Mission-critical applications, such as real-time emergency response, healthcare, and transport systems, depend heavily on the low latency and reliability provided by Mobile Edge Computing (MEC). The failure of such applications can lead to high latency and severe consequences, including loss of life, financial catastrophe, or operational disruption. However, the dependability of edge clusters is often overlooked, particularly in terms of fault awareness and recovery strategies, which are crucial to these applications. In this work, we focus on loosely coupled IoT applications and propose a Failure and Latency-aware Scheduling approach for Edge (FaLSE) that balances the trade-off between the availability of edge clusters and the latency of containerized mission-critical applications. We used a decentralized network coordinate system to estimate latency between IoT devices/users and nodes. To validate the proposed approach, we compare it with the standard Kubernetes scheduler, which is currently among the most widely used workload orchestration platforms. The results indicate that FaLSE reduced the failure request rate by 87.9% while maintaining a 71.97% lower 95th percentile latency for mission-critical applications and a 10.63% lower latency for normal applications compared to the standard Kubernetes scheduler.

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (MultiObjective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.