Abstract
Recently, IoT (Internet of Things) has been an attractive area of research to develop smart home, smart city environment. IoT sensors generate data stream continuously and majority of the IoT based applications are highly delay sensitive. The initially used cloud based IoT services suffers from higher delay and lack of efficient resources utilization. Fog computing is introduced to improve these problems by bringing cloud services near to edge in small scale and distributed nature. This work considers an integrated fog-cloud environment to minimize resource cost and reduce delay to support real-time applications at a lower operational cost. We first present a cooperative three-layer fog-cloud computing environment, and propose a novel optimization model in this environment. This model has a composite objective function to minimize the bandwidth cost and provide load balancing. We consider balancing load in both links' bandwidth and servers' CPU processing capacity level. Simulation results show that our framework can minimize the bandwidth cost and balance the load by utilizing the cooperative environment effectively. We assign weight factors to each objective of the composite objective function to set the level of priority. When minimizing bandwidth cost gets higher priority, at first, the demand generated from the traffic generator sensors continues to be satisfied by the regional capacity of layer-1 fog. If the demand of a region goes beyond the capacity of that region, remaining demand is served by other regions layer-1 fog, then by layer-2 fog, and finally by the cloud. However, when load balancing is the priority, the demand is distributed among these resources to reduce delay. Link level load balancing can reduce the queueing delay of links while server level load balancing can decrease processing delay of servers in an overloaded situation. We further analyzed how the unit bandwidth cost, the average and maximum link utilization, the servers' resources utilization, and the average number of servers used vary with different levels of priority on different objectives. As a result, our optimization formulation allows tradeoff analysis in the cooperative three-layer fog-cloud computing environment.