162 research outputs found
Understanding (Un)Written Contracts of NVMe ZNS Devices with zns-tools
Operational and performance characteristics of flash SSDs have long been
associated with a set of Unwritten Contracts due to their hidden, complex
internals and lack of control from the host software stack. These unwritten
contracts govern how data should be stored, accessed, and garbage collected.
The emergence of Zoned Namespace (ZNS) flash devices with their open and
standardized interface allows us to write these unwritten contracts for the
storage stack. However, even with a standardized storage-host interface, due to
the lack of appropriate end-to-end operational data collection tools, the
quantification and reasoning of such contracts remain a challenge. In this
paper, we propose zns.tools, an open-source framework for end-to-end event and
metadata collection, analysis, and visualization for the ZNS SSDs contract
analysis. We showcase how zns.tools can be used to understand how the
combination of RocksDB with the F2FS file system interacts with the underlying
storage. Our tools are available openly at
\url{https://github.com/stonet-research/zns-tools}
Understanding (Un)Written Contracts of NVMe ZNS Devices with zns-tools
Operational and performance characteristics of flash SSDs have long been associated with a set of Unwritten Contracts due to their hidden, complex internals and lack of control from the host software stack. These unwritten contracts govern how data should be stored, accessed, and garbage collected. The emergence of Zoned Namespace (ZNS) flash devices with their open and standardized interface allows us to write these unwritten contracts for the storage stack. However, even with a standardized storage-host interface, due to the lack of appropriate end-to-end operational data collection tools, the quantification and reasoning of such contracts remain a challenge. In this paper, we propose zns.tools, an open-source framework for end-to-end event and metadata collection, analysis, and visualization for the ZNS SSDs contract analysis. We showcase how zns.tools can be used to understand how the combination of RocksDB with the F2FS file system interacts with the underlying storage. Our tools are available openly at \url{https://github.com/stonet-research/zns-tools}
Corporate influence and the academic computer science discipline. [4: CMU]
Prosopographical work on the four major centers for computer
research in the United States has now been conducted, resulting in big
questions about the independence of, so called, computer science
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy
In the post-Moore's Law era, relying solely on hardware advancements for
automatic performance gains is no longer feasible without increased energy
consumption, due to the end of Dennard scaling. Consequently, computing
accounts for an increasing amount of global energy usage, contradicting the
objective of sustainable computing. The lack of hardware support and the
absence of a standardized, software-centric method for the precise tracing of
energy provenance exacerbates the issue. Aiming to overcome this challenge, we
argue that fine-grained software energy attribution is attainable, even with
limited hardware support. To support our position, we present a thread-level,
NUMA-aware energy attribution method for CPU and DRAM in multi-tenant
environments. The evaluation of our prototype implementation, EnergAt,
demonstrates the validity, effectiveness, and robustness of our theoretical
model, even in the presence of the noisy-neighbor effect. We envisage a
sustainable cloud environment and emphasize the importance of collective
efforts to improve software energy efficiency.Comment: 8 pages, 4 figures; Published in HotCarbon 2023; Artifact available
at https://github.com/HongyuHe/energa
Scalable Storage for Digital Libraries
I propose a storage system optimised for digital libraries. Its key features are its heterogeneous scalability; its integration and exploitation of rich semantic metadata associated with digital objects; its use of a name space; and its aggressive performance optimisation in the digital library domain
On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective
Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service providerâs data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage.
In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a clientâs perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer
Feedback-Based Admission Control for Firm Real-Time Task Allocation with Dynamic Voltage and Frequency Scaling
Feedback-based mechanisms can be employed to monitor the performance of Multiprocessor Systems-on-Chips (MPSoCs) and steer the task execution even if the exact knowledge of the workload is unknown a priori. In particular, traditional proportional-integral controllers can be used with firm real-time tasks to either admit them to the processing cores or reject in order not to violate the timeliness of the already admitted tasks. During periods with a lower computational power demand, dynamic voltage and frequency scaling (DVFS) can be used to reduce the dissipation of energy in the cores while still not violating the tasksâ time constraints. Depending on the workload pattern and weight, platform size and the granularity of DVFS, energy savings can reach even 60% at the cost of a slight performance degradation
Workload-Based ConďŹguration of MEMS-Based Storage Devices for Mobile Systems
Because of its small form factor, high capacity, and expected low cost, MEMS-based storage is a suitable storage technology for mobile systems. However, flash memory may outperform MEMS-based storage in terms of performance, and energy-efficiency. The problem is that MEMS-based storage devices have a large number (i.e., thousands) of heads, and to deliver peak performance, all heads must be deployed simultaneously to access each single sector. Since these devices are mechanical and thus some housekeeping information is needed for each head, this results in a huge capacity loss and increases the energy consumption of MEMS-based storage with respect to flash.
We solve this problem by proposing new techniques to lay out data in MEMS-based storage devices. Data layouts represent optimizations in a design space spanned by three parameters: the number of active heads, sector parallelism, and sector size. We explore this design space and show that by exploiting knowledge of the expected workload, MEMS-based devices can employ all heads, thus delivering peak performance, while decreasing the energy consumption and compromising only a little on the capacity. Our exploration shows that MEMS-based storage is competitive with flash in most cases, and outperforms flash in a few cases
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