  Edge computing is an emerging solution to support the future Internet of
Things (IoT) applications that are delay-sensitive, processing-intensive or
that require closer intelligence. Machine intelligence and data-driven
approaches are envisioned to build future Edge-IoT systems that satisfy IoT
devices' demands for edge resources. However, significant challenges and
technical barriers exist which complicate the resource management for such
Edge-IoT systems. IoT devices running various applications can demonstrate a
wide range of behaviors in the devices' resource demand that are extremely
difficult to manage. In addition, the management of multidimensional resources
fairly and efficiently by the edge in such a setting is a challenging task. In
this paper, we develop a novel data-driven resource management framework named
BEHAVE that intelligently and fairly allocates edge resources to heterogeneous
IoT devices with consideration of their behavior of resource demand (BRD).
BEHAVE aims to holistically address the management technical barriers by: 1)
building an efficient scheme for modeling and assessment of the BRD of IoT
devices based on their resource requests and resource usage; 2) expanding a new
Rational, Fair, and Truthful Resource Allocation (RFTA) model that binds the
devices' BRD and resource allocation to achieve fair allocation and encourage
truthfulness in resource demand; and 3) developing an enhanced deep
reinforcement learning (EDRL) scheme to achieve the RFTA goals. The evaluation
results demonstrate BEHAVE's capability to analyze the IoT devices' BRD and
adjust its resource management policy accordingly.
