Most enterprise customers now choose to divide a large monolithic service into large numbers of loosely-coupled, specialized microservices, which can be developed and deployed separately. Docker, as a light-weight virtualization technology, has been widely adopted to support diverse microservices. At the moment, Kubernetes is a portable, extensible, and open-source orchestration platform for managing these containerized microservice applications. To adapt to frequently changing user requests, it offers an automated scaling method, Horizontal Pod Autoscaler (HPA), that can scale itself based on the system’s current workload. The native reactive auto-scaling method, however, is unable to foresee the system workload scenario in the future to complete proactive scaling, leading to QoS (quality of service) violations, long tail latency, and insufficient server resource usage. In this paper, we suggest a new proactive scaling scheme based on deep learning approaches to make up for HPA’s inadequacies as the default autoscaler in Kubernetes. After meticulous experimental evaluation and comparative analysis, we use the Gated Recurrent Unit (GRU) model with higher prediction accuracy and efficiency as the prediction model, supplemented by a stability window mechanism to improve the accuracy and stability of the prediction model. Finally, with the third-party custom autoscaling framework, Custom Pod Autoscaler (CPA), we packaged our custom autoscaling algorithm into a framework and deployed the framework into the real Kubernetes cluster. Comprehensive experiment results prove the feasibility of our autoscaling scheme, which significantly outperforms the existing Horizontal Pod Autoscaler (HPA) approach.

With the rapidly increasing demands of e-government systems in smart cities, a myriad of challenges and issues are required to be addressed. Among them, security is one of the prime concerns. To this end, we analyze different e-government systems and find that an e-government system built with container-based technology is endowed with many features. In addition, overhauling the architecture of container-technology-driven e-government systems, we observe that securing an e-government system demands quantifying security issues (vulnerabilities, threats, attacks, and risks) and the related countermeasures. Notably, we find that the Attack Tree and Attack–Defense Tree methods are state-of-the-art approaches in these aspects. Consequently, in this paper, we work on quantifying the security attributes, measures, and metrics of an e-government system using Attack Trees and Attack–Defense Trees—in this context, we build a working prototype of an e-government system aligned with the United Kingdom (UK) government portal, which is in line with our research scope. In particular, we propose a novel measure to quantify the probability of attack success using a risk matrix and normal distribution. The probabilistic analysis distinguishes the attack and defense levels more intuitively in e-government systems. Moreover, it infers the importance of enhancing security in e-government systems. In particular, the analysis shows that an e-government system is fairly unsafe with a $99\%$ probability of being subject to attacks, and even with a defense mechanism, the probability of attack lies around $97\%$, which directs us to pay close attention to e-government security. In sum, our implications can serve as a benchmark for evaluation for governments to determine the next steps in consolidating e-government system security.