# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
###### Abstract  
Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with \(p\)-value equal to 0.684 by \(t\)-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization. a 2021 29  
and mobile computing, which provides additional capabilities for mobile devices by centralizing their resources in the cloud [8]. The technique, called offloading, can transfer resource-intensive tasks to remote clouds. MCC can reduce latency problems by providing a secure and efficient model. However, cloud resources are often far away from end-users [9]. Thus, with multiple mobile devices, MCC faces notable challenges, such as high latency, security vulnerability, and limited data transmission. Mobile Edge Computing (MEC) has emerged as a more efficient alternative to MCC architecture. The main goal of MEC is to address the challenges that MCC has been facing, deploying resources even closer to users--at the edge of the network. Thus, computing and storage are performed closer to the source device [10]. MEC aims to enable the billions of connected mobile devices to execute the real-time compute-intensive applications directly at the network edge. The distinguishing features of MEC are its closeness to end-users, mobility support, and dense geographical deployment of the MEC servers [11]. Since MEC is still considered a recent topic, some research gaps still have to be explored, such as performance evaluation of MEC architectures. Availability evaluation is another topic of interest in the MEC area. MEC was designed to provide services in real-time, and therefore, robustness and availability are essential requirements, since the resources are close to the clients, providing them critical services. All components of a system are prone to failure, and failures must be repaired as soon as possible. Assessing the availability of a MEC architecture can be costly, considering practical experiments. Thus, it is necessary to evaluate MEC architectures even before a real deployment, using analytical models, for example. Therefore, there is a lack of studies evaluating MEC architectures through analytical models in terms of performance and availability. Stochastic Petri nets (SPN) [12; 13] are analytical models capable of representing concurrency, synchronization, and parallelism of complex systems. Among other metrics, SPN are well suited to evaluate performance and availability [14; 15; 16].