Modern cloud-native applications are adopting the microservice architecture in which applications are deployed in lightweight containers that run inside a virtual machine (VM). Containers running different services are often co-located inside the same virtual machine. While this enables better resource optimization, it can cause interference among applications. This can lead to performance degradation. Detecting the cause of performance degradation at runtime is crucial to decide the correct remediation action such as, but not limited to, scaling or migrating. We propose a non-intrusive detection technique that differentiates between degradation caused by load and by interference. First, we define an operational zone for the application. Then we define a disambiguation method that uses models to classify interference and normal load. In contrast to previous work, our proposed detection technique does not require intrusive application instrumentation and incurs minimal performance overhead. We demonstrate how we can design effective Machine Learning models that can be generalized to detect interference from different types of applications. We evaluate our technique using realistic microservice benchmarks on AWS EC2. The results show that our approach outperforms existing interference detection techniques in F_1 score by at least 2.75% and at most 53.86%.

In self-adaptive systems, model-based control assumes decisions are taken based on a model that is identified at run-time. The model is built by measuring the control inputs, disturbances, and outputs of the controlled system and fitting the data into a function. Models can be accurate locally, that is, for data already seen by the system and by the model identification method. However, many times an Autonomic Manager (AM) needs to move the cloud-native applications into new operational points, e.g. by adding new applications to the shared environment, scaling applications or consolidating resources. There are no data points yet for these new operational regions to have any certainty that the prediction models are accurate. In this paper, we propose a method to identify a model that predicts metrics at any unexplored operational point of a cloud-native application. The method is based on a lightweight Look-Ahead Scanner (LAS) mechanism that explores different operational points by injecting controlled short-lived load. We evaluate our method on realistic applications deployed on public clouds. We show that the proposed method can build models that outperform the state of the art ML models by 42%.

DevOps is an emerging paradigm that integrates the development and operations teams to enable fast and efficient continuous delivery of software. Applications and services deployed on cloud platforms can benefit from implementing the DevOps practice. This involves using different tools for enabling end-to-end automation to ensure continuous deployment and maintain good Quality-of-Service. Self-Adaptive systems can support the DevOps process by automating service deployment and maintenance without manual intervention by employing a MAPE-K (Monitoring, Analysis, Planning, Execution- Knowledge) framework. While industrial MAPE-K tools are robust and built for production environments, they lack the flexibility to adapt large applications on multi-cloud environments. Academic models are more flexible and can be used to perform sophisticated self-adaption, but can lack the robustness to be used in production environments. In this paper, we present a MAPE-K framework that is built with existing Components-off-the-Shelf (COTS) that interacts with each other to perform self-adaptive actions on multi-cloud environments. By integrating existing COTS, we are able to deploy a MAPE-K framework efficiently to support DevOps for applications running on a multi-cloud environment. We validate our framework with a prototype implementation and demonstrate its practical feasibility by a detailed case study done on a real industrial platform.

Web software systems generally reside in highly volatile environments, their incoming traffic may be subject to sharp fluctuations from reasons that cannot always be captured or predicted. Cloud computing provides a solution to this problem by offering flexible resources, like containers, which can be quickly and easily scaled according to the current workload needs. Automating this process is a key aspect for the management of modern web software systems, and there is a plethora of methods to implement autonomic management systems. In this work, we review three of these methods, a threshold-based approach, a control-based approach and a model-based approach. We design and run a number of experiments for all three systems with different workloads to evaluate their ability to manage the software system and how well they do so. Our experiments were conducted on the Amazon EC2 cloud with Docker containers.