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This paper focuses on the algorithms design of heterogeneous green scheduling for energy conservation and emission reduction in cloud computing. In essence, the real time, dynamic and complexity of heterogeneous scheduling require higher algorithm performance; however, the swarm intelligent algorithms although with some improvements, still exist big imbalances between local exploration and macro development or between route (solution) diversity and faster convergence. In this paper, a greener heterogeneous scheduling algorithm via blending pattern of particle swarm computing intelligence and geometric Brownian motion, is proposed, based on our earlier theoretical breakthroughs on G-Brownian motion and through a series of mathematical derivations or proofs; furthermore, in order for suitable for the hybrid processor architecture of the scheduling management server, the algorithm is designed in parallel with deep fusion of coarse-grained and master-slave models. A large number of experimental results are given. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with lower improvement factors for small inconsistent instances.

A Greener Heterogeneous Scheduling Algorithm via Blending Pattern of Particle Swarm Computing Intelligence and Geometric Brownian Motion