Resource scheduling in enhanced pay-per-view continuous media databases

The enhanced pay-per-view (EPPV) model for providing continuous-media-on-demand (CMOD) services associates with each continuous media clip a display frequency that dependson the clip’s popularity. The aim is to increase the number of clients that can be serviced concurrently beyond the capacity limitations of available resources, while guaranteeing a constraint on the response time. This is achieved by sharing periodic continuous media streams among multiple clients. In this paper, we provide a comprehensive study of the resource scheduling problems associated with supporting EPPV for continuous media clips with (possibly) different display rates, frequencies, and lengths. Our main objective is to maximize the amount of disk bandwidth that is effectively scheduled under the given data layout and storage constraints. This formulation gives rise to NP-hard combinatorial optimization problems that fall within the realm of hard real-time scheduling theory. Given the intractability of the problems, we propose novel heuristic solutions with polynomial-time complexity. Preliminary results from an experimental evaluation of the proposed schemes are also presented.

Resource scheduling in enhanced pay-per-view continuous media databases

Summarization: The enhanced pay-per-view (EPPV) model for providing continuous-media-on-demand (CMOD) services associates with each continuous media clip a display frequency that depends on the clip’s popularity. The aim is to increase the number of clients that can be serviced concurrently beyond the capacity limitations of available resources, while guaranteeing a constraint on the response time. This is achieved by sharing periodic continuous media streams among multiple clients. In this paper, we provide a comprehensive study of the resource scheduling problems associated with supporting EPPV for continuous media clips with (possibly) different display rates, frequencies, and lengths. Our main objective is to maximize the amount of disk bandwidth that is effectively scheduled under the given data layout and storage constraints. This formulation gives rise to NP-hard combinatorial optimization problems that fall within the realm of hard real-time scheduling theory. Given the intractability of the problems, we propose novel heuristic solutions with polynomial-time complexity. Preliminary results from an experimental evaluation of the proposed schemes are also presentedPresented on