Abstract
While a wide range of resources is available on orchestration techniques and best practices for containerized software systems, many are not documented clearly or in detail. This complicates the process of selecting the most suitable methods for various usage scenarios. To address this gap, we documented a set of orchestration patterns. This paper reports the results of a focus group conducted during the EuroPLoP 2024 conference, where we aimed to obtain feedback on that group of patterns and on a wider pattern map we outlined. We also aimed to identify container orchestration patterns that have not yet been documented. We found that participants knew most of the patterns we included on the pattern map. Additionally, one of the practices mentioned by the participants (Node Balancing) was previously documented as a pattern by us with the name of Service Balancing. Finally, we found important insights into container orchestration patterns, expanding our pattern map to include eight new proto-patterns.

Abstract
Logging has long been a pillar for monitoring and troubleshooting software systems. From server and infrastructure to application-specific data, logs are an easy and quick way to collect information that may prove useful in diagnosing future issues. When systems become distributed, as is common on the cloud, logs are harder to collect and process. This paper presents three design patterns for logging in cloud-native applications. Standard Logging advises using a standard format for logs across all services and teams so they are easier to process by humans and machines. Audit Logging suggests that important user actions and system changes are recorded in a data store to ensure regulatory compliance or help investigate user-reported issues. Lastly, Log Sampling is about prioritizing logs to maintain a manageable amount of storage. These patterns were mined from existing literature on logging and cloud best practices to make them simpler to communicate, more detailed, and easier for all practitioners to understand.

Abstract
Although service-based architectures offer significant advantages, some aspects of service orchestration remain challenging, particularly for new adopters. Despite the availability of resources on orchestration techniques, many lack clarity or detail. As a result, best practices are often not well explained or standardized, making them difficult to implement and hindering broader adoption within the software industry. To address these concerns, we looked into existing literature and tools to identify common practices. We used our findings to describe as patterns two patterns focused on orchestration configuration, which we present in this paper, and that serve as a stepping stone for other orchestration practices: labeling and resource reserve and limit. These patterns contribute to configuring a system; the former consists of defining key-value pairs to express identifiable properties of system components, and the latter is about supporting two bounds for each resource type: the amount of resources reserved for the service to operate and the maximum amount of resources it can use.

Abstract
Incidental visualizations allow individuals to access information on-the-go, at-a-glance, and without needing to consciously search for it. Unlike ambient visualizations, incidental visualizations are not fixed in a specific location and only appear briefly within a person's field of view while they are engaged in a primary task. Despite their potential, incidental visualizations have not yet been thoroughly studied in current literature. We conducted exploratory research to establish the distinctiveness of incidental visualizations and to advocate for their study as an independent research topic. We tested both incidental and ambient visualizations in two separate studies, each involving one specific scenarios: a cognitively demanding primary task (42 participants), and a mechanical primary task (28 participants). Our findings show that in the cognitively demanding task, both types of visualizations resulted in similar performance. However, in the mechanical task, ambient visualizations led to better results compared to incidental visualizations. Based on these results, we argue that incidental visualizations should be further explored in scenarios involving physical requirements, as these situations present the greatest challenges for their integration.

Abstract

Abstract
Incidental visualizations convey information to a person during an ongoing primary task, without the person consciously searching for or requesting that information. They differ from glanceable visualizations by not being people's main focus, and from ambient visualizations by not being embedded in the environment. Instead, they are presented as secondary information that can be observed without a person losing focus on their current task. However, despite extensive research on glanceable and ambient visualizations, the topic of incidental visualizations is yet a novel topic in current research. To bridge this gap, we conducted an empirical user study presenting participants with an incidental visualization while performing a primary task. We aimed to understand how complexity contributory factors - task complexity, output complexity, and pressure - affected primary task performance and incidental visualization accuracy. Our findings showed that incidental visualizations effectively conveyed information without disrupting the primary task, but working memory limitations should be considered. Additionally, output and pressure significantly influenced the primary task's results. In conclusion, our study provides insights into the perception accuracy and performance impact of incidental visualizations in relation to complexity factors. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).