Storage technique for real-time streaming of layered video

Abstract

Scalable streaming technology has been proposed to effectively support heterogeneous devices with dynamically varying bandwidth. From the file system's point of view, scalable streaming introduces another dimension of complexity in disk scheduling. Most of the existing efforts on multimedia file systems are dedicated to I /O scheduling algorithm and data placement scheme that efficiently guarantee I /O bandwidth. The important underlying assumption in these efforts is that most of the multimedia file accesses are simple playback operations and therefore are sequential. However, this workload characteristic is not valid in scalable streaming environment. In a scalable streaming environment, i.e., when only a subset of imagery is retrieved, the playback does not necessarily coincide with the sequential access on the file. The current file structure and the file system organization leaves much to be desired for supporting scalable streaming service. In this work, we propose a file system scheme, Harmonic Placement to efficiently support Communicated by P. Shenoy. scalable streaming. The basic idea of Harmonic placement is to cluster the frequently accessed layers together to avoid unnecessary disk seeks. The data blocks are partitioned into two sets with respect to the layers: lower layers and upper layers. In Harmonic placement, the data blocks in the lower layers are placed with respect to their frame sequence and the data blocks in the upper layers are clustered according to the layers they belong to. We develop elaborate performance models for three different file system schemes: Progressive placement, Interleaved Placement and Harmonic Placement. We investigate the performance of the file server with different file system schemes. It was found that file system performance is very sensitive to the file organization scheme. When most of the service requests are for low-quality video (e.g., 128 Kbits/s ISDN), Progressive placement scheme supports twice as many sessions as the Interleaved placement scheme. When most of the service requests are for high-quality video (e.g., 1.5 Mbits/s MPEG-2 DVD quality), Interleaved placement can support twice as many requests as Progressive placement. In both cases, Harmonic placement scheme yields the most promising performance.

VSS: A Storage System for Video Analytics [Technical Report]

“…By contrast, quFiles exploits data independence at the granularity of entire videos [48]. Others have explored ondisk layout of video for scalable streaming [29], and systems such as Haystack [5], AWS Serverless Image Handler [1], and VDMS [43] emphasize image and metadata operations.…”

VStore

“…Demands for different resource types may conflict. Towards optimizing these stages for resource efficiency, classic video databases are inadequate [35]: they were designed for human consumers watching videos at 1×-2× speed of video realtime; they are incapable of serving some algorithmic consumers, i.e., operators, processing videos at more than 1000× video realtime. Shifting part of the query to ingestion [24] has important limitations and does not obviate the need for such a video store, as we will show in the paper.…”

“…To decide video formats, VStore is challenged by i) an enormous combinatorial space of video knobs; ii) complex impacts of these knobs and high profiling costs; iii) optimizing for multiple resource types. These challenges were unaddressed: while classic video databases may save video contents in multiple formats, their format choices are oblivious to analytics and often ad hoc [35]; while existing query engines recognize the significance of video formats [32,33,67] and optimize them for query execution, they omit video coding, storage, and retrieval, which are all crucial to retrospective analytics.…”

“…Video systems for human consumers Many multimedia server systems in 90's stored videos on disk arrays in multiple resolutions or in complementary layers, in order serve human clients [17,36]. Since then, Kang et al [35] optimizes placement of on-disk video layers in order to reduce disk seek. Oh et al [45] segments videos into shots, which are easier for humans to browse and search.…”