Cooperative and Parallel Fog Discovery and Pareto Optimal Fog Commerce Bargaining

While
fog resource discovery
involves propagating resource requests to a huge number of fog nodes,
fog commerce
refers to the activity of buying and selling fog resources. This work devises: 1) the
KM-gossip algorithm
for bolstering
fog discovery
and 2) a
bargaining mechanism
for
pricing
fog resources. The
KM-gossip algorithm
is a
generalization
of the
gossip algorithm
. It uses
$K$

broker agents
(
BAs
) to
cooperatively
“gossip” requests among themselves, and
in parallel
, each
BA
relays the requests to
$M$
fog nodes. While
computational complexity analysis
validates that the
KM-gossip algorithm
has
logarithmic time complexity
(i.e., it is
computationally efficient
), empirical results show that it significantly outperforms the existing
gossip
and
flooding algorithms
. Supplementing and complementing previous empirical results,
game-theoretic analysis
in this work validates that the
bargaining mechanism
generates
Pareto optimal
(
economically efficient
) solutions. The solutions also satisfy the famous
Nash’s axioms
that prescribe
highly desirable properties
of bargaining solutions.