Software systems have reached a significant level of complexity. Most applications are designed with Cloud Computing in mind, according to DevOps concepts, CI/CD, containers, and microservices. These applications are referred to as Cloud-native applications. Despite the undeniable benefits, such as scalability, high availability, and efficient system deployment, this paradigm presents several challenges. One of them is ensuring that the system is autonomic, namely that the system is able to automatically adjust its behavior in response to changes in state and external factors. In this article, we opt for the fact that to enable autonomic features in any system, actions must address the whole execution environment. The proposed architecture of an action distinguishes three of its possible states that are: definition, planning, and processing. In each state, actions can be characterized by properties that cover aspects such as prioritization or idempotency restrictions. Then, we propose an architecture of the execution controller—a component processing the actions and responsible for modification of the Cloud-native execution environment in response to occurring anomalies. The proposed solution was evaluated with respect to performance, management capabilities and the impact on autonomy. Hence, showing improvement of dynamic Cloud-native execution environments.

Abstract Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices.

Abstract The Autonomic Computing paradigm reduces complexity in installing, configuring, optimizing, and maintaining heterogeneous systems. Despite first discussing it a long ago, it is still a top research challenge, especially in the context of other technologies. It is necessary to provide autonomic features to the Cloud-native execution environment to meet the rapidly changing demands without human support and continuous improvement of their capabilities. The present work attempts to answer how to explore autonomic features in Cloud-native environments. As a solution, we propose using the AMoCNA framework. It is rooted in Autonomic Computing. The success factors for the AMoCNA implementation are its execution controllers. They drive the management actions proceeding in a Cloud-native execution environment. A similar concept already exists in Kubernetes, so we compare both execution mechanisms. This research presents guidelines for including autonomic features in Cloud-native environments. The integration of Cloud-native Applications with AMoCNA leads to facilitating autonomic management. To show the potential of our concept, we evaluated it. The developed executor performs cluster autoscaling and ensures autonomic management in the infrastructure layer. The experiment also proved the importance of observations. The knowledge gained in this process is a good authority of information about past and current state of Cloud-native Applications. Combining this knowledge with defined executors provides an effective means of achieving the autonomic nature of Cloud-native applications.

The policy-based management paradigm in a flexible manner governs the system behavior. For Cloud-native applications, additionally, it simplifies the compliance with CI/CD objectives. Hence, the velocity of changes in requirements made at runtime does not influence the system implementation. Continuously the adjustments are integrated into the system on the fly. This paper evaluates the rule-based approach to representing policies in the context of Cloud-native applications. Deploying applications in orchestrated environments is one of the main principles of Cloud-native. Our approach represents the extension of the management characteristics that are available in current implementations of the orchestrators. The presented study also shows a general methodology for experimental evaluation of complex Cloud-native environments. We propose two categories of experiments. Both evaluate the rule-based approach. The first category evaluates the impacts of dynamic adjustment of resources in the context of the Cloud-native execution environment. The second category assesses the influence of the rule engine approach on the autonomic management process. Given the wide range of available experiments, we additionally assume that evaluation is performed from the point of view of the execution environment's resources. This approach tightly embraces the capabilities of the proposed solution realized by the AMoCNA system and demonstrates its usability.

Abstract In order to meet the rapidly changing requirements of the Cloud-native dynamic execution environment, without human support and without the need to continually improve one’s skills, autonomic features need to be added. Embracing automation at every layer of performance management enables us to reduce costs while improving outcomes. The main contribution of this paper is the definition of autonomic management requirements of Cloud-native applications. We propose that the automation is achieved via high-level policies. In turn autonomy features are accomplished via the rule engine support. First, the paper presents the engineering perspective of building a framework for Autonomic Management of Cloud-Native Applications, namely AMoCNA, in accordance with Model Driven Architecture (MDA) concepts. AMoCNA has many desirable features whose main goal is to reduce the complexity of managing Cloud-native applications. The presented models are, in fact, meta-models, being technology agnostic. Secondly, the paper demonstrates one possibility of implementing the aforementioned design procedures. The presented AMoCNA implementation is also evaluated to identify the potential overhead introduced by the framework.

Abstract Contact: [email protected] or [email protected]

This paper presents the concept of an Adaptive SOA Solution Stack (AS3). It is an extension of the S3 model, implemented via uniform application of the AS3 element pattern across different layers of the model. The pattern consists of components constituting an adaptation loop. The functionality of each component is specified in a generic way. Aspects of these patterns are analyzed in relation to individual S3 layers. The ability to achieve multilayer adaptation, provided by several cooperating AS3 elements is also discussed. Practical usage of the proposed concepts for Adaptive Operational Systems, Integration, and Service Component layers are presented in the form of three case studies. Each study describes the architecture of the proposed system extensions, selected software technologies, implementation details, and sample applications. Related work is discussed in order to provide a background for the reported research. This paper ends with conclusions and an outline of future work.