Computational Offloading for Non-Time-Critical Applications

Abstract

The increasing demand for computational resources keeps outpacing available User Equipment (UE). To overcome intrinsic hardware limitations of UEs, computational offloading was proposed. The combination of UE and seemingly endless computational capacity in the cloud aims to cope with those limitations. Numerous frameworks leverage Edge Computing (EC) but a significant drawback of this is the required infrastructure. Some use cases however, do not benefit from lower response time and can remain in the cloud, where more potent resources are at one’s disposal. Main contributions are to determine computational demands, allocate serverless resources, partition code and integrate computational offloading into a modern software deployment process. By focusing on non-time-critical use cases, drawbacks of EC can be neglected to create a more developer-friendly approach. Originality lies in the resource allocation of serverless resources for such endeavours, appropriate deployment of partitions and integration into CI/CD pipelines. Methodology used will be Design Science Research. Thus, many iterations and proof-of-concept implementations yield knowledge and artefacts.