Internet censorship limits the access of nodes residing within a specific network environment to the public Internet, and vice versa. During the last decade, techniques for conducting Internet censorship have been developed further. Consequently, methodology for measuring Internet censorship had been improved as well. In this paper, we firstly provide a survey of network-level Internet censorship techniques. Secondly, we survey censorship measurement methodology. We further cover the censorship of circumvention tools and its measurement, as well as available datasets. In cases where it is beneficial, we bridge the terminology and taxonomy of Internet censorship with related domains, namely traffic obfuscation and information hiding. We further extend the technical perspective with recent trends and challenges, including human aspects of Internet censorship.

Control groups (cgroups) are a crucial isolation mechanism in containerized environments, but they don't fully prevent performance interference (noisy neighbors). This paper presents a novel, workload-agnostic approach for detecting noisy neighbors within CPU-isolated cgroups. Using in-kernel profiling with Extended Berkeley Packet Filter (eBPF), we instrument the Linux process scheduler to capture scheduling latencies and preemption frequencies. We introduce a detection method based on these metrics to identify noisy neighbors online without requiring workload profiles or offline analysis. Evaluations across various workload scenarios demonstrate the effectiveness of our approach in accurately identifying performance degradation caused by noisy neighbors.

Industry and academia have strong incentives to adopt virtualization technologies. Such technologies can reduce the total cost of ownership or facilitate business models like cloud computing. These options have recently grown significantly with the rise of Kubernetes and the OCI runtime specification. Both enabled virtualization technology vendors to easily integrate their solution into existing infrastructures, leading to increased adoption. Making a detailed decision on a technology selection based on objective characteristics is a complex task. This specifically includes the instrumentation of performance characteristics that are an important aspect for a fair comparison. Moreover, a subsequent quantification of the isolation capability based on performance metrics is not readily available.

The capability to isolate system resources is an essential characteristic of virtualisation technologies and is therefore important for research and industry alike. It allows the co-location of experiments and workloads, the partitioning of system resources and enables multi-tenant business models such as cloud computing. Poor isolation among tenants bears the risk of noisy-neighbour and contention effects which negatively impacts all of those use-cases. These effects describe the negative impact of one tenant onto another by utilising shared resources. Both industry and research provide many different concepts and technologies to realise isolation. Yet, the isolation capabilities of all these different approaches are not well understood; nor is there an established way to measure the quality of their isolation capabilities. Such an understanding, however, is of uttermost importance in practice to elaborately decide on a suited implementation. Hence, in this work, we present a novel methodology to measure the isolation capabilities of virtualisation technologies for system resources, that fulfils all requirements to benchmarking including reliability. It relies on an immutable approach, based on Experiment-as-Code. The complete process holistically includes everything from bare metal resource provisioning to the actual experiment enactment.

Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is currently an open question to what extent they support scaling at run-time. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. This paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for write-heavy and read-heavy workloads of different intensities. The results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at run-time may even decrease the experienced performance.