Bio-inspired cost-effective access to big data

With the rapid proliferation of services and cloud computing, Big Data has become a significant phenomenon across many scientific disciplines and sectors of society, wherever huge amounts of data are generated and processed daily. End users will always seek higher-quality data access at lower prices. This demand poses challenges to service composers, service providers and data providers, who should maintain their service and data provision as cost-effectively as possible. This paper will apply bio-inspired approaches to achieving equilibrium among the otherwise competitive stakeholders. In addition to novel models of cost for Big Data provision, bio-inspired algorithms will be developed and validated for dynamic optimisation. Furthermore, the optimised algorithms will also be applied in the data-mining research on the Alpha Magnetic Spectrometer (AMS) experiment, which is aiming to find dark matter in the universe. This experiment typically receives 200G and generates 700G data daily.