Allocation and Scheduling of Linear Workflows Incorporating Security Constraints Across Fog and Cloud Infrastructures

The escalating volume of data produced by IoT devices places heavy pressure on the resources of conventional cloud data centers. This impedes their ability to meet the demands of IoT applications, especially those with time-sensitive requirements. Fog computing, a contemporary computing paradigm, extends the capabilities of cloud resources to the network's periphery. In these collaborating computing environments, various workloads encompass jobs characterized by differing levels of security requirements. It is imperative to implement security-aware scheduling schemes to ensure the proper execution of applications and achieve the desired Quality of Service (QoS). Consequently, in this research, we investigate security-aware scheduling techniques tailored for linear workflow (LW) applications within a fog-cloud computing framework. Fog resources are deemed suitable for processing LW jobs with high security requirements, termed “fog jobs”. Conversely, LW jobs with medium or low security requirements, referred to as”cloud jobs”, may be executed on either cloud or fog resources. To facilitate timely execution of fog jobs, two versions of a novel scheduling technique are introduced, utilizing approximate computations for cloud jobs. Simulation-based evaluations provide valuable insights into how these approximate computations, contingent upon cloud jobs' security levels, impact system performance across the investigated scenarios.