![](bg2.png)
Figure 1: The architecture of the DISSECT-CF simulator .
mance and increase the problem complexity, therefore
innovative solutions are needed to deal with multiple
and complex aims. Our proposed simulation environ-
ment aims at providing a way for investigating certain
policies to achieve these goals.
Concerning simulations, the CloudSim toolkit has
been widely used to propose and evaluate certain heu-
ristics for datacenter consolidation, such as in (Abdul-
lah et al., 2017) and (Kertesz et al., 2016). Though
these solutions provide load balance improvements,
they do not take into account and do not apply provi-
der pricing.
3 OUR PROPOSED COST MODEL
FOR CLOUD DATACENTRE
MANAGEMENT
DISSECT-CF is a compact open source (DISSECT-
CF, 2017) simulator focusing on the internals of IaaS
systems. Figure 1 presents its architecture including
our extensions (denoted with grey colour). There are
six subsystems (encircled with dashed lines) imple-
mented, each responsible for a particular functiona-
lity: (i) event system – the primary time reference;
(ii) unified resource sharing – models low-level re-
source bottlenecks; (iii) energy modelling – for the
analysis of energy-usage patterns of resources (e.g.,
NICs, CPUs) or their aggregations; (iv) infrastructure
simulation – for physical/virtual machines, sensors
and networking; (v) cost modelling – for managing
IoT and cloud provider pricing schemes, and (vi) in-
frastructure management – provides a cloud like API,
cloud level scheduling, and IoT system monitoring
and management.
In a recent work (Markus et al., 2017), we intro-
duced the following new components to model IoT
Cloud systems: Sensor, IoT Metering and IoT Con-
troller. Sensors are essential parts of IoT systems,
and usually they are passive entities (actuators could
change their surrounding environment though). Their
performance is limited by their network gateway’s
(i.e., the device which polls for the measurements and
sends them away) connectivity and maximum update
frequency. Our network gateway model builds on
DISSECT-CF’s already existing Network Node mo-
del, which allows changes in connection quality as
well. In our model, the Sensor component is used
to define the sensor type, properties and connections
to a cloud system. IoT Metering is used to define and
characterize messages coming from sensors, and the
IoT Controller is used for sensor creation and mana-
gement.
To incorporate cost management, we enabled de-
fining and applying provider pricing schemes both for
IoT and cloud part of the simulated environments.
These schemes are managed by the IoT and Cloud
Pricing components of the Cost modeling subsystem
of DISSECT-CF, as shown in Figure 1.
3.1 Configurable Cost Models based on
Real Provider Schemes
In order to enable realistic datacentre consolidation
simulations, we considered four of the most popu-
lar, commercial cloud providers, namely: Amazon,
MS Azure, IBM Bluemix and Oracle. Most providers
have a simple pricing method for VM management
(beside thaditional virtual machines, some provide
containers, compute services or application instances
for similar purposes). The pricing scheme of these
providers can be found on their websites. We conside-
red the Azure’s application service (Azure, 2017), the
Bluemix’s runtime pricing sheet under the Runtimes
section (IBM, 2017), the Amazon EC2 On-Demand
prices (Amazon, 2017), and the Oracle’s compute ser-
vice (Oracle, 2017) together with the Metered Servi-
ces pricing calculator (Oracle Calculator, 2017). The
cloud-related cost is based on either instance prices
(Azure and Oracle), hourly prices (Amazon) or the
mix of the two (Bluemix) provider uses both type of
price calculating. For example, Oracle charges depen-
ding on the daily uptime of our application as well as
the number of CPU cores used by our VMs.
Figure 2 shows the XML structure and the cost va-
lues for the applied categories we designed to be used
in the simulator. This configuration file contains some
providers (for example the amazon element starting in
the second line), and the defined values are based on
the gathered information from the providers’ public
websites discussed before. We specified 3 different
sizes for applicable VMs (named small, medium and
large).
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