The design and the deployment of energy-efficient distributed systems is a challenging task, which requires software engineers to consider all the layers of a system, from hardware to software. In particular, monitoring and analyzing the power consumption of a distributed system spanning several-potentially heterogeneous-nodes becomes particularly tedious when aiming at a finer granularity than observing the power consumption of hosting nodes. While the state-of-the-art in software-defined power meters fails to deliver adaptive solutions to offer such service-level perspective and to cope with the diversity of hardware CPU architectures, this paper proposes to automatically learn the power models of the nodes supporting a distributed system, and then to use these inferred power models to better understand how the power consumption of the system's processes is distributed across nodes at runtime. Our solution, named WattsKit, offers a modular toolkit to build software-defined power meters "à la carte", thus dealing with the diversity of user and hardware requirements. Beyond the demonstrated capability of covering a wide diversity of CPU architectures with high accuracy, we illustrate the benefits of adopting software-defined power meters to analyze the power consumption of complex layered and distributed systems. In particular, we illustrate the capability of our approach to monitor the power consumption of a system composed of Docker Swarm, Weave, Elasticsearch, and Apache Zookeeper. Thanks to WattsKit, developers and administrators are now able to identify potential power leaks in their software infrastructure.

Cloud data centers largely rely on virtualization to provision resources and host services across their infrastructure. The scheduling problem has been widely studied and is well understood when the resource requirements and the expected lifetime of services are known beforehand. In contrast, when workloads are not known in advance, effective scheduling of services, and more generally system containers, becomes much more complex. In this paper, we propose GENPACK, a framework for system containers scheduling in cloud data centers that leverages principles from generational garbage collection (GC). It combines runtime monitoring of system containers to learn their requirements and properties, and a scheduler that manages different generations of servers. The population of these generations may vary over time depending on the global load, hence they are subject to being shut down when idle to save energy. We implemented GENPACK and tested it in a dedicated data center, showing that it can be up to 23% more energy-efficient that SWARM's built-in scheduling policies on a real-world trace.

Power estimation of software processes provides critical indicators to drive scheduling or power capping heuristics. State-of-the-art solutions can perform coarse-grained power estimation in virtualized environments, typically treating virtual machines (VMs) as a black box. Yet, VM-based systems are nowadays commonly used to host multiple applications for cost savings and better use of energy by sharing common resources and assets.