Emerging byte-addressable, non-volatile memory like phase-change memory, STT-MRAM, etc. brings persistence at latencies within an order of magnitude of DRAM, thereby motivating their inclusion on the memory bus. According to some recent work on NVM, traditional file systems are ineffective and sub-optimal in accessing data from this low latency media. However, there exists no systematic performance study across different file systems and their various configurations validating this point. In this work, we evaluate the performance of various legacy Linux file systems under various real world workloads on non-volatile memory (NVM) simulated using ramdisk and compare it against NVM optimized file system - PMFS. Our results show that while the default file system configurations are mostly sub-optimal for NVM, these legacy file systems can be tuned using mount and format options to achieve performance that is comparable to NVM-aware file system such as PMFS. Our experiments show that the performance difference between PMFS and ext2/ext3 with execute-in-place (XIP) option is around 5% for many workloads (TPCC and YCSB). Furthermore, based on the learning from our performance study, we present few key file system features such as in-place update layout with XIP, and parallel metadata and data allocations, etc. that could be leveraged by file system designers to improve performance of both legacy and new file systems for NVM.

In the past storage vendors used different types of storage depending upon the type of workload. For example, they used Solid State Drives (SSDs) or FC hard disks (HDD) for online transaction, while SATA for archival type workloads. However, recently many storage vendors are designing hybrid SSD/HDD based systems that can satisfy multiple service level objectives (SLOs) of different workloads all placed together in one storage box, at better cost points. The combination is achieved by using SSDs as a read-write cache while HDD as a permanent store. In this paper we present an SLO based resource management algorithm that controls the amount of SSD given to a particular workload. This algorithm solves following problems: 1) it ensures that workloads do not interfere with each other 2) it ensure that we do not overprovision (cost wise) the amount of SSD allocated to a workload to satisfy its SLO (latency requirement) and 3) dynamically adjust SSD allocated in light of changing workload characteristics (i.e., provide only required amount of SSD). We have implemented our algorithm in a prototype Hybrid Store, and have tested its efficacy using many real workloads. Our algorithm satisfies latency SLOs almost always by utilizing close to optimal amount of SSD and saving 6-50% of SSD space compared to the naïve algorithm.

Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system's energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component's impact separately. Various factors like workloads, hardware configurations, and software configurations impact the performance and energy efficiency of the system. This article evaluates the file system's impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and fileserver, on two different hardware configurations. The file system design, implementation, and available features have a significant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. In this article we show that a careful matching of expected workloads and hardware configuration to a single software configuration—the file system—can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.

Data compression has been claimed to be an attractive solution to save energy consumption in high-end servers and data centers. However, there has not been a study to explore this. In this paper, we present a comprehensive evaluation of energy consumption for various file compression techniques implemented in software. We apply various compression tools available on Linux to a variety of data files, and we try them on server class and workstation class systems. We compare their energy and performance results against raw reads and writes. Our results reveal that software based data compression cannot be considered as a universal solution to reduce energy consumption. Various factors like the type of the data file, the compression tool being used, the read-to-write ratio of the workload, and the hardware configuration of the system impact the efficacy of this technique. In some cases, however, we found compression to save substantial energy and improve performance.