uFLIP: Understanding Flash IO Patterns

Does the advent of flash devices constitute a radical change for secondary storage? How should database systems adapt to this new form of secondary storage? Before we can answer these questions, we need to fully understand the performance characteristics of flash devices. More specifically, we want to establish what kind of IOs should be favored (or avoided) when designing algorithms and architectures for flash-based systems. In this paper, we focus on flash IO patterns, that capture relevant distribution of IOs in time and space, and our goal is to quantify their performance. We define uFLIP, a benchmark for measuring the response time of flash IO patterns. We also present a benchmarking methodology which takes into account the particular characteristics of flash devices. Finally, we present the results obtained by measuring eleven flash devices, and derive a set of design hints that should drive the development of flash-based systems on current devices.Comment: CIDR 200

Unblinding the OS to Optimize User-Perceived Flash SSD Latency

In this paper, we present a flash solid-state drive (SSD) optimization that provides hints of SSD internal behaviors, such as device I/O time and buffer activities, to the OS in order to mitigate the impact of I/O completion scheduling delays. The hints enable the OS to make reliable latency predictions of each I/O request so that the OS can make accurate scheduling decisions when to yield or block (busy wait) the CPU, ultimately improving user-perceived I/O performance. This was achieved by implementing latency predictors supported with an SSD I/O behavior tracker within the SSD that tracks I/O behavior at the level of internal resources, such as DRAM buffers or NAND chips. Evaluations with an SSD prototype based on a Xilinx Zynq-7000 FPGA and MLC flash chips showed that our optimizations enabled the OS to mask the scheduling delays without severely impacting system parallelism compared to prior I/O completion methods.We would like to thank the anonymous USENIX HotStorage reviewers. This research was supported by NextGeneration Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Plannig (2015M 3C 4A7065646).OAIID:RECH_ACHV_DSTSH_NO:A201608543RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A002712CITE_RATE:DEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:CONFIRM:

I/O Schedulers for Proportionality and Stability on Flash-Based SSDs in Multi-Tenant Environments

The use of flash based Solid State Drives (SSDs) has expanded rapidly into the cloud computing environment. In cloud computing, ensuring the service level objective (SLO) of each server is the major criterion in designing a system. In particular, eliminating performance interference among virtual machines (VMs) on shared storage is a key challenge. However, studies on SSD performance to guarantee SLO in such environments are limited. In this paper, we present analysis of I/O behavior for a shared SSD as storage in terms of proportionality and stability. We show that performance SLOs of SSD based storage systems being shared by VMs or tasks are not satisfactory. We present and analyze the reasons behind the unexpected behavior through examining the components of SSDs such as channels, DRAM buffer, and Native Command Queuing (NCQ). We introduce two novel SSD-aware host level I/O schedulers on Linux, called A & x002B;CFQ and H & x002B;BFQ, based on our analysis and findings. Through experiments on Linux, we analyze I/O proportionality and stability in multi-tenant environments. In addition, through experiments using real workloads, we analyze the performance interference between workloads on a shared SSD. We then show that the proposed I/O schedulers almost eliminate the interference effect seen in CFQ and BFQ, while still providing I/O proportionality and stability for various I/O weighted scenarios

Signal Processing for Caching Networks and Non-volatile Memories

The recent information explosion has created a pressing need for faster and more reliable data storage and transmission schemes. This thesis focuses on two systems: caching networks and non-volatile storage systems. It proposes network protocols to improve the efficiency of information delivery and signal processing schemes to reduce errors at the physical layer as well. This thesis first investigates caching and delivery strategies for content delivery networks. Caching has been investigated as a useful technique to reduce the network burden by prefetching some contents during o˙-peak hours. Coded caching [1] proposed by Maddah-Ali and Niesen is the foundation of our algorithms and it has been shown to be a useful technique which can reduce peak traffic rates by encoding transmissions so that different users can extract different information from the same packet. Content delivery networks store information distributed across multiple servers, so as to balance the load and avoid unrecoverable losses in case of node or disk failures. On one hand, distributed storage limits the capability of combining content from different servers into a single message, causing performance losses in coded caching schemes. But, on the other hand, the inherent redundancy existing in distributed storage systems can be used to improve the performance of those schemes through parallelism. This thesis proposes a scheme combining distributed storage of the content in multiple servers and an efficient coded caching algorithm for delivery to the users. This scheme is shown to reduce the peak transmission rate below that of state-of-the-art algorithms