Temporal Consistency Maintenance Upon Partitioned Multiprocessor Platforms

Abstract

Maintaining timeliness and data freshness for real-time data objects has long been recognized as an important problem in real-time database research. Despite years of active research, most of the past work focuses on uniprocessor systems. In this paper, we study the workload-aware temporal consistency maintenance problem upon multiprocessor platforms. We consider the problem of how to partition a set of update transactions to $m \ge 2$ processors to maintain the temporal consistency of real-time data objects under both earliest deadline first (EDF) and deadline monotonic (DM) scheduling in each processor, while minimizing the total workload on $m$ processors. Firstly, we only consider the feasibility aspect of the problem by proposing two polynomial time partitioning schemes, Temporal Consistency Partitioning under EDF ( $\mathsf TCP_{\mathsf{EDF}}$ ) and Temporal Consistency Partitioning under DM ( $\mathsf TCP_{\mathsf{DM}}$ ), and formally showing that the resource augmentation bounds of both $\mathsf TCP_{\mathsf{EDF}}$ and $\mathsf TCP_{\mathsf{DM}}$ are $({3 - \frac{1}{m}})$ . Secondly, we address the partition problem globally by proposing a polynomial time heuristic, Density factor Balancing Fit ( $\mathsf{DBF}$ ), where density factor balancing plays a major role in producing workload-efficient partitionings. Finally, we evaluate the feasibility and workload performances of $\mathsf{DBF}$ versus other heuristics with comparable quality experimentally.