workloads of two systems are not changed. Because and use EDF
scheduling policy and execution times(not computation times) and deadlines of
and its
corresponding are identical under worst-case execution scenarios, Lemma 7 holds. There could
be the cases when we execute of
and of , i.e., deadlines of two tasks are coincidently
exact, but by Lemma 3 and Corollary 1, Lemma 7 still holds as we insisted at Lemma 5.
Corollary 2: For a periodic task system
with constant CPU speed .

*

(

)∑

+, then we can schedule

7


Proof: By Lemma 7, we can construct a temporal-workload-isomorphic system of whose
is 1 and its CPU processing speed is 1. Then by theorem 1, the newly constructed task set
could be successfully scheduled with EDF. By Lemma 6, this shows that original task set could
be successfully scheduled.
Using Lemma 7 and Corollary 2, we can safely lower processing speed for periodic real-time task
system when its total utilization is below 1 as many previous works for power-aware scheduling
said.
Following theorem is the repetition of Theorem 4 of DVSST paper[10]. DVSST algorithm scales
up CPU whenever new task releases and scales down whenever its deadline expires exactly as
much as utilization of that task.
Theorem 3: Sporadic task system
EDF if and only if ∑
.

with ∑

and DVSST algorithm, it is schedulable using

Proof: “Only If” part: As the assertion stated in [10], if
, then EDF will not find a feasible
schedule, therefore DVSST combined with EDF will not find a feasible schedule.
“If” part: As we stated at Lemma 4, ideal CPU can provide an upper bound of temporal
workload. So, whenever a new task released, temporal workload of ideal CPU system increased
- and whenever a deadline of a task expires it
as much as utilization of that task during ,
decreased also as much as that amount. Now suppose that there is neither new task release nor
deadline expiration during ,
- and is the temporal workload of ideal CPU during the time
interval, then we can construct a new task system whose total utilization is 1 and each task has
the same deadline of original corresponding task and its computation time is
.
Then by Theorem 1, we can schedule new task system using EDF and by Lemma 6, we can say
that we can schedule original task system with EDF for two task systems are temporal workload
isomorphic during the interval. And for two task systems have identical deadline distributions, we
always make sure that (
) pair of tasks execute at the same time in each system without
violating EDF policy. So by Lemma 7, above process could be repeated at every time interval
during which neither new task releases nor deadline expires. This proves „if‟ part.
Following theorem 4 states that temporal workload analysis provides another useful
schedulability test.
Theorem 4: Let
= {periodic or sporadic task systems whose total utilizations are less than or
equal to 1},
= {task systems whose
are less than or equal to 1}, and
= {task
systems whose loading factors[5](
((∑
) (
))) are less than or equal to
1}, then
.
Proof: By Theorem 2,3 and Corollary 2, clearly
neither minimum interval nor periodicity of tasks, so

. But
.

doesn’t assume

is the maximum set of tasks which could be scheduled by EDF because loading factor test
provides necessary and sufficient condition[5]. So,
. But, for the following task
*
+,
(
)
set
, but
. So,
Theorem 4 holds.

8


The temporal workload of the task set in Theorem 4 is infinite when N is infinite, so there is no
upper limited value of
which provides necessary condition for schedulability test.

4. POWER-AWARE SCHEDULING ALGORITHM
4.1. An On-Line Algorithm
In section 3, we analyzed scheduling behaviours of EDF using temporal workload. Now we apply
the results into power-aware scheduling. As we stated at section 2, previous algorithms such as
CC-EDF, DVSST and CC-DVSST don‟t fully utilize the slacks generated by early completed
tasks. But, based on the temporal workload analysis, we can find more slacks to slow down the
CPU speed. Before presenting more discussion, let‟s introduce new definition.
( ) of a task

Definition 4: Temporal idleness

at time t is defined as following.

0 until its completion and after its deadline.
(
) if it was completed at time and
()
if
.

.

The real value of depends on the status of system and how to calculate will be presented later.
Likewise the definition of temporal workload of a system, temporal idleness of a system, ( ) or
, is defined as the sum of ( ). Note that ( ) is the same as
( ) of uncompleted case.
Now, consider the amount of computation time until its completion. At CC-EDF and CC-DVSST,
they lower the processing speed of task when it is completed. This means that its pace of
computation is faster than actual execution needed as stated at section 2. Following figure shows
the situation.

c

f

a

d

g

b

e

h

Figure 4. Computation time comparison

At time , task was completed, so during ,
-, its computation time(=
) is
larger than needed (
) as amount of (
). If we assume that task executed with its
( ) (
temporal workload during , - , then
) equals to
(
) and
( ) (
) equals to
(
) . But,
(
), (
)
(
). So,
, i.e.,
. It is that the
)
(
)
amount of slack could be used until its deadline is (
, i.e.,
(
). Following lemma shows it formally.
Lemma 8: At CC-EDF or CC-DVSST scheduling, the amount of computation time exceeding its
)- (
actual pace until its completion time(
) is the same as , ( ) (
).
Proof: ,
0

( )

(

)1

(

(
)

)
(

)

(

)
9


,

-

But, by definition,
(5)

, (
(

)
)

(5)

, so,
(

)-

(

,

-

(

)

)

Using Lemma 8, if a task completed at time t, then we can slow down processing speed by
amount of
when executing lower priority tasks than until its deadline, because CC-EDF or
CC-DVSST slows down processing speed by the amount of (
) . The proposed
algorithm tries to use slacks of already completed higher priority tasks which are necessarily
generated by assuming worst-case execution scenarios and follows the result of CC-EDF when
running task‟s priority is higher than those of already completed tasks. Also, if we cannot utilize
those slacks by some reasons, i.e., when executing higher priority tasks or when slacks are too
much to fully utilize, then we evenly distribute those unused slacks until corresponding deadlines.
One more consideration occurs when there is idle period. Let‟s consider periodic task set
( )
( )
( )+ . If
*
(
)
and
complete at t=1 and t=2,
( ) =1.5/4.5=3/7. If we lower the
respectively, and
completes early at t=2.5, then
processing speed as much as
( ), then actual processing capacity during t=[3,6) is (13/7)*3=12/7 which is less than sum of WCET of
and
. Deadline miss occurs because
there is idle period during t=[2.5,3). During the idle period, the total processing capacity which
should be processed under the actual execution scenarios is larger than sum of slack used. So, we
should reduce future slacks to compensate larger processing capacity. Following figure shows it.
idle period
0

0

1

1

2 2.5 3
a

2 2.5 3

deadline miss
4.75
b

6.5
c

4.5

6

Figure 5. Slack reduction example

The amount of slack which should be reduced is
, and is the same as ,
(
(
))-. At the above figure, the area „a‟ is the same as „b‟+‟c‟. And if exceeds , we
could save some slacks because only
(length of idle period) should be processed by CPU.

10

global variables
last_idle_time
last_cpu_speed
last_calculation_time
ex_ratio, ex_flag, ex_task
∑
// current total utilization, i.e., ∑
// completed task set sorted by deadline
compute_cpu_speed( ) // is the highest priority task or NULL if no ready task
cpu_speed =
for all tasks ti at
if
or is NULL
if ( cpu_speed
(
(
)))
(
))
cpu_speed
(
else
(
)) cpu_speed
ex_ratio =(
ex_flag = 1, ex_task =
cpu_speed = 0
break
(
))
else if ( ex_flag
0 && (
)
(
))
ex_ratio = (
ex_flag = 1, ex_task =
break
last_calculation_time =
return cpu_speed
decrease_temporal_idleness()
idle_time = last_idle_time - tcur
idle_work = idle_time * last_cpu_speed
for all tasks ti at TC
if ( idle_work == 0)
break
(
)) (
(
)
if (
idle_work)
idle_work
(
) (
)
break
else
idle_work = idle_work
(
)
increase_temporal_idleness()
for all tasks whose priority is less than or equals to that of ex_task at
if ( ex_flag
1)
busy_ratio = ex_ratio
ex_flag = 0
else
(
))
busy_ratio = (
busy_time = last_calculation_time
busy_work = busy_time busy_ratio
if (
)
(busy_work)/(
)
Figure 6. Temporal idleness management
11

when task arrived
if ( exceed_flag)
else if ( cpu was idle)
last_cpu_speed = compute_cpu_speed(
set cpu speed as last_cpu_speed
when task completed
=
if ( exceed_flag)
insert into
if there is no task to execute
last_idle_time =
set cpu as idle
else

)

)

on deadline of task
if ( exceed_flag)
else if ( cpu was idle)
delete from
if there is no task to execute
last_idle_time =
set cpu as idle
else
cf.

)

: current time,
: current ready task of highest priority or NULL if no ready task.
Figure 7. Power-aware scheduling algorithm

Now, following theorem proves the correctness of our algorithm.
Theorem 5: The algorithm presented at Fig. 3, schedules every periodic or sporadic task set if and
only if ∑
.
Proof: “Only If” part: If
, then EDF will not find a feasible schedule, therefore our
algorithm will not find a feasible schedule because our algorithm is the same as EDF or DVSST
when tasks execute always at worst-case execution scenarios, i.e., ( )
and
for
t and i.
„If” part: We will show that temporal workload of a system adopting our algorithm is always less
than or equals to that of another ideal system which schedules tasks without violating their timing
constraints. Then by lemma 6 and theorem 1, we can insist that our scheduling algorithm also
satisfy real-time constraints.

12


Let‟s consider ideal CPU of ideal system which can anticipate task‟s actual computation time
when released and can execute active tasks with concurrently with the speed of CU. This ideal
system of figure 8 resembles the system of figure 2.

Figure 8. CC-EDF schedule : ideal CPU and ideal execution

Our real system starts to execute with TU speed and slows down with (
) when some
(
)- (
)
tasks are completed. And we already said that ,
is exactly the
) (
)
same as (
. Therefore we can apply following operation to scheduling
result of our real system which replaces some areas of by the same areas of . Following
figures shows it.
(

)

Figure 9. Slack exchange process

During the time interval ,
- which has no idle period, we can apply the above operation for all
areas of . Then temporal workload of the last applied result is less than or equals to that of ideal
system because the total computing capacity of the former is larger than or equals to that of the
latter and the computing capacity of the former is always exhausted by tasks which have shorter
or the same deadlines of tasks of the latter.
During idle period, there exist some areas of ideal system which cannot find counterparts of real
system. Those areas could be filled up or substituted with the areas of future slacks of already
completed tasks as was illustrated at figure 5 and above exchange operation could be also applied
for them. The temporal workload of substitution and exchange result is also less than or equals to
that of ideal system for the same reason.
Now temporal workload of our real system is always less than or equals to that of substitution and
exchange result because the total computing capacity of the former is always larger than or the
same as that of the latter and the same tasks are executed. This proves this theorem.

4.2. An Illustrative Example
Let‟s consider an illustrative example, a periodic task system
*(
)( )( )(
)+.
Total utilization of task system is 1 and
is also 1. Suppose that actual computation
times of tasks , and are 1/2, 1/2, and 1/3 respectively. Then following figure shows the
result of our scheduling algorithm.
At time t=1/2,
(
))

completes its execution and we can set processing speed as
=1 - 0.5/1.5=2/3.

(

13


At time t=1/2+3/4=5/4,
completes its execution, the processing speed should be
=1-1/3-0.5/(7/4)=8/21. During [5/4,2], computation time of
is 8/21*3/4=2/7. Deadline of
is 2 and
is released at that time, so
=1-2/7=5/7.

0

1

2

3

4

5

6

7

Figure 10. Scheduling example

At t=2+7/10,
completes its execution and
=1-0.5/(13/10)-2/7=30/91.
During [2+7/10,3],
will executes with speed of 30/91 and its computation time is
30/91*3/10=9/91, so total computation time of
until released is 2/7+9/91=245/637.
At t=3,
(

releases, and its deadline is less than
. So,
(
))
=1-5/13=8/13.

At time t=3+13/16,

will be completed and

will execute with speed of

will execute with speed of

≒0.387, and it will complete at t≒3.823. Then, during [3.823,4], there is no ready task, so slack
reduction process should be done.
At t=4,
will be released and it will be executed with speed of ≒0.722 until t≒4.693. At t≒
4.693, there is no ready task, so slack reduction process should be done at t=6 when
and
are released. At t=6,

will be executed with speed of ≒0.889, and so on.

5. EXPERIMENTAL RESULTS
We evaluated our proposed algorithm using RTSIM[14] which is a real-time simulator. RTSIM
can simulate the behaviors of dynamic voltage scaling algorithms as well as traditional real-time
scheduling algorithms. In this simulation, it is assumed that a constant amount of energy is
required for each cycle of operation at a given voltage. This quantum is scaled by the square of
the operating voltage, consistent with energy dissipation in CMOS circuits(
)[2,5]. Only
the energy consumed by CPU was computed and any other source of energy consumption was
ignored. Also we do not consider preemption overheads, task switch overheads, and operating
frequency change overheads. It is also assumed that the CPU consumes no energy during idle
period and its operating frequency range is continuous at [fmin=0, fmax=1].
We compared our proposed algorithm with CC-EDF[9], DVSST[10], and CC-DVSST[8]. CCEDF assumes periodic task model, so we compared it at periodic task system. DVSST and CCDVSST assume sporadic task model, so we compared them at sporadic task system.
To evaluate the effect of number of tasks in the system, we generated 10 or 20 tasks for each
comparison. Their periods or minimum inter arrival times are chosen randomly in the interval [11000]ms. We divided task set into three groups to reflect more real environments. One group of
tasks have short period in the interval [1-10]ms, another group of tasks have medium period in the
interval [10-100]ms, and the last group of tasks have long period in the interval [100-1000]ms.

14


1

0.8
0.6
0.4
0.2

non-DVS
CC-EDF
PAS-TW

0

Energy(normalized)

1.2

1

Energy(normalized)

1.2

0.8
0.6
0.4
0.2
0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

10 tasks

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

load ratio

10 tasks
1.2

1

1

0.8

load ratio

0.8

0.6
0.4
0.2

non-DVS
CC-EDF
PAS-TW

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

20 tasks

load ratio

Energy(normalized)

1.2

Energy(normalized)

non-DVS
DVSST
CC-DVSST
PAS-TW

non-DVS
DVSST
CC-DVSST
PAS-TW

0.6
0.4
0.2
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

20 tasks

load ratio

Figure 11. Simulation results : for periodic tasks(left) and for sporadic tasks(right)

The simulation was performed also by varying the load ratio of tasks, i.e., the ratio of the actual
computation time to the worst case computation time. For all simulations, the worst-case total
utilization of system is always 1, i.e.,
. Above figures show the simulation results.
Figure 11(left) shows the result for periodic task system. In this case, our proposed
algorithm(PWA-TW) always outperforms CC-EDF, and the ratio of energy saving is up to 10%.
The effect of number of tasks in the system could be neglected on the simulation result as the
figures show. Also the number of CPU frequency changes was almost the same for the two
algorithms, so more energy saving could be acquired at real environments.
Figure 11(right) shows the result for sporadic task system. In this case, our proposed algorithm
also outperforms both DVSST and CC-DVSST. For DVSST, the ratio of energy saving is up to
70% and for CC-DVSST, up to 10 %. The effect of number of tasks in the system could be also
neglected, but the number of CPU frequency change of our algorithm was larger than that of
DVSST and almost the same as that of CC-DVSST. So, the ratio of energy saving to DVSST
could be shrinked. But our algorithm has huge performance gain to DVSST, so in spite of
frequency change overheads, it is expected that our algorithm still outperforms to DVSST at real
environments.

15


6. CONCLUSION
In this paper, we analyzed the behaviors of EDF scheduling and presented a power-aware
scheduling algorithm for periodic and sporadic tasks. Temporal workload analysis provides
another sufficient condition for schedulability of preemptive real-time task scheduling and
another formal method to prove the correctness of power-aware scheduling algorithms. The
proposed algorithm also adopts the results of cycle conserving method(CC-EDF) and sporadic
task scheduling(DVSST). The simulation results show that the proposed algorithm outperforms
existing algorithms up to 10-70 % with respect to CPU energy saving.
In the future we would like to improve the proposed algorithm. This could be done if we assign
all slacks generated by early completed higher priority tasks into the task of highest priority
among uncompleted ready tasks instead of evenly distributing them until the ends of deadlines.
This method may lower processor frequency much more than the proposed algorithm. Also we
would like to apply the temporal workload analysis into another area of real-time task scheduling,
for example, aperiodic task acceptance problem. If we could maintain the temporal workload of a
system below or equal to 1, then real-time constraints of all tasks in the system are met. So this
may provide an effective method for aperiodic task scheduling.

ACKNOWLEDGEMENTS
This work was supported by the Industrial Strategic Technology Development Program(10041740, Development of a software that provides customized real-time optimal control
monitoring services by integrating equipments in buildings with web service) funded by the
Ministry of Trade, Industry and Energy(MOTIE, Korea).

REFERENCES
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Twenty-Eighth Hawaii Int‟l Conf. on System Sciences, Vol. 1, 1995.
[3] H. Chetto and M. Chetto. “Some Results of the Earliest Deadline Scheduling Algorithm,” IEEE
Transactions on Software Engineering, Vol.15(10),pp.1261–1269, 1989.
[4] W. Kim, J. Kim, and S. L. Min, “A Dynamic Voltage Scaling Algorithm for Dynamic-Priority Hard
Real-Time Systems Using Slack Time Analysis,” In Proc. of Design, Automation and Test in Europe,
pp. 788–794, 2002.
[5] C. H. Lee and K. G. Shin, “On-line dynamic voltage scaling for hard real-time systems using the EDF
algorithm,” In Proc. of IEEE Int‟l Real-Time Systems Symposium, pp. 319-327, 2004.
[6] J. Lee, A. Easwaran, I. Shin, and I. Lee, “Multiprocessor real-time scheduling considering
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[1]

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[13] F. Yao, A. Demers, and S. Shenker, “A scheduling model for reduced CPU energy,” In Proc. of the
IEEE Foundations of Computer Science, pp.374–382, 1995.
[14] RTSIM:Real-time system simulator, http://rtsim.sssup.it.

AUTHORS
Ye-In Seol received his B.S. degree in Nuclear Engineering from Seoul National
University, Korea in 1992, M.S. degree in Computer Science and Statistics from Seoul
National University in 1994. He is a chief researcher in Green Energy Institute of
SangMyung University in Seoul. His research interests include embedded system,
real-time system and building automation system.
Jeong-Uk Kim received his B.S. degree in Control and Instrumentation Engineering
from Seoul National University, Korea in 1987, M.S. and Ph.D. degrees in Electrical
Engineering from Korea Advanced Institute of Science and Technology in 1989, and
1993, respectively. He is a professor in SangMyung University in Seoul. His research
interests include smart grid demand response, building automation system, and
renewable energy.
Young-Kuk Kim received the B.S. and M.S. degrees in Computer Science and
Statistics from Seoul National University, Korea in 1985 and 1987 respectively and the
Ph.D. degree in Computer Science from University of Virginia, Charlottesville,
Virginia in 1995. After his Ph.D., he visited VTT Information Technology, Finland
and SINTEF Telecom & Informatics, Norway as an ERCIM research fellow during
1995-1996. He joined the Chungnam National University as a faculty member of the
Computer Science Department in March 1996. From August 2002 to July 2003, he
visited Computer Science Department at University of California, Davis as an
exchange scholar. His research interests include real-time systems, database systems,
multimedia and mobile information systems

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