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 make span, cost,
system-level efficiency, fairness, and other aspects compared with other
related algorithms.
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