Power consumption is a major concern in data centers and HPC applications, with GPUs typically accounting for more than half of system power usage. While accurate power measurement tools are crucial for optimizing the energy efficiency of (GPU) applications, both built-in power sensors as well as state-of-the-art power meters often lack the accuracy and temporal granularity needed, or are impractical to use. Released as open hardware, firmware, and software, PowerSensor3 provides a cost-effective solution for evaluating energy efficiency, enabling advancements in sustainable computing. The toolkit consists of a baseboard with a variety of sensor modules accompanied by host libraries with C++ and Python bindings. PowerSensor3 enables real-time power measurements of SoC boards and PCIe cards, including GPUs, FPGAs, NICs, SSDs, and domain-specific AI and ML accelerators. Additionally, it provides significant improvements over previous tools, such as a robust and modular design, current sensors resistant to external interference, simplified calibration, and a sampling rate up to 20 kHz, which is essential to identify GPU behavior at high temporal granularity. This work describes the toolkit design, evaluates its performance characteristics, and shows several use cases (GPUs, NVIDIA Jetson AGX Orin, and SSD), demonstrating PowerSensor3's potential to significantly enhance energy efficiency in modern computing environments.

Resource managers such as Kubernetes are rapidly evolving to support low-latency and scalable computing paradigms such as serverless and granular computing. As a result, Kubernetes supports dozens of workload deployment models and exposes roughly 1,600 configuration parameters. Previous work has shown that parameter tuning can significantly improve Kubernetes' performance, but identifying which parameters impact performance and should be tuned remains challenging. To help users optimize their Kubernetes deployments, we present Columbo, an offline reasoning framework to detect and resolve performance bottlenecks using configuration parameters. We study Kubernetes and define its workload deployment pipeline of 6 stages and 26 steps. To detect bottlenecks, Columbo uses an analytical model to predict the best-case deployment time of a workload per pipeline stage and compares it to empirical data from a novel benchmark suite. Columbo then uses a rule-based methodology to recommend parameter updates based on the detected bottleneck, deployed workload, and mapping of configurations to pipeline stages. We demonstrate that Columbo reduces workload deployment time across its benchmark suite by 28% on average and 79% at most. We report a total execution time decrease of 17% for data processing with Spark and up to 20% for serverless workflows with OpenWhisk. Columbo is open-source and available at https://github.com/atlarge-research/continuum/tree/columbo.

We live in a data-centric world where many fundamental shifts in our daily lives are powered by Big Data. To meet the performance, cost, and energy demands of modern Big Data systems, there have been significant technological and engineering breakthroughs in the field of storage systems with novel hardware innovations and software architectures. Nevertheless, a typical computer science student still associates data storage solely with the technology of hard disk drives (HDD), which was invented six decades ago. One key reason for this association is the lack of courses on modern storage systems in computer science education curricula. In this paper, we make a concerted effort to summarize the state of storage systems education across universities, popular textbooks, and policies (ACM/IEEE). We make a case that the storage systems should have its own home course in educational curricula. We report on our experience of designing and offering one such course at the Vrije Universiteit, Amsterdam over the past four years. We further contribute to the educational material in this direction by making the course lectures, video recordings, assignments, and grading framework freely and openly accessible at https://atlarge-research.com/courses/storage-systems-vu.

Flash-based storage is known to suffer from performance unpredictability due to interference between host-issued I/O and device-side I/O management. SSDs with data placement capabilities, such as Zoned Namespaces (ZNS) and Flexible Data Placement (FDP), expose selective device-side I/O management operations to the host to provide predictable performance. In this paper, we demonstrate that these host-issued I/O management operations lead to performance interference with host-issued I/O. Indeed, we find that the I/O management operations introduced by ZNS and FDP create I/O interference, leading to significant performance losses. Despite the performance implications, we observe that ZNS research frequently uses emulators (over 20 recently published papers), but no emulator currently has function-realistic models for I/O management. To address this gap, we identify ten ZNS I/O management designs, explain how they interfere with I/O, and introduce ConfZNS++, a function-realistic emulator with native I/O management support, providing future research with the capability to explore these designs. Additionally, we introduce two actionable host-managed solutions to reduce ZNS management interference: ZINC, an I/O scheduler prioritizing I/O over I/O management, and the softfinish operation, a host-managed implementation of the finish operation. In our experiments, ZINC reduces reset interference by 56.9%, and softfinish reduces finish interference by 50.7%.

Flash SSDs have become the de-facto choice to deliver high I/O performance to modern data-intensive workloads. These workloads are often deployed in the cloud, where multiple tenants share access to flash-based SSDs. Cloud providers use various techniques, including I/O schedulers available in the Linux kernel, such as \textitBFQ, Multiqueue-Deadline ~(\mqddl ), and \kyber, to ensure certain performance qualities (i.e., service-level agreements, SLAs). Though designed for fast NVMe SSDs, there has not been a systematic study of these schedulers for modern, high-performance SSDs with their unique challenges. In this paper. we systematically characterize the performance, overheads, and scalability properties of Linux storage schedulers on NVMe SSDs with millions of I/O operations/s. We report 23 observations and 5 key findings that indicate that (i) CPU performance is the primary bottleneck with the Linux storage stack with high-performance NVMe SSDs; (ii) Linux I/O schedulers can introduce 63.4% performance overheads with NVMe SSDs; (iii) \kyber and q can deliver 99.3% lower P99 latency than \none or \mqddl schedulers in the presence of multiple interfering workloads. We open-source the scripts and datasets of this work at: https://zenodo.org/records/10599514.

The recent emergence of NVMe flash devices with Zoned Namespace support, ZNS SSDs, represents a significant new advancement in flash storage. ZNS SSDs introduce a new storage abstraction of append-only zones with a set of new I/O (i.e., append) and management (zone state machine transition) commands. With the new abstraction and commands, ZNS SSDs offer more control to the host software stack than a non-zoned SSD for flash management, which is known to be complex (because of garbage collection, scheduling, block allocation, parallelism management, overprovisioning). ZNS SSDs are, consequently, gaining adoption in a variety of applications (e.g., file systems, key-value stores, and databases), particularly latency-sensitive big-data applications. Despite this enthusiasm, there has yet to be a systematic characterization of ZNS SSD performance with its zoned storage model abstractions and I/O operations. This work addresses this crucial shortcoming. We report on the performance features of a commercially available ZNS SSD (13 key observations), explain how these features can be incorporated into publicly available state-of-the-art ZNS emulators, and recommend guidelines for ZNS SSD application developers. All artifacts (code and data sets) of this study are publicly available at https://github.com/stonet-research/NVMeBenchmarks.