Information Technology Reference
In-Depth Information
return
{(
You can try by using solution 1, or,
You can try by using solution 2, or,
You can try by using solution 3, or,
You can try by using solution 4, or,
You can try by using solution 5, or,
You can try by using solution 6 !
“
”
);
}
Compute(Resp
ðw
h
Þ
k
;
1
);
Compute(Resp
ðw
h
Þ
k
;
2
);
Compute(Resp
ðw
h
Þ
k
;
3
);
Compute(Resp
ðw
h
Þ
k
4
);
Compute(Resp
ðw
h
Þ
k
;
5
;
);
Compute(Resp
ðw
h
Þ
k
;
6
);
Generate(Resp
ðw
h
;
opt
Þ
k
)
end
Complexity
This algorithm assumes that sporadic tasks span no more than one hyperperiod of
the periodic tasks hp
a
ðw
h
Þ
k
2
LCM
¼½
0
;
þ
max
k
ð
Þ
, where LCM is the well-known
;
1
a
ðw
h
Þ
k
;
1
Least Common Multiple of all task periods and
ð
Þ
is the earliest activation time
s
ðw
h
k
. The extension of the proposed algorithm should be straightfor-
ward, when this assumption does not hold and its running time is O(n + m)
Gharsellaoui et al. (
2012
). The EDF-based schedulability in the case of a mixture of
periodic (synchronous and asynchronous tasks) and sporadic tasks, i.e. each task
has an offset Si
i
, such that the jobs are released at k*Ti
i
+ S
i
ð
of each task
is strongly coNP-
hard Buttazzo and Stankovic (
1993
). This complexity was decreased in our
approach to ef
k
2@Þ
cient O(n + m)
2
guarantee algorithm. This optimal algorithm results
in the dynamic scheduling solutions. These solutions are presented by a proposed
intelligent agent-based architecture where a software agent is used to evaluate the
response time, to calculate the processor utilization factor and also to verify the
satisfaction of real-time deadlines. On the other hand, the busy period, which is
computed for every analyzed task set and has a pseudo-polynomial complexity for
T
S
, is decreased also by the optimization of the response time. The most important
results are presented in our work. So, we can deduce that using our proposed
approach under such conditions may be advantageous.
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