Fog computing is promising for Industrial Internet of Things (IIoT) which are typically large-scale, time-critical, and geo-distributed. However, it needs to address challenges such as scalability and heterogeneity in application provisioning with diverse and strict Quality-of-Service (QoS) requirements from IIoT devices. In this paper, a fog service orchestrator, Q-FSO, is proposed for IIoT application provisioning in which request satisfaction is maximized. With a two-level QoS model that considers key performance metrics for IIoT, including availability, reliability, response time, and cost, Q-FSO can realize large-scale service orchestration by exploiting the advantages of fog computing. Further with a harmonious local QoS assignment model, Q-FSO solves the workflow construction problem by multiple independent local optimization tasks, one for each stage, for the concurrent requests. Each local optimization task can be reduced from the multiple multidimensional knapsack (MMKP) problem. We then propose two practical MMKP heuristic algorithms, namely, Incremental Similarity Matching (ISM) and Greedy Multiple Matching (GMM), to tackle the local QoS assignment problem with polynomial time complexity. The scalability issue in orchestration is then handled by a decentralized workflow construction protocol. The simulation results validate their applicability, demonstrating that Q-FSO is indeed an efficient and effective IIoT application provision mechanism with significant performance benefits in terms of service processing throughput, resource utilization, and speed of service orchestration.
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