2 research outputs found
Implementation and evaluation of semantic clustering techniques for Fog nodes
Growing at an extremely rapid rate, the Internet of Things (IoT) devices
are becoming a crucial part of our everyday lives. They are
embedded in almost everything we do on a daily basis. From simple
sensors, cell phones, wearable devices to smart city technologies,
we are becoming heavily dependent on such devices. At this current
state, the Cloud paradigm is being
ooded by massive amounts
of data continuously. The current amounts of data is minimal compared
to the amounts that we are about to witness in the near future,
mainly because of the 5G deployment expediting and the increase in
network intelligence. This increased data could lead to more network
congestion and higher latency, due to the physical distance between
the devices and the Cloud data centers. Therefore, a need for a new
model is paramount, and will be essential in realizing the Internet
of Everything (IoE) and the next stage in the digital evolution. Fog
computing is one of the promising paradigms, since it extends the
Cloud with intelligent computing units, placed closer to where the
data is being generated to o oad the Cloud. This tackles the issues
of latency, mobility and network congestion. In this work we present
a conceptual Fog computing ecosystem, where we model the Cloud
to Fog (C2F) environment. Then we implement two dynamic clustering
techniques of Fog nodes to utilize combined resources, using
a semantic description of the Fog nodes' resources and properties of
the edge devices. Finally, we optimize the assignment of applications
over Fog cluster resources, using Linear programming and a First Fit
Heuristic Algorithm. We evaluate our implementation by analyzing
the di erences between the two clustering techniques.
We perform several experiments to evaluate our implementation, and
the results prove that the heuristic optimization of task allocation is
much faster and more consistent than the Linear programming solver,
as expected. Moreover, the results show that clustering Fog nodes is
bene cial in o oading the Cloud and reducing response times
Implementation and evaluation of semantic clustering techniques for Fog nodes
Growing at an extremely rapid rate, the Internet of Things (IoT) devices
are becoming a crucial part of our everyday lives. They are
embedded in almost everything we do on a daily basis. From simple
sensors, cell phones, wearable devices to smart city technologies,
we are becoming heavily dependent on such devices. At this current
state, the Cloud paradigm is being
ooded by massive amounts
of data continuously. The current amounts of data is minimal compared
to the amounts that we are about to witness in the near future,
mainly because of the 5G deployment expediting and the increase in
network intelligence. This increased data could lead to more network
congestion and higher latency, due to the physical distance between
the devices and the Cloud data centers. Therefore, a need for a new
model is paramount, and will be essential in realizing the Internet
of Everything (IoE) and the next stage in the digital evolution. Fog
computing is one of the promising paradigms, since it extends the
Cloud with intelligent computing units, placed closer to where the
data is being generated to o oad the Cloud. This tackles the issues
of latency, mobility and network congestion. In this work we present
a conceptual Fog computing ecosystem, where we model the Cloud
to Fog (C2F) environment. Then we implement two dynamic clustering
techniques of Fog nodes to utilize combined resources, using
a semantic description of the Fog nodes' resources and properties of
the edge devices. Finally, we optimize the assignment of applications
over Fog cluster resources, using Linear programming and a First Fit
Heuristic Algorithm. We evaluate our implementation by analyzing
the di erences between the two clustering techniques.
We perform several experiments to evaluate our implementation, and
the results prove that the heuristic optimization of task allocation is
much faster and more consistent than the Linear programming solver,
as expected. Moreover, the results show that clustering Fog nodes is
bene cial in o oading the Cloud and reducing response times