Abstract
Fog and edge computing has emerged as an important paradigm to address many challenges related to time-sensitive and real-time applications, high network loads, user privacy, security, and others. While these developments offer huge potential, many efforts are needed to study and design applications and systems for these emerging computing paradigms. This paper provides a detailed study of workflow scheduling and offloading of service-based applications. We develop different models of cloud, fog and edge systems and study the scheduling of workflows (such as scientific and machine learning workflows) using a range of system sizes and application intensities. Firstly, we develop several Markov models of cloud, fog, and edge systems and compute the steady-state probabilities for system utilization and stability. Secondly, using steady-state probabilities, we define a range of system metrics to study the performance of workflow scheduling and offloading including, network load, response delay, energy consumption, and energy costs. An extensive investigation of application intensities and cloud, fog, and edge system sizes reveals that significant benefits can be accrued from the use of fog and edge computing in terms of low network loads, response times, energy consumption and costs.