Author:
Frohlich Piotr,Gelenbe Erol,Nowak Mateusz P.
Abstract
<p>We present a smart Service Manager whose role is</p>
<p>to direct user requests (such as those coming from IoT devices)</p>
<p>at the edge towards appropriate servers where the services they</p>
<p>request can be satisfied, when services can be housed at different</p>
<p>Fog locations, and the system is subject to variations in workload.</p>
<p>The approach we propose is based on using an SDN controller as</p>
<p>a decision element, and to incorporate measurement data based</p>
<p>machine learning that uses Reinforcement Learning to make the</p>
<p>best choices. The system we have developed is illustrated with</p>
<p>experimental results on a test-bed in the presence of time-varying</p>
<p>loads at the servers. The experiments confirm the ability of the</p>
<p>system to adapt to significant changes in system load so as to</p>
<p>preserve the QoS perceived by end users.</p>
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Cited by
8 articles.
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