Claus Pahl

Nabor C. Mendonca, Pooyan Jamshidi, David Garlan et al.

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies.

Davide Taibi, Valentina Lenarduzzi, Claus Pahl

Context: Several companies are migrating their information systems into the Cloud. Microservices and DevOps are two of the most common adopted technologies. However, there is still a lack of understanding how to adopt a microservice-based architectural style and which tools and technique to use in a continuous architecting pipeline. Objective: We aim at characterizing the different microservice architectural style principles and patterns in order to map existing tools and techniques adopted in the context of DevOps. Methodology: We conducted a Systematic Mapping Study identifying the goal and the research questions, the bibliographic sources, the search strings, and the selection criteria to retrieve the most relevant papers. Results: We identified several agreed microservice architectural principles and patterns widely adopted and reported in 23 case studies, together with a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. Finally, we mapped the existing microservices-specific techniques in order to understand how to continuously deliver value in a DevOps pipeline. We depicted the current research, reporting gaps and trends. Conclusion: Different patterns emerge for different migration, orchestration, storage and deployment settings. The results also show the lack of empirical work on microservices-specific techniques, especially for the release phase in DevOps.

Pierangelo Rosati, Frank Fowley, Claus Pahl et al.

Cloud computing promises traditional enterprises and independent software vendors a myriad of advantages over on-premise installations including cost, operational and organizational efficiencies. The decision to migrate software configured for on-premise delivery to the cloud requires careful technical consideration and planning. In this chapter, we discuss the impact of right-scaling on the cost modelling for migration decision making and price setting of software for commercial resale. An integrated process is presented for measuring total cost of ownership, taking in to account IaaS/PaaS resource consumption based on forecast SaaS usage levels. The process is illustrated with a real world case study.

Davide Taibi, Valentina Lenarduzzi, Claus Pahl

Several companies are re-architecting their monolithic information systems with microservices. However, many companies migrated without experience on microservices, mainly learning how to migrate from books or from practitioners' blogs. Because of the novelty of the topic, practitioners and consultancy are learning by doing how to migrate, thus facing several issues but also several benefits. In this chapter, we introduce a catalog and a taxonomy of the most common microservices anti-patterns in order to identify common problems. Our anti-pattern catalogue is based on the experience summarized by different practitioners we interviewed in the last three years. We identified a taxonomy of 20 anti-patterns, including organizational (team oriented and technology/tool oriented) anti-patterns and technical (internal and communication) anti-patterns. The results can be useful to practitioners to avoid experiencing the same difficult situations in the systems they develop. Moreover, researchers can benefit of this catalog and further validate the harmfulness of the anti-patterns identified.

Hamid Arabnejad, Claus Pahl, Pooyan Jamshidi et al.

A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL). As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time.

Pooyan Jamshidi, Amir Sharifloo, Claus Pahl et al.

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling.