Block-structured Adaptive Mesh Refinement (AMR), while essential for improving efficiency in large-scale irregular and dynamic simulations, poses unique optimization challenges. Previous work has identified load imbalance and synchronization overhead as key obstacles to performance, but the deep understanding of complex runtime behavior needed to systematically address them remains elusive. In this paper, we integrate telemetry collection, analysis, and intervention to bridge this understanding gap. Establishing reliable, actionable telemetry required systematic tuning to eliminate cross-stack performance anomalies. Leveraging this foundation we design CPLX, a tunable placement policy balancing compute load and communication locality, improving runtime by up to$\mathbf{2 1. 6 \%}$over optimized baselines. Our experience highlights the empirical nature of placement optimization, requiring theoretical models to be grounded in observed runtime behavior.

The deployment of large-scale data analytics between on-premise and cloud sites, i.e., hybrid clouds, requires careful partitioning of both data and computation to avoid massive networking costs. We present Moirai, a cost-optimization framework that analyzes job accesses and data dependencies and optimizes the placement of both in hybrid clouds. Moirai informs the job scheduler of data location and access predictions, so it can determine where jobs should be executed to minimize data transfer costs. Our optimizer achieves scalability and cost efficiency by exploiting recurring jobs to identify data dependencies and job access characteristics and reduces the search space by excluding data not accessed recently.

Datacenters need to reduce embodied carbon emissions, particularly for flash, which accounts for 40% of embodied carbon in servers. However, decreasing flash’s embodied emissions is challenging due to flash’s limited write endurance, which more than halves with each generation of denser flash. Reducing embodied emissions requires extending flash lifetime, stressing its limited write endurance even further. The legacy Logical Block-Addressable Device (LBAD) interface exacerbates the problem by forcing devices to perform garbage collection, leading to even more writes. Flash-based caches in particular write frequently, limiting the lifetimes and densities of the devices they use. These flash caches illustrate the need to break away from LBAD and switch to the new Write-Read-Erase iNterfaces (WREN) now coming to market. WREN affords applications control over data placement and garbage collection. We present Fairy Wren , 1 a flash cache designed for WREN. Fairy Wren reduces writes by co-designing caching policies and flash garbage collection. Fairy Wren provides a 12.5× write reduction over state-of-the-art LBAD caches. This decrease in writes allows flash devices to last longer, decreasing flash cost by 35% and flash carbon emissions by 33%.

Ingestion of data generated by high-performance scientific applications continues to stress available storage resources. Efficient range-based analyses on this data can be enabled by reordering it on attributes of interest, but require expensive post-processing sorts to realize the query benefits of reordering. In-situ indexing techniques, while write-efficient, are orders of magnitude slower at range queries than sorted indices. Range queries are necessary for analyzing continuous physical attributes and tracking phenomena such as energy bands and wave fronts. We present CARP, a scalable data partitioner for range queries that reorders data in-situ as it is streamed to storage during application I/O. Motivated by our findings that real application distributions tend to be highly skewed and dynamic, CARP dynamically discovers and adapts its data partitions to track these characteristics. As a result, CARP can approximate the query performance of a sort without any ingestion overhead, making it $5 \times$ faster than prior work.

An increasing demand for cross-cloud and cross-region data access is bringing forth challenges related to high data transfer costs and latency. In response, we introduce Macaron, an auto-configuring cache system designed to minimize cost for remote data access. A key insight behind Macaron is that cloud cache size is tied to cost, not hardware limits, shifting the way we think about cache design and eviction policies. Macaron dynamically configures cache size and utilizes a mix of cloud storage types to adapt to workload changes and reduce costs. We demonstrate that Macaron reduces cross-cloud workload costs by 65% and cross-region costs by 67%, mainly by reducing outgoing data transfer and by leveraging object storage alongside DRAM to reduce capacity cost.

Zoned Namespace (ZNS) SSDs are the latest evolution of host-managed flash storage, enabling improved performance at a lower cost-per-byte than traditional block interface (conventional) SSDs. To date, there is no support for arranging these new devices in arrays that offer increased throughput and reliability (RAID). We identify key challenges in designing redundant ZNS SSD arrays, such as managing metadata updates and persisting partial stripe writes in the absence of overwrite support from the device. We present RAIZN, a logical volume manager that exposes a ZNS interface and stripes data and parity across ZNS SSDs. RAIZN provides more stable throughput and lower tail latencies than an mdraid array of conventional SSDs based on the same hardware platform. RAIZN achieves superior performance because device-level garbage collection slows down conventional SSDs. We confirm that the benefits of RAIZN translate to higher layers by adapting the F2FS file system, RocksDB key-value store, and MySQL database to work with ZNS and leverage its benefits by closely controlling garbage collection. Compared to arrays of conventional SSDs experiencing on-device garbage collection, RAIZN leverages the ZNS interface to maintain consistent performance with up to 14× higher throughput and lower tail latency.

Public cloud providers offer a diverse collection of storage types and configurations with different costs and performance SLAs. As a consequence, it is difficult to select the most cost-efficient allocations for storage backends, while satisfying a given workload's performance requirements, when moving data-heavy applications to the cloud. We present Mimir, a tool for automatically finding a cost-efficient virtual storage cluster configuration for a customer's storage workload and performance requirements. Importantly, Mimir considers all block storage types and configurations, and even heterogeneous mixes of them. In our experiments, compared to state-of-the-art approaches that consider only one storage type, Mimir finds configurations that reduce cost by up to 81% for real-application-based key-value store workloads.

High-Performance Computing (HPC) is known for its use of massive concurrency. But it can be challenging for a parallel filesystem's control plane to utilize cores when every client process must globally synchronize and serialize its metadata mutations with those of other clients. We present DeltaFS, a new paradigm for distributed filesystem metadata. DeltaFS allows jobs to self-commit their namespace changes to logs, avoiding the cost of global synchronization. Followup jobs selectively merge logs produced by previous jobs as needed, a principle we term No Ground Truth which allows for efficient data sharing. By avoiding unnecessary synchronization of metadata operations, DeltaFS improves metadata operation throughput up to 98X leveraging parallelism on the nodes where job processes run. This speedup grows as job size increases. DeltaFS enables efficient inter-job communication, reducing overall workflow runtime by significantly improving client metadata operation latency up to 49X and resource usage up to 52X.

Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conserve bandwidth involves resizing or compressing data prior to training. We introduce Progressive Compressed Records (PCRs), a data format that uses compression to reduce the overhead of fetching and transporting data, effectively reducing the training time required to achieve a target accuracy. PCRs deviate from previous storage formats by combining progressive compression with an efficient storage layout to view a single dataset at multiple fidelities---all without adding to the total dataset size. We implement PCRs and evaluate them on a range of datasets, training tasks, and hardware architectures. Our work shows that: (i) the amount of compression a dataset can tolerate exceeds 50% of the original encoding for many DL training tasks; (ii) it is possible to automatically and efficiently select appropriate compression levels for a given task; and (iii) PCRs enable tasks to readily access compressed data at runtime--- utilizing as little as half the training bandwidth and thus potentially doubling training speed.

For a decade, the Ceph distributed file system followed the conventional wisdom of building its storage backend on top of local file systems. This is a preferred choice for most distributed file systems today, because it allows them to benefit from the convenience and maturity of battle-tested code. Ceph’s experience, however, shows that this comes at a high price. First, developing a zero-overhead transaction mechanism is challenging. Second, metadata performance at the local level can significantly affect performance at the distributed level. Third, supporting emerging storage hardware is painstakingly slow. Ceph addressed these issues with BlueStore, a new backend designed to run directly on raw storage devices. In only two years since its inception, BlueStore outperformed previous established backends and is adopted by 70% of users in production. By running in user space and fully controlling the I/O stack, it has enabled space-efficient metadata and data checksums, fast overwrites of erasure-coded data, inline compression, decreased performance variability, and avoided a series of performance pitfalls of local file systems. Finally, it makes the adoption of backward-incompatible storage hardware possible, an important trait in a changing storage landscape that is learning to embrace hardware diversity.

Complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow slower than the core count per die. Computational storage offers a promising solution to this problem by utilizing dedicated compute resources along the storage processing path. We present DeltaFS Indexed Massive Directories (IMDs), a new approach to computational storage. DeltaFS IMDs harvest available (i.e., not dedicated) compute, memory, and network resources on the compute nodes of an application to perform computation on data. We demonstrate the efficiency of DeltaFS IMDs by using them to dynamically reorganize the output of a real-world simulation application across 131,072 CPU cores. DeltaFS IMDs speed up reads by 1,740× while only slightly slowing down the writing of data during simulation I/O for in situ data processing.

We are approaching a point in time when it will be infeasible to catalog and query data after it has been generated. This trend has fueled research on in-situ data processing (i.e. operating on data as it is streamed to storage). One important example of this approach is in-situ data indexing. Prior work has shown the feasibility of indexing at scale as a two-step process. First, one partitions data by key across the CPU cores of a parallel job. Then each core indexes its subset as data is persisted. Online partitioning requires transferring data over the network so that it can be indexed and stored by the core responsible for the data. This approach is becoming increasingly costly as new computing platforms emphasize parallelism instead of individual core performance that is crucial for communication libraries and systems software in general. In addition to indexing, scalable online data partitioning is also useful in other contexts such as load balancing and efficient compression. We present FilterKV, an efficient data management scheme for fast online data partitioning of key-value (KV) pairs. FilterKV reduces the total amount of data sent over the network and to storage. We achieve this by: (a) partitioning pointers to KV pairs instead of the KV pairs themselves and (b) using a compact format to represent and store KV pointers. Results from LANL show that FilterKV can reduce total write slowdown (including partitioning overhead) by up to 3x across 4096 CPU cores.

For a decade, the Ceph distributed file system followed the conventional wisdom of building its storage backend on top of local file systems. This is a preferred choice for most distributed file systems today because it allows them to benefit from the convenience and maturity of battle-tested code. Ceph's experience, however, shows that this comes at a high price. First, developing a zero-overhead transaction mechanism is challenging. Second, metadata performance at the local level can significantly affect performance at the distributed level. Third, supporting emerging storage hardware is painstakingly slow.

Storage systems rely on maintenance tasks, such as backup and layout optimization, to ensure data availability and good performance. These tasks access large amounts of data and can significantly impact foreground applications. We argue that storage maintenance can be performed more efficiently by prioritizing processing of data that is currently cached in memory. Data can be cached either due to other maintenance tasks requesting it previously, or due to overlapping foreground I/O activity.

Latent sector errors (LSEs) are a common hard disk failure mode, where disk sectors become inaccessible while the rest of the disk remains unaffected. To protect against LSEs, commercial storage systems use scrubbers: background processes verifying disk data. The efficiency of different scrubbing algorithms in detecting LSEs has been studied in depth; however, no attempts have been made to evaluate or mitigate the impact of scrubbing on application performance. We provide the first known evaluation of the performance impact of different scrubbing policies in implementation, including guidelines on implementing a scrubber. To lessen this impact, we present an approach giving conclusive answers to the questions: when should scrubbing requests be issued, and at what size, to minimize impact and maximize scrubbing throughput for a given workload. Our approach achieves six times more throughput, and up to three orders of magnitude less slowdown than the default Linux I/O scheduler.

The energy consumed by data centers is starting to make up a significant fraction of the world's energy consumption and carbon emissions. A large fraction of the consumed energy is spent on data center cooling, which has motivated a large body of work on temperature management in data centers. Interestingly, a key aspect of temperature management has not been well understood: controlling the setpoint temperature at which to run a data center's cooling system. Most data centers set their thermostat based on (conservative) suggestions by manufacturers, as there is limited understanding of how higher temperatures will affect the system. At the same time, studies suggest that increasing the temperature setpoint by just one degree could save 2-5% of the energy consumption. This paper provides a multi-faceted study of temperature management in data centers. We use a large collection of field data from different production environments to study the impact of temperature on hardware reliability, including the reliability of the storage subsystem, the memory subsystem and server reliability as a whole. We also use an experimental testbed based on a thermal chamber and a large array of benchmarks to study two other potential issues with higher data center temperatures: the effect on server performance and power. Based on our findings, we make recommendations for temperature management in data centers, that create the potential for saving energy, while limiting negative effects on system reliability and performance.