  The recent emergence of the small cloud (SC), both in concept and in
practice, has been driven mainly by issues related to service cost and
complexity of commercial cloud providers (e.g., Amazon) employing massive data
centers. However, the resource inelasticity problem faced by the SCs due to
their relatively scarce resources (e.g., virtual machines) might lead to a
potential degradation of customer QoS and loss of revenue. A proposed solution
to this problem recommends the sharing of resources between competing SCs to
alleviate the resource inelasticity issues that might arise [1]. Based on this
idea, a recent effort ([2]) proposed SC-Share, a performance-driven static
market model for competitive small cloud environments that results in an
efficient market equilibrium jointly optimizing customer QoS satisfaction and
SC revenue generation. However, an important non-obvious question still remains
to be answered, without which SC sharing markets may not be guaranteed to
sustain in the long-run - is it still possible to achieve a stable market
efficient state when the supply of SC resources is dynamic in nature and there
is a variation of customer demand over time? In this paper, we address the
problem of efficient market design for SC resource sharing in dynamic
environments. We answer our previous question in the affirmative through the
use of Arrow and Hurwicz's disequilibrium process [3], [4] in economics, and
the gradient play technique in game theory that allows us to iteratively
converge upon efficient and stable market equilibria
