A dynamic tradeoff data processing framework for delay-sensitive applications in Cloud of Things systems

The steep rise of Internet of Things (IoT) applications along with the limitations of Cloud Computing to address all IoT requirements leveraged a new distributed computing paradigm called Fog Computing, which aims to process data at the edge of the network. With the help of Fog Computing, the transmission latency, monetary spending and application loss caused by Cloud Computing can be effectively reduced. However, as the processing capacity of fog nodes is more limited than that of cloud platforms, running all applications indiscriminately on these nodes can cause some QoS requirement to be violated. Therefore, there is important decision-making as to where executing each application in order to produce a cost effective solution and fully meet application requirements. In particular, we are interested in the tradeoff in terms of average response time, average cost and average number of application loss. In this paper, we present an online algorithm, called unit-slot optimization, based on the technique of Lyapunov optimization. The unit-slot optimization is a quantified near-optimal online solution to balance the three-way tradeoff among average response time, average cost and average number of application loss. We evaluate the performance of the unit-slot optimization algorithm by a number of experiments. The experimental results not only match up the theoretical analyses properly, but also demonstrate that our proposed algorithm can provide cost-effective processing, while guaranteeing average response time and average number of application loss in a three-tier Cloud of Things system.