# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 5 Discussion  
Understanding the availability of dedicated connections in multi-tier systems, particularly using a Markov chain model, offers a distinctive blend of advantages. The approach  
Figure 8: Impact of the number of tiers on the unavailability of a dedicated endpoint service in the multi-tier system architecture. not only provides robustness in reliability analysis but also ensures that system designers and maintainers have a more granular view of their network. Each dedicated connection is analyzed in isolation, ensuring that its metrics are pure and unaffected by other system components. By understanding each connection's behavior, tailored solutions can be created. Instead of a one-size-fits-all solution that may not address specific weak points, this granular understanding enables targeted interventions. To demonstrate the advantages of the proposed methodology for multi-tier availability modeling, we can compare it, in general, against prior work using simulation-based approaches and analytical models. While simulations can provide high-fidelity availability estimates, such techniques are typically computationally intensive and lack analytical tractability. The Markov modeling approach is more efficient and enables mathematical analysis. Purely analytical methods in the literature often model reliability within isolated tiers but do not capture inter-tier dependencies and cascading failures. The proposed Markov methodology is better equipped to account for cross-tier effects on end-to-end availability. Some data-driven techniques are restricted to only predicting availability for specific tiers, like edge devices. They have limited generalization capability across complex multi-tier topologies. In contrast, the flexibility of the Markov modeling approach allows it to analyze arbitrary heterogeneous architectures. The proposed methodology balances model complexity, computational efficiency, mathematical insight, and the ability to assess end-to-end availability across interconnected systems. This makes it well suited for availability analysis in emerging multi-tier networks, in comparison to the limitations of existing techniques. The methodology can be iteratively applied to various connections across the system. This modular approach means that as the system grows or evolves, the methodology remains relevant and applicable. By using the same Markov model across various dedicated connections, there is uniformity in how availability is determined. This makes comparisons and aggregations more coherent. The Markov chain model offers predictive insights by analyzing the probability of transitions between states. This provides foresight, allowing stakeholders to prepare for potential downtimes or disruptions. Understanding the individual availability of dedicated connections can aid in quicker root cause analysis during system outages or disruptions. Instead of sifting through the entire system, the affected connection can be quickly identified and rectified. With a clear understanding of which connections are most and least reliable, resources (both human and technological) can be allocated more efficiently. This prevents wastage and ensures that attention is directed where it is most needed. The predictive insights from the Markov model can guide preventive maintenance schedules. This can lead to cost savings, as potential disruptions are addressed before they escalate into bigger issues. While our discussion centers around IoT in multi-tier systems, the methodology is versatile. It can be adapted for various domains and scenarios in which understanding the availability or reliability of individual connections or components is pivotal. The approach provides foundational data that can be used in more complex system reliability models. For instance, once individual availabilities are understood, they can be incorporated into systemic models that consider interdependencies and cascading effects.