Abstract: 

The rapid expansion of time-critical applications such as health monitoring, self-driving cars, intelligent traffic control, and emergency services, with substantial demands on high bandwidth and ultra-low latency pose critical challenges for Cloud data centers. To address time-critical application demands, the computing continuum emerged as a new distributed platform that extends the Cloud toward nearby Edge and Fog resources, substantially decreasing communication latency and network traffic. However, the distributed and heterogeneous nature of the computing continuum with sporadic availability of devices may result in service failures and deadline violations, significantly negating its advantages for hosting time-critical applications with diverse demands and lowering users’ satisfaction. Additionally, the dense deployment and intense competition for limited nearby resources in the computing continuum pose resource utilization challenges. To tackle these problems, we investigated the problem of time-critical application placement with constraint deadlines and various demands in the heterogeneous computing continuum with three main contributions:

A multilayer resource partitioning model for placing time-critical applications to minimize resource wastage while maximizing deadline satisfaction;

An adaptive placement for dynamic computing continuum with sporadic device availability to minimize resource wastage and maximize deadline satisfaction;

A proactive service level agreement-aware placement method, leveraging distributed monitoring to enhance deadline satisfaction and service success.