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Table 3: Average Price per accepted Sequence.
# of input Seq. Single Cloud Multi-Cloud
50 0.0150$ 0.0070$
500 0.0149$ 0.0058$
5000 0.0149$ 0.0128$
Table 4: Total Costs for 250 Input Sequences.
Single Cloud Multi-Cloud
Pattern A 2.62$ 2.62$
Pattern B 4.82$ 0.30$
of the costs of the Amazon S3 Cloud. But the total
costs for the 5000 sequence experiment shows that the
multi-Cloud constellation can generate more costs,
which is in the interest of the user, because in this
case more sequences are processed, compared to the
single Cloud experiment. However, the multi-Cloud
setup provides lower prices per accepted sequence in
each experiment, as seen in Table 3.
4.2 Effect of the Used Sequence Type
In order to study the impact of the chosen sequence
type, we conducted four experiments each of them
with a different sequence type and on a single Cloud
or a multi-Cloud environment. As we submitted 250
sequences, no sequence was rejected in any experi-
ment. Table 4 shows that the multi-Cloud variant gen-
erates less or the same costs as the single Cloud vari-
ant. The costs for single and multi-Cloud for the se-
quences of type A is the same, whereas the difference
for sequences of type B is 4.52$ (saving of factor 16).
This can be explained by the fact, that the sequences
of type A have tighter restriction on the SLA: The
minimal accepted value for the maximum object size
depends on the largest object of the sequence, which
can be between 1 and 100GB. Thus, the majority of A
sequences can not be scheduled on the private Cloud,
which is deciding for the costs for B sequences
5 CONCLUSIONS
We extended the popular Cloud simulation framework
CloudSim to enable the simulation of STaaS Clouds.
The concurrentuse of resources is modeled accurately
in order to gain realistic simulation results. Detailed
models for storage disks, servers and storage con-
troller are provided and can be easily extended. Mon-
itoring allows users to investigate a broad spectrum of
metrics from each simulated STaaS Cloud as well as
from the user’s perspective.
Different access patterns can be modeled and used
to generate sequences of requests. These sequences
can be stored and read from file to provide random,
but repeatable experiments. In addition, different
SLAs can be modeled, such as certain capabilities of
a Cloud or some restrictions. A Meta Broker enables
simulations of multiple Clouds, where different us-
age sequences can be dispatched on the best-matching
Clouds. Compared to single Cloud usage users can
save huge costs if the SLAs of the request sequences
are not very strict.
In a future work, we will investigate more realistic
pricing models (no linear price functions) and more
complex SLA mechanisms. The framework will be
extended to enable the simulation of the storage allo-
cation policies and mechanisms used in current STaaS
Clouds, for example mechanisms to prevent bit rot-
ting of failover reactions to outages of hardware. We
will also introduce more heuristic parameters to make
the simulation more realistic. Also brokers could not
only compare requirements against the static SLA but
could also calculate metrics based on previous band-
width usage and then decide which Cloud to use for
further requests.
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