Exploiting and Extracting Workload Patterns for Efficient IoT Processing
Leming Cheng, Hsin-Yu Ting, Sanfeng Wu, Eli Bozorgzadeh · 2025
Edge computing has become crucial for managing compute-intensive tasks in Internet of Things (IoT) and Cyber-Physical Systems (CPS) where resource-constrained devices seek offloading tasks to nearby edge servers. Conventional scheduling approaches, such as first-come-first-served (FCFS), frequently incur significant dynamic Processing Element (PE) transition overhead, substantially increasing the system latency. However, when multiple end devices transmit acceleration requests at constant rates, temporal patterns emerge in task arrival sequences. This paper proposes a sequence-based and an optimized cluster-based pattern extraction framework to exploit regularities in the entire workload for efficient resource allocation in multi-accelerator edge server systems processing heterogeneous tasks. Whereas the sequence-based approach utilizes a statistical method, the cluster-based approach employs a machine-learning-enabled hierarchical clustering algorithm to derive representative task patterns to generate a static scheduling guideline, exploiting task similarity in occurrence and arrival time. The lightweight scheduler dynamically allocates optimal PEs based on the guideline to maximize acceleration performance. Experiment results show that the proposed cluster-based method achieves up to 4.1x shorter wait time and 2.5x faster response times on average compared to the sequence-based approach, while maintaining near-zero drop rates in dynamic, high-load scenarios.