Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

Adaptive Prefetching for Device-Independent File I/O

Device independent I/O has been a holy grail to operating system designers since the early days of UNIX. Unfortunately, existing operating systems fall short of this goal for multimedia applications. Techniques such as caching and sequential read-ahead can help mask I/O latency in some cases, but in others they increase latency and add substantial jitter. Multimedia applications, such as video players, are sensitive to vagaries in performance since I/O latency and jitter affect the quality of presentation. Our solution uses adaptive prefetching to reduce both latency and jitter. Applications submit file access plans to the prefetcher, which then generates I/O requests to the operating system and manages the buffer cache to isolate the application from variations in device performance. Our experiments show device independence can be achieved: an MPEG video player sees the same latency when reading from a local disk or an NFS server. Moreover, our approach reduces jitter substantially

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 provider’s 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 client’s 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

Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

Prefetching and clustering techniques for network based storage.

The usage of network-based applications is increasing, as network speeds increase, and the use of streaming applications, e.g BBC iPlayer, YouTube etc., running over network infrastructure is becoming commonplace. These applications access data sequentially. However, as processor speeds and the amount of memory available increase, the rate at which streaming applications access data is now faster than the rate at which the blocks can be fetched consecutively from network storage. In addition to sequential access, the system also needs to promptly satisfy demand misses in order for applications to continue their execution. This thesis proposes a design to provide Quality-Of-Service (QoS) for streaming applications (sequential accesses) and demand misses, such that, streaming applications can run without jitter (once they are started) and demand misses can be satisfied in reasonable time using network storage. To implement the proposed design in real time, the thesis presents an analytical model to estimate the average time taken to service a demand miss. Further, it defines and explores the operational space where the proposed QoS could be provided. Using database techniques, this region is then encapsulated into an autonomous algorithm which is verified using simulation. Finally, a prototype Experimental File System (EFS) is designed and implemented to test the algorithm on a real test-bed

3PO: Programmed Far-Memory Prefetching for Oblivious Applications

Using memory located on remote machines, or far memory, as a swap space is a promising approach to meet the increasing memory demands of modern datacenter applications. Operating systems have long relied on prefetchers to mask the increased latency of fetching pages from swap space to main memory. Unfortunately, with traditional prefetching heuristics, performance still degrades when applications use far memory. In this paper we propose a new prefetching technique for far-memory applications. We focus our efforts on memory-intensive, oblivious applications whose memory access patterns are independent of their inputs, such as matrix multiplication. For this class of applications we observe that we can perfectly prefetch pages without relying on heuristics. However, prefetching perfectly without requiring significant application modifications is challenging. In this paper we describe the design and implementation of 3PO, a system that provides pre-planned prefetching for general oblivious applications. We demonstrate that 3PO can accelerate applications, e.g., running them 30-150% faster than with Linux's prefetcher with 20% local memory. We also use 3PO to understand the fundamental software overheads of prefetching in a paging-based system, and the minimum performance penalty that they impose when we run applications under constrained local memory.Comment: 14 page