Recent technological advances in the development of flash-memory based devices have consolidated their leadership position as the preferred storage media in the embedded systems market and opened new vistas for deployment in enterprise-scale storage systems. Unlike hard disks, flash devices are free from any mechanical moving parts, have no seek or rotational delays and consume lower power. However, the internal idiosyncrasies of flash technology make its performance highly dependent on workload characteristics. The poor performance of random writes has been a cause of major concern, which needs to be addressed to better utilize the potential of flash in enterprise-scale environments. We examine one of the important causes of this poor performance: the design of the Flash Translation Layer (FTL), which performs the virtual-to-physical address translations and hides the erase-before-write characteristics of flash. We propose a complete paradigm shift in the design of the core FTL engine from the existing techniques with our Demand-based Flash Translation Layer (DFTL), which selectively caches page-level address mappings. We develop a flash simulation framework called FlashSim. Our experimental evaluation with realistic enterprise-scale workloads endorses the utility of DFTL in enterprise-scale storage systems by demonstrating: (i) improved performance, (ii) reduced garbage collection overhead and (iii) better overload behavior compared to state-of-the-art FTL schemes. For example, a predominantly random-write dominant I/O trace from an OLTP application running at a large financial institution shows a 78% improvement in average response time (due to a 3-fold reduction in operations of the garbage collector), compared to a state-of-the-art FTL scheme. Even for the well-known read-dominant TPC-H benchmark, for which DFTL introduces additional overheads, we improve system response time by 56%.

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DFTL: a flash translation layer employing demand-based selective caching of page-level address mappings

In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundant data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client.

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Serverless Network File Systems

Efficient and effective buffering of disk blocks in main memory is critical for better file system performance due to a wide speed gap between main memory and hard disks. In such a buffering system, one of the most important design decisions is the block replacement policy that determines which disk block to replace when the buffer is full. In this paper, we show that there exists a spectrum of block replacement policies that subsumes the two seemingly unrelated and independent Least Recently Used (LRU) and Least Frequently Used (LFU) policies. The spectrum is called the LRFU (Least Recently/Frequently Used) policy and is formed by how much more weight we give to the recent history than to the older history. We also show that there is a spectrum of implementations of the LRFU that again subsumes the LRU and LFU implementations. This spectrum is again dictated by how much weight is given to recent and older histories and the time complexity of the implementations lies between O(1) (the time complexity of LRU) and {\rm O}(\log_2 n) (the time complexity of LFU), where n is the number of blocks in the buffer. Experimental results from trace-driven simulations show that the performance of the LRFU is at least competitive with that of previously known policies for the workloads we considered.

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LRFU: a spectrum of policies that subsumes the least recently used and least frequently used policies

DiskSim is an efficient, accurate and highly-configurable disk system simulator developed to support research into various aspects of storage subsystem architecture. It includes modules that simulate disks, intermediate controllers, buses, device drivers, request schedulers, disk block caches, and disk array data organizations. In particular, the disk drive module simulates modern disk drives in great detail and has been carefully validated against several production disks (with accuracy that exceeds any previously reported simulator). It also includes a MEMS-based storage device module. This manual describes how to configure and use DiskSim, which has been made publicly available with the hope of advancing the state-of-the-art in disk system performance evaluation in the research community. The manual also briefly describes DiskSim’s internal structure and various validation results.

The DiskSim Simulation Environment Version 4.0 Reference Manual (CMU-PDL-08-101)

In this paper, we analyze access traces to a Web proxy, looking at statistical parameters to be used in the design of a replacement policy for documents held in the cache. In the first part of this paper, we present a number of properties of the lifetime and statistics of access to documents, derived from two large trace sets coming from very different proxies and spanning over time intervals of up to five months. In the second part, we propose a novel replacement policy, called LRV, which selects for replacement the document with the lowest relative value among those in cache. In LRV, the value of a document is computed adaptively based on information readily available to the proxy server. The algorithm has no hardwired constants, and the computations associated with the replacement policy require only a small constant time. We show how LRV outperforms least recently used (LRU) and other policies and can significantly improve the performance of the cache, especially for a small one.

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Replacement policies for a proxy cache

I/O subsystem manufacturers attempt to reduce latency by increasing disk rotation speeds, incorporating more intelligent disk scheduling algorithms, increasing I/O bus speed, using solid-state disks, and implementing caches at various places in the I/O stream. In this article, we examine the use of caching as a means to increase system response time and improve the data throughput of the disk subsystem. Caching can help to alleviate I/O subsystem bottlenecks caused by mechanical latencies. This article describes a caching strategy that offers the performance of caches twice its size. After explaining some basic caching issues, we examine some popular caching strategies and cache replacement algorithms, as well as the advantages and disadvantages of caching at different levels of the computer system hierarchy. Finally, we investigate the performance of three cache replacement algorithms: random replacement (RR), least recently used (LRU), and a frequency-based variation of LRU known as segmented LRU (SLRU). >

J.S. Love

Papers

Caching strategies to improve disk system performance