The increasing variety of input data and complexity of tasks that are handled by the devices of internet of things (IoT) environments require solutions that consider the limited hardware and computation power of the edge devices. Complex event processing (CEP), can be given as an example, which involves reading and aggregating data from multiple sources to infer triggering of important events. In this study, we balance the execution costs between different paths of the CEP task graph with a constrained programming optimization approach and improve critical path performance. The proposed approach is implemented as a Python library, allowing small-scale IoT devices to adaptively optimize code and I/O assignments and improve overall latency and throughput. The implemented library abstracts away the communication details and allows virtualization of a shared memory between IoT devices. The results show that optimizing critical path performance increases throughput and reduces delay across multiple devices during CEP operations.

翻译：物联网（IoT）环境中设备所处理输入数据的日益多样化及任务复杂性的不断提升，要求解决方案必须考虑边缘设备有限的硬件资源与计算能力。复杂事件处理（CEP）便是一个典型示例，其涉及从多源读取并聚合数据以推断重要事件的触发。本研究通过约束规划优化方法，平衡CEP任务图中不同路径间的执行开销，并优化关键路径性能。所提出的方法实现为一个Python库，使小规模物联网设备能够自适应地优化代码与I/O分配，从而改善整体延迟与吞吐量。该实现库抽象了通信细节，支持物联网设备间共享内存的虚拟化。实验结果表明，在CEP操作过程中优化关键路径性能可有效提升多设备间的吞吐量并降低延迟。