Computation Offloading for Real-Time Applications

Abstract: With the vast and ever-growing range of applications which have started to seek real-time data processing and timing-predictable services comes an extensive list of problems when trying to establish these applications in the real-time domain. Depending on the purpose of the real-time application, the requests that they impose on resources are vastly different. Some real-time applications require large computational power, large storage capacities, and large energy storage. However, not all devices can be equipped with processors, batteries, or power banks adequate for such resource requirements. While these issues can be mitigated by offloading computations using cloud computing, this is not a universal solution for all applications. Real-time applications need to be predictable and reliable, whereas the cloud can cause high and unpredictable latencies. One possible improvement in the predictability and reliability aspect comes from offloading to the edge, which is closer than the cloud and can reduce latencies. However, even the edge comes with certain limitations, and it is not exactly clear how, where and when applications should be offloaded. The problem then presents itself as: how should real-time applications in the-edge cloud architecture be modeled? Moreover, how should they be modeled to be agnostic from certain technologies and provide support for timing analysis? Another thing to consider is the question of 'when' to offload to the edge-cloud architecture. For example, critical computations can be offloaded to the edge, while less critical computations can be offloaded to the cloud, but before one can determine 'where' to offload, one must determine 'when'. Thus, this thesis focuses on designing a new technology-agnostic mathematical model to allow holistic modeling of real-time applications on the edge-cloud continuum and provide support for timing analysis.