# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 4 Results  
The success and efficiency of multi-level systems, whether traditional or incorporating segments such as space, are deeply entrenched in the Quality of Service (QoS) they offer and their inherent reliability. Ensuring high QoS and reliability becomes especially pivotal in a complex, geographically distributed network. QoS denotes the performance level of a service or system, encapsulating various parameters such as latency, throughput, availability, and error rate. In multi-level systems:  
* Latency refers to the time it takes for a packet of data to move from the source to the destination. In geographically distributed networks, this is influenced by the number of tiers the data have to traverse and the nature of those tiers (terrestrial, aerial, or orbital). * Throughput is the amount of data that can be transferred within a given time frame. It is influenced by the bandwidth and capacity of each tier. * Availability is the likelihood that the system or service is operational and accessible when needed. It is directly correlated with the system's reliability. * Error rate is the frequency at which errors occur during data transmission or processing. In multi-level systems, errors can arise from factors like signal interference, hardware malfunctions, or software glitches. In the context of multi-level systems, reliability focusing on availability stands out as a paramount concern. Availability, in essence, indicates the system's operational uptime, ensuring that users can access services without disruptions. There are some main challenges in maintaining QoS and reliability:  
* As the number of connected devices grows, ensuring consistent QoS and reliability can become challenging. * Effective communication and coordination between tiers are essential, especially in dynamic scenarios. * External factors like space weather for satellite tiers, physical obstructions for aerial tiers, or terrestrial network congestion can pose challenges. While the multi-tier architecture of geographically distributed networks offers numerous advantages in terms of scalability, flexibility, and coverage, maintaining a consistent quality of service and ensuring high reliability are fundamental to the system's success. As technology advances, strategies to bolster QoS and reliability will be crucial for these multi-level systems to fulfill their promise and potential. Reliability, in the context of geographically distributed networks, signifies the capability of the system to provide uninterrupted service despite inherent uncertainties and potential failures. Historically, several mathematical models have been utilized to analyze and predict reliability. Among them, Markov reliability models have emerged as a potent tool due to their adaptability and comprehensive nature [38]. Markov reliability models, with their probabilistic approach and inherent flexibility, serve as invaluable tools in the analysis of complex systems. Their adaptability makes them an apt choice for the proposed model, aiming to understand and predict the availability of service in geographically distributed networks [39]. Adapting Markov reliability models for multi-level systems requires an understanding of the interplay between various layers, their unique characteristics, and the overarching network dynamics. When appropriately tailored, these models can provide insight into the reliability and availability of service across the entire network, guiding both design decisions and operational strategies. In any complex system, particularly multi-level networks, the concept of availability stands out as a paramount metric. Availability denotes the proportion of time a system, or any of its components, is operational and ready to deliver the expected service. Within the framework of Markov reliability models adapted for multi-level systems, understanding and measuring availability demands a specialized approach.