Title: Fast-FFA: a fast online scheduling approach for big data stream computing with future features-aware
Authors: Dawei Sun; Hao Tang
Addresses: School of Information Engineering, China University of Geosciences, 100083, Beijing, China ' School of Information Engineering, China University of Geosciences, 100083, Beijing, China
Abstract: Awareness of future features is more important than that of historical features for online scheduling in a big data stream computing environment. In this paper, a fast future feature-aware online scheduling approach fast-FFA is put forward, exhibiting the following contributions; 1) Modelling the online resource scheduling from viewpoints of user and data centre, considering multi-dimensional features of online data stream and quantitating preferences and utilities of each dimension. 2) Obtaining future features from historical features of multidimensional data stream with a hybrid particle swarm optimisation, back propagation (PSO-BP) algorithm and optimising online scheduling with an immune clonal algorithm. 3) Evaluating fast-FFA and balancing both fast future feature awareness and acceptable accuracy objectives. Experimental results demonstrate that the proposed fast-FFA approach has high potential as the approach provides significant system efficiency enhancements in online big data environments.
Keywords: big data computing; data stream; online scheduling; feature awareness; intelligent optimisation.
DOI: 10.1504/IJBIC.2017.086717
International Journal of Bio-Inspired Computation, 2017 Vol.10 No.3, pp.205 - 217
Received: 15 May 2017
Accepted: 10 Jun 2017
Published online: 21 Sep 2017 *