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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 the realm of edge computing, the optimization of latency, energy, bandwidth, and local computation is critical, especially for mission-critical applications in sectors like disaster management and healthcare. Such applications, exemplified by deploying UAVs and autonomous robots, demand instantaneous data processing. Given the inherent constraints of edge servers—characterized by their limited capacity—meticulous resource management becomes paramount. This entails judicious resource allocation, astute provisioning, strategic task offloading, and judicious application placement, all pivotal for both fixed and mobile resource service delivery. This survey delves deep into the nuances of deploying mission-critical applications in an edge environment, dissecting their technological prerequisites. Our exploration employs a systematic literature review grounded in a conventional review methodology. We analyze the cornerstone quality of service metrics pivotal for such critical applications in edge contexts, aiming for efficient service delivery. Moreover, we identified some major gaps in current resource management strategies. Our overarching ambition is to pave the way for robust edge computing paradigms tailored for mission-critical applications.

Disaster management, such as early warnings for earthquakes, hurricanes, and fires, requires IoT sensors and cameras, which produce tremendous amounts of data.To avoid network bandwidth congestion, much of this data needs to be processed close to where it is produced, as enabled by Mobile Edge Clouds (MEC). However, for such use cases, the disaster itself may take out the MEC, hence hindering disaster management efforts. We present a fault tolerance infrastructure tailored specifically for MEC systems to address various types of failures as part of a holistic disaster recovery solution. Our research investigates using current technologies, such as Kubernetes, to effectively handle fault tolerance in situations involving the failure of one or several edge nodes and RabbitMQ as a resilient message broker in our proposed infrastructure to ensure dependable message transmission, even during network outages. To evaluate our framework, we conduct a case study using weather stations as mission-critical assets within an urban setting next to forests where edge nodes are placed as safely as possible. The experiments demonstrate that the infrastructure can handle two node failures simultaneously. The proposed infrastructure ensures 99.966\% availability for both the system and mission-critical applications.