Rubing Duan , R. Duan is with Institute of High Performance Computing, A*STAR, Singapore.(email:firstname.lastname@example.org)
Scheduling multiple large-scale parallel workflow applications in hybrid clouds is a fundamental NP-complete problem that is critical to obtaining good performance and execution cost. In this paper, we address the scheduling problem of large-scale applications inspired from real-world, characterized by a huge number of homogeneous and concurrent bags-of-tasks that are the main sources of bottlenecks but open great potential for optimization. We formulate the scheduling problem as a new sequential cooperative game and propose a communication and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, makespan, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.
X. Li, R. Prodan and R. Duan, "Multiobjective Game Theory-based Schedule Optimization for Bags-of-Tasks on Hybrid Clouds," in IEEE Transactions on Cloud Computing.