Publications

Context: Recent developments in modern IT systems including internet of things, edge/fog computing, or cyber–physical systems support intelligent and seamless interaction between users and systems. This requires a reaction to changes in their environment or the system. Adaptive systems provide mechanisms for these reactions. Objective: To implement this functionality, several approaches for the planning of adaptations exist that rely on rules, utility functions, or advanced techniques, such as machine learning. As the adaptation space with possible options is often extensively huge, optimization techniques might support efficient determination of the adaptation space and identify the system’s optimal configuration. With this paper, we provide a systematic review of adaptation planning as the optimization target. Method: In this paper, we review which optimization techniques are applied for adaptation planning in adaptive systems using a systematic literature review approach. Results: We reviewed 115 paper in detail out of an initial search set of 9,588 papers. Our analysis reveals that learning techniques and genetic algorithms are by far dominant; in total, heuristics (anytime learning) are more frequently applied as exact algorithms. We observed that around 57% of the approaches target multi-objectiveness and around 30% integrate distributed optimization. As last dimension, we focused on situation-awareness, which is only supported by two approaches. Conclusion: In this paper, we provide an overview of the current state of the art of approaches that rely on optimization techniques for planning adaptations in adaptive systems and further derive open research challenges, in particular regarding the integration of distributed optimization and situation-awareness.

Autoscaling is a task of major importance in the cloud computing domain as it directly affects both operating costs and customer experience. Although there has been active research in this area for over ten years now, there is still a significant gap between the proposed methods in the literature and the deployed autoscalers in practice. Hence, many research autoscalers do not find their way into production deployments. This paper describes six core challenges that arise in production systems that are still not solved by most research autoscalers. We illustrate these problems through experiments in a realistic cloud environment with a real-world multi-service business application and show that commonly used autoscalers have various shortcomings. In addition, we analyze the behavior of overloaded services and show that these can be problematic for existing autoscalers. Generally, we analyze that these challenges are only insufficiently addressed in the literature and conclude that future scaling approaches should focus on the needs of production systems.

Application performance management (APM) tools are useful to observe the performance properties of an application during production. However, APM is normally purely reactive, that is, it can only report about current or past performance degradation. Although some approaches capable of predictive application monitoring have been proposed, they can only report a predicted degradation but cannot explain its root-cause, making it hard to prevent the expected degradation. In this paper, we present SuanMing---a framework for predicting performance degradation of microservice applications running in cloud environments. SuanMing is able to predict future root causes for anticipated performance degradations and therefore aims at preventing performance degradations before they actually occur. We evaluate SuanMing on two realistic microservice applications, TeaStore and TrainTicket, and we show that our approach is able to predict and pinpoint performance degradations with an accuracy of over 90%.