Data Engineering

A growing number of applications need access to video data stored in digital form on secondary storage devices (e.g., video-on-demand, multimedia messaging). As a result, video servers that are responsible for the storage and retrieval, at fixed rates, of hundreds of videos from disks are becoming increasingly important. Since video data tends to be voluminous, several disks are usually used in order to store the videos. A challenge is to devise schemes for the storage and retrieval of videos that distribute the workload evenly across disks, reduce the cost of the server and at the same time, provide good response times to client requests for video data. In this paper, we present schemes that are based on striping videos (fine-grained as well as coarse-grained) across disks in order to effectively utilize disk bandwidth. For the schemes, we show how an optimal-cost server architecture can be determined if data for a certain pre-specified number of videos is to be concurrently retrieved..

Data Engineering

A growing number of applications need access to video data stored in digital form on secondary storage devices (e.g., video-on-demand, multimedia messaging). As a result, video servers that are responsible for the storage and retrieval, at fixed rates, of hundreds of videos from disks are becoming increasingly important. Since video data tends to be voluminous, several disks are usually used in order to store the videos. A challenge is to devise schemes for the storage and retrieval of videos that distribute the workload evenly across disks, reduce the cost of the server and at the same time, provide good response times to client requests for video data. In this paper, we present schemes that are based on striping videos (fine-grained as well as coarse-grained) across disks in order to effectively utilize disk bandwidth. For the schemes, we show how an optimal-cost server architecture can be determined if data for a certain pre-specified number of videos is to be concurrently retrieved..