  This paper studies fog computing systems, in which cloud data centers can be
supplemented by a large number of fog nodes deployed in a wide geographical
area. Each node relies on harvested energy from the surrounding environment to
provide computational services to local users. We propose the concept of
dynamic network slicing in which a regional orchestrator coordinates workload
distribution among local fog nodes, providing partitions/slices of energy and
computational resources to support a specific type of service with certain
quality-of-service (QoS) guarantees. The resources allocated to each slice can
be dynamically adjusted according to service demands and energy availability. A
stochastic overlapping coalition-formation game is developed to investigate
distributed cooperation and joint network slicing between fog nodes under
randomly fluctuating energy harvesting and workload arrival processes. We
observe that the overall processing capacity of the fog computing network can
be improved by allowing fog nodes to maintain a belief function about the
unknown state and the private information of other nodes. An algorithm based on
a belief-state partially observable Markov decision process (B-POMDP) is
proposed to achieve the optimal resource slicing structure among all fog nodes.
We describe how to implement our proposed dynamic network slicing within the
3GPP network sharing architecture, and evaluate the performance of our proposed
framework using the real BS location data of a real cellular system with over
200 BSs deployed in the city of Dublin. Our numerical results show that our
framework can significantly improve the workload processing capability of fog
computing networks. In particular, even when each fog node can coordinate only
with its closest neighbor, the total amount of workload processed by fog nodes
can be almost doubled under certain scenarios.
