Fog computing, which provides low-latency computing services at the networkedge, is an enabler for the emerging Internet of Things (IoT) systems. In thispaper, we study the allocation of fog computing resources to the IoT users in ahierarchical computing paradigm including fog and remote cloud computingservices. We formulate a computation offloading game to model the competitionbetween IoT users and allocate the limited processing power of fog nodesefficiently. Each user aims to maximize its own quality of experience (QoE),which reflects its satisfaction of using computing services in terms of thereduction in computation energy and delay. Utilizing a potential game approach,we prove the existence of a pure Nash equilibrium and provide an upper boundfor the price of anarchy. Since the time complexity to reach the equilibriumincreases exponentially in the number of users, we further propose anear-optimal resource allocation mechanism and prove that in a system with $N$IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$time. Through numerical studies, we evaluate the users' QoE as well as theequilibrium efficiency. Our results reveal that by utilizing the proposedmechanism, more users benefit from computing services in comparison to anexisting offloading mechanism. We further show that our proposed mechanismsignificantly reduces the computation delay and enables low-latency fogcomputing services for delay-sensitive IoT applications.
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