QoS management in real-time spatial big data using a feedback control scheduling

Abstract

Heterogeneous real-time spatial data management is a very active research domain nowadays. We are talking about a real-time spatial big data that process a large amount of heterogeneous data accessed simultaneously by two types of transactions update transactions and user transactions. In these applications, it is desirable to execute transactions within their deadlines using a real-time spatial data. But the real-time spatial big data can be overloaded and many transactions may miss their deadlines, or real-time spatial data can be violated. To address these problems, we proposed, as a first contribution, a new architecture called feedback control scheduling architecture for real-time spatial big data (FCSA-RTSBD) (Hamdi et al., 2015). Then, we propose, as a second contribution, two-shadow speculative concurrency control (SCC-2S) with priority and imprecise computation (SCC-2S-P-IC). Finally, a simulation study is shown to prove that our contributions can achieve a significant performance improvement using the TPC-DS (TPC, 2014) benchmark.