Abstract:
An Internet-based distributed manufacturing
system usually consists of a network/grid of
autonomous centers that collaborate together. Each
center may receive world-wide customer orders
and collaborates with other friendly manufacturers,
sub-contractors, agents, and suppliers to maximize
the sales and quality of the goods. The centers
together strike a consistent balance in terms of
manufacturing speed, quality, material costs, and
workload. The queue maintained by the front-end
server/coordinator in each center usually contains
requests from different internal and external
sources. The merged traffic from these sources can
inundate the queue buffer and cause overflow
easily in high loading situations. As a result this
leads to request retransmissions, unreliable
collaborations, and unhappy customers. One way
to eliminate buffer overflow in non-persistent
(transient) situations is to tune the buffer size on
the fly to ensure that it always cover the queue
length. The Recurrent NNC (neural network
controller) or R-NNC proposed in this paper can
achieve efficacious dynamic buffer size tuning at
the user level for autonomous centers in an
Internet-based distributed manufacturing system.
Keywords: Intemet-based distributed
manufacturing system, dynamic buffer size tuning,
Recurrent NNC, backpropagation, IEPM (Internet
End-to-End Performance Measurement)
