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ment a Task Placement Segment (TPS). However, the
problem with the TPSs is that when there are many
TPSs and they are paired very differently, it will take
more time to reconfigurate the cloud and thus the per-
formance is reduced.
The robustness in our case is an indicator for mea-
suring the changes within a sequence of TPSs. If the
difference between subsequent pairs of TPSs is high,
the robustness will be low. To measure the devia-
tion of a TPS from its predecessor, we determine the
number n of needed manipulation operations to trans-
form one cloud configuration into another, resulting
in q =
1
n+1
∗ 100. The robustness value qo of the en-
tire sequence aggregates the particular deviation val-
ues, for instance by a weighted sum with a logarith-
mic factor. The performance of the workflow execu-
tion depends on the expected execution time r
i
of the
tasks, the speed up sp
j
of the VMs, the reconfigura-
tion time w f t
k
of the workflow level operators and the
reconfiguration time cct
k
of the resource level opera-
tors of the current TPS t p
k
. Each TPS includes one
task placement t p
k
. The performance is described as:
p =
∑
n
k=1
max
i∈t p
k
(sp
i
∗
∑
j∈vmt
i
r
j
)+ w f t
k
+cct
k
. The
value of the expected execution time is not just the
sum of all expected executions within a TPC, because
VMs execute tasks parallel so we search for the max-
imum makespan of the VMs which is max
i∈t p
k
(sp
i
∗
∑
j∈vmt
i
r
j
). In addition, the speed up factor might con-
sider the type of the task. Some tasks could be more
memory or network intensive than others which leads
to a different speed up for every task depending on
the task himself. As a solution, the speed up could be
determined for a set of reference task. However, this
would require additional effort in classifying work-
flow tasks.
The overall costs are the sum of the costs of
the PMs cpm and the costs of the VMs cvm: oc =
cpm + cvm. cvm =
∑
n
k=1
∑
i∈t p
k
c
i
∗
∑
j∈vmt
i
r
j
.
∑
n
k=1
is the sum over all TPS,
∑
i∈t p
k
is the sum over all
VMs and c
i
∗
∑
j∈vmt
i
r
j
is the sum over all products
of the expected execution r
j
and the costs c
i
of VM
i. The costs for the PMs are similar, in short the costs
of the PM pmc multiplied with the highest run time
of the VM assigned to the PM. So the formula is:
cpm =
∑
n
k=1
(max
i∈t p
k
(
∑
j∈vmt
i
r
j
)∗pmc). We are plan-
ning to measure these values to assess the target con-
figuration that is suggested by the retrieval results.
7 SUMMARY AND FUTURE
WORK
The task placement problem for workflows in a cloud
environment requires an intelligent solution due to its
complexity. We think the problem is more difficult
than the VM placement problem because it increases
the VM placement problem by an additional layer.
In this paper, we present our approach that uses
case-based reasoning to find good task placements
without a recalculation of the configuration on ev-
ery single step of the workflow. We belief reasoning
techniques are feasible and useful for task placement.
Case-based reasoning is a valid method to develop a
solution.
The work is still in an early phase of development.
It provides a representation and a case-based solution
for the task placement problem. In a next step, we
will finish the implementation of the prototype and
conduct an experimental evaluation. Furthermore, we
will deploy a more dynamic approach to determine
the changes in speed up of a virtual machine when
the manipulation operators change the resources of
the VM. Another issue of our future work is to deter-
mine the granularity in which the task placement seg-
ment should be chosen. To achieve a solution for this,
we will implement a configurable prototype in order
to conduct further experiments with different setups.
The set of values to be measured might be adjusted
after first experimental results have been achieved.
We expect the following benefits of the approach.
The deep integration of workflow management and
cloud management creates novel business opportuni-
ties for cloud providers in the area of cloud-based
workflow services. Furthermore, the preference on
cases with a robust configuration will hopefully re-
duce the re-configuration costs. Additionally, the
number of SLA violations is considered by the ap-
proach and, thus, will most probably be reduced. We
expect a significantly better performance of the work-
flow execution service in comparison to simply mi-
grating a traditional workflow management system
into a cloud infrastructure as a whole. The novel ma-
nipulation operators at workflow level facilitate both
scalability at the task and at the data level.
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