Abstract:
Electricity prices vary by time and location. Cloud workflow applications transmit geo-distributed data between heterogeneous servers in intra- and inter-data centers. When scheduling workflow tasks to heterogeneous servers in cloud data centers, varying electricity prices and data transmission time make energy cost optimization difficult.
In a deadline-constrained energy-aware workflow scheduling problem with data distributed across data centers, we minimize the total electricity cost. Scheduling algorithm. Workflow applications, deadlines, and tasks are arranged.
An adaptive local search method dynamically balances intensification using neighborhood structures of increasing size to improve solutions. Components and parameter values are statistically calibrated over many random instances. Comparing the proposed algorithm to modified classical algorithms for similar problems. The proposed solution works in experiments.
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