Self managed cloud computing and edge computing system using deep reinforcement learning
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Pulchowk Campus
Abstract
In order to tackle the latency problems in sensitive applications implemented in
IoT devices, the edge computing came into existence. On top of that, the idea of
a mobile edge computing (MEC) network, in which such computationally heavy
activities are computed in many edge servers deployed adjacent to mobile devices,
has recently gained popularity. Unlike cloud server, the edge device has a nite
computation capacity and it can't handle massive computation tasks. There needs
to be a smart task o oading scheme in order to decide whether to execute the
task in the local device itself, or o oad it to the edge device and process there or
again send it further to the cloud server for processing. Also the algorithm should
be able to utilize the available network bandwidth e ciently. The optimization of
joint o oading decision and bandwidth allocation in a multi user, multi task, multi
server environment can be formulated as a mixed integer non linear programming
problem (MINLP) in MEC to preserve energy and maintain quality of service
for wireless devices. MINLP is a NP-hard problem and the time complexity to
solve it grows exponentially. In this research work, the power of deep learning
and reinforcement learning has been applied to solve this MINLP problem under
a fraction of a second which makes it suitable for real world usage. Further an
end to end integrated edge and cloud computing system has been proposed that
switches from one to another whenever required, and leverages the bene ts of both
paradigm.
Description
In order to tackle the latency problems in sensitive applications implemented in
IoT devices, the edge computing came into existence.
Citation
MASTER OF SCIENCE IN COMPUTER SYSTEM AND KNOWLEDGE ENGINEERING