Latency-Aware Deployment of IoT Services in a Cloud-Edge Environment

Abstract

Efficient scheduling of data elements and computation units can help to reduce the latency of processing big IoT stream data. In many cases, moving computation turns out to be more cost-effective than moving data. However, deploying computations from cloud-end to edge devices may face two difficult situations. First, edge devices usually have limited computing power as well as storage capability, and we need to selectively schedule computation tasks. Secondly, the overhead of stream data processing varies over time and makes it necessary to adaptively adjust service deployment at runtime. In this work, we propose a heuristics approach to adaptively deploying services at runtime. The effectiveness of the proposed approach is demonstrated by examining real cases of China’s State Power Grid.