Performance of real-time data scheduling heuristics under data replacement policies and access patterns in data grids

A variety of real-time data scheduling heuristics were proposed for distributed, data intensive real-time applications running on a distributed computing system. The proposed heuristics are used to produce real-time data dissemination schedules for the applications' requests for the data stored on the machines in the system. However, how these real-time data scheduling heuristics will perform for different data replacement policies and data access patterns is a question left unanswered. Based on this motivation, in this study, the performance of the two real-time data scheduling heuristics, namely the Full Path Heuristic and the Extended Partial Path Heuristic, are evaluated under different data replacement policies and data access patterns. A detailed set of simulation studies are presented to reveal how these algorithms are affected by the changes in the data replacement policy and data access pattern as well as the other system parameters of interest.