Operating large scale and feature-rich applications is becoming increasingly complex as engineers need to deploy highly configurable software releases on distributed cloud stacks while managing ever-shorter production cycles. Although recent proposals attempt to streamline cloud resources orchestration, there is still a significant challenge in making such solutions generalize to unseen cloud stacks. In other words, the behavior of application-specific Key Performance Indicators (KPIs) and resource configurations, crafted for specific stacks, may differ on heterogeneous deployments, requiring time-consuming policy adjustments. We introduce IMITA, a system that leverages imitation learning to create models by imitating an expert behavior that can be generalized seamlessly to new cloud stacks. To make a generalized model, IMITA maps expert actions taken based on the application KPI space to the space of resource utilization metrics that are universally available in cloud platforms. This mapping enables the model to trigger actions, mimicking expert behavior, upon the occurrence of similar re-source utilization footprints across deployments. We demonstrate IMITA by learning to scale-out Cassandra deployments with diverse configurations and workloads. Our results show IMITA can replicate expert actions across deployments and extrapolate to unseen environments by achieving 50 - 94% fewer false positives actions than traditional threshold-based policies while still adhering to Service-Level Objectives (SLO) and avoiding under-provisioning of resources. Moreover, since collecting data in clouds is costly, IMITA gathers data only for representative configurations to train the imitator model. This approach reduces the size of the collected data to 50%.

Cloud-native systems and application software platforms are becoming increasingly complex, and, ideally, they are expected to be quick to launch, elastic, portable across different cloud environments and easily managed. However, as cloud applications increase in complexity, so do the resultant challenges in configuring such applications, and orchestrating the deployment of their constituent services on potentially different cloud operating systems and environments. This paper presents a new orchestration system called Karamel that addresses these challenges by providing a cloud-independent orchestration service for deploying and configuring cloud applications and platforms across different environments. In Karamel, we model configuration routines with their dependencies in composable modules, and we achieve a high level of configuration/deployment parallelism by using techniques such as DAG traversal control logic, dataflow variable binding, and parallel actuation. In Karamel, complex distributed deployments are specified declaratively in a compact YAML syntax, and cluster definitions can be validated using an external artifact repository (GitHub).

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Across many fields of science, primary data sets like sensor read-outs, time series, and genomic sequences are analyzed by complex chains of specialized tools and scripts exchanging intermediate results in domain-specific file formats. Scientific workflow management systems (SWfMSs) support the development and execution of these tool chains by providing workflow specification languages, graphical editors, fault-tolerant execution engines, etc. However, many SWfMSs are not prepared to handle large data sets because of inadequate support for distributed computing. On the other hand, most SWfMSs that do support distributed computing only allow static task execution orders. We present SAASFEE, a SWfMS which runs arbitrarily complex workflows on Hadoop YARN. Workflows are specified in Cuneiform, a functional workflow language focusing on parallelization and easy integration of existing software. Cuneiform workflows are executed on Hi-WAY, a higher-level scheduler for running workflows on YARN. Distinct features of SAASFEE are the ability to execute iterative workflows, an adaptive task scheduler, re-executable provenance traces, and compatibility to selected other workflow systems. In the demonstration, we present all components of SAASFEE using real-life workflows from the field of genomics.