## 3 Related Work
### Serverless in Edge Computing  
This subsection is dedicated to illustrating the current research efforts for taking advantage of serverless computing features to manage edge systems. To this end, we present some recent contributions that provide an overview of the most popular research topics. Review paper [1] focuses on novel criteria to evaluate the maturity of serverless computing for the edge-cloud continuum. The authors identify several maturity levels of serverless edge computing, used to evaluate the maturity of current state-of-the-art serverless approaches. The considered key aspects are programming, reliability, and performance. Paper [6] focuses on function allocation policy in edge systems based on serverless computing. The research purpose is to help edge nodes select the functions to deploy in order to satisfy incoming service requests while reducing latency and minimizing request outage rates. A deep deterministic policy gradient (DDPG)-based function allocation algorithm is used. In paper [33], the authors propose an algorithm to optimize the weighted cost for IoT applications scheduling in edge/fog computing environments. The proposed cost model is tailored to optimizing load balancing and response time. The experimental setup is based on a scheduler for a function-as-a-service framework designed for IoT applications. A proposal for resource orchestration in dynamic edge environments, called FEARLESS, is presented in [11]. It is based on a reinforcement learning approach to orchestrating function invocation requests in serverless swarms. The experimental results show positive effects on SLA violations. Cold start latency is considered in [34]. The paper presents a proposal related to machine learning-based resource management, called MASTER. The proposal utilizes an extreme gradient boosting model to predict the cold start latency for Industry 4.0 applications. The experimental results show appreciable performance metric values in comparison with other solutions based on deep deterministic policy gradient and long short-term memory algorithms. Paper [2] shows a proposal combining deep reinforcement learning and deep neural networks for edge computing systems. The resulting framework, called DRLFO, is based on actor-critic algorithms used to trigger offloading operations. Performance is evaluated by experiments. Paper [35] has some objectives in common with this paper, since it deals with the problem of efficient scaling serverless functions in edge systems for delay-sensitive applications. A combination of Q-learning and deep neural network is proposed. Performance is evaluated with reference to a baseline approach based on monitoring-based heuristics. The simulation results show that the RL algorithm outperforms the considered baseline system. Paper [36] investigates the integration of serverless computing with edge, fog, and cloud computing layers. The main application context is the IoT. The performance of a serverless platform is analyzed by considering different function types and workloads, using the ServerlessBench platform. An experimental analysis of the serverless autoscaling function is shown in [12; 37]. Some basic metrics are considered, such as success ration and response time, and the related auto-scaling effects. An edge computing system managed by serverless technologies is considered in [10]. It is implemented through Raspberry Pis. The authors show an extensive comparison of the performance achievable by using AWS Greengrass, OpenFaaS, and Apache OpenWhisk. In the considered scenario, OpenFaaS outperforms the other technologies. However, horizontal function autoscaling is not considered. In [8], performance analysis of some open-source FaaS platforms--namely, OpenFaaS, Kubeless, Knative, and Fission--is shown. Particular emphasis is placed on latency. The functions used in the analysis are implemented through Golang, Python, and NodeJS. The analysis includes auto-scaling capabilities, based on both workload-based and resource-based policies.