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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