Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.

Microservices are one of the most recommended architectural styles for distributed applications that support independent development and deployment, enable rapid release, and are highly scalable and polyglot. Many well-established patterns and best practices have been documented in the literature. As there are many such guidances, they have numerous interdependencies, and system designs must adhere to many other architecture constraints, too, implementations do not always conform to those guidances. In complex or large systems, it can be hard and tedious to spot violations. Our work aims to offer automated support for architecting during the continuous evolution of microservice-based systems. More specifically we aim to provide the foundations for an automated approach for architecture reconstruction, assessing conformance to patterns and practices specific for microservice architectures, and detect possible violations. Based on this, we provide actionable options to architects for improving architecture conformance as part of a continuous feedback loop. That is, our goal is to support architecting in the context of continuous delivery practices, where architecture violations are continuously analyzed and fix options are continuously suggested.

Edge computing describes a paradigm for combining computational resources at the edge of the network with the cloud. Even though complementing the cloud with these resources provides benefits, e.g., low latency, it also introduces new challenges to the operational staff. Such challenges can be: deciding if the applications should be placed in the cloud or at the edge, and monitoring them at runtime to ensure that all the application requirements are met. This becomes more challenging when using microservices due to the complexity of the resulting placement problem. To mitigate such concerns, we introduce an automatic deployment framework along with a prototype implementation, called D-DAD. This framework provides a transparent (to the operational staff) way to deploy applications with respect to all their requirements-including the non-functional-using mechanisms for monitoring and adapting the deployments to the available resources in a cloud-edge environment. For evaluating our framework, we provide results from a series of experiments which show how the adaptation mechanism meets the application requirements, including a ~90% reduction of CPU utilization violations, compared to using only the local resources.

In this work we advance the understanding of software eco-systems research by examining the structure and evolution of the R statistical computing open-source ecosystem. Our research attempts to shed light on the following intriguing question: what makes software ecosystems successful? The approach we follow is to perform a quantitative analysis of the R ecosystem. R is a well-established and popular ecosystem, whose community and marketplace are steadily growing. We assess and quantify the ecosystem throughout its history, and derive metrics on its core software components, the marketplace as well as its community. We use our insights to make observations that are applicable to ecosystems in general, validate existing theories from the literature, and propose a predictive model for the evolution of software packages. Our results show that the success of the ecosystem relies on a strong commitment by a small core of users who support a large and growing community.