Increased resilience compared to monolithic architectures is both one of the key promises of microservice-based architectures and a big challenge, e.g., due to the systems’ distributed nature. Resilience assessment through simulation requires fewer resources than the measurement-based techniques used in practice. However, there is no existing simulation approach that is suitable for a holistic resilience assessment of microservices comprised of (i) representative fault injections, (ii) common resilience mechanisms, and (iii) time-varying workloads. This paper presents MiSim — an extensible simulator for resilience assessment of microservice-based architectures. It overcomes the stated limitations of related work. MiSim fits resilience engineering practices by supporting scenario-based experiments and requiring only lightweight input models. We demonstrate how MiSim simulates (1) common resilience mechanisms — i.e., circuit breaker, connection limiter, retry, load balancer, and autoscaler — and (2) fault injections — i.e., instance/service killing and latency injections. In addition, we use TeaStore, a reference microservice-based architecture, aiming to reproduce scaling behavior from an experiment by using simulation. Our results show that MiSim allows for quantitative insights into microservice-based systems’ complex transient behavior by providing up to 25 metrics.

In a microservice system, runtime changes such as failures, deployments, or self-adaptation can trigger the system to transition from one steady state to another, i.e., exhibiting transient behavior. To assess a system’s quality, it is imperative that this transient behavior is specified in non-functional requirements and that stakeholders can analyze whether these requirements are met. Yet, there is little support for either specifying transient behavior as a non-functional requirement or analyzing how such a requirement is met in production. We aim to make these two tasks more accessible by utilizing novel human-computer interaction methods. To this end, we developed TransVis, an approach for specifying and analyzing transient behavior based on chatbot interactions and visualizations of the systems’ resilience. We examined the effectiveness of our approach by conducting an exploratory expert study on a prototypical implementation. The study revealed that the developed visualizations are effective for specifying and exploring transient behavior. Participants found especially helpful the feature to compare specifications with the actual behavior. However, the integration of a chatbot did not prove effective for our use cases. In conclusion, our approach is capable of supporting stakeholders in the exploration and specification of transient behavior.

Abstract One of the main features of the hypervisor is the isolation among virtual machine (VM) environments. By isolation between VMs, malicious activity in one VM could not affect all other VMs, so it is necessary to apply security mechanisms in order to improve isolation between VMs. Before applying security policies to a virtualization system, it is necessary to quantitatively measure the hypervisor from isolation point of view aiming at increasing security of isolation among VMs; then considering the circumstances of the VM execution environments and the results of measurements, we can find areas in the virtualization system with the most effective on enhancing isolation. This paper, proposed a semi‐Markov model for evaluation of isolation, by studying the Xen virtualization architecture. We considered certain type of vulnerability that successfully exploiting it can lead to the attacker's malicious codes execution in part of memory address space. We included all three layers in virtualization for the evaluation purpose, because we wanted to consider strength and weakness areas in virtualization system and not just specific layer such as hypervisor, so it can be figured out that improving security in which layer of virtualization is most effective in improving security of isolation, in respect to the increasing or decreasing attacker's (defender's) ability to be successful. The sensitivity analysis results show that MTTSF is more sensitive to the increasing ability of defensive mechanisms to be successful at the application layer and decreasing the attacker's ability to successfully exploit vulnerabilities at the guest operating system layer, model parameters. Copyright © 2015 John Wiley & Sons, Ltd.