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<p>To ensure real-time performance and reliability, workloads in an airborne-cloud are often planned in advance, thereby making key task attributes—such as estimated arrival time, execution duration, and resource requirements—available prior to scheduling. Such foresight provides an opportunity to optimize scheduling from a long-term perspective, whereas most existing airborne-cloud schedulers are designed for online settings and assume that future task information is unavailable. To bridge this gap, we formalize the planned task scheduling problem and propose ElitePT (Elite Genetic Algorithm for Planned Tasks), a scheduling strategy that explicitly exploits planned information. ElitePT evaluates candidate schedules with a long-term performance objective that jointly accounts for load balancing and energy consumption over a planning horizon, and it strengthens the evolutionary search by injecting heuristic-constructed elite individuals while enforcing population diversity through Hamming-distance-based initialization. Experiments in CloudSim show that, compared with representative baselines including least-loaded scheduling, rotating scheduling, and a standard genetic algorithm, ElitePT delivers consistently better long-term scheduling quality and stability; across diverse cluster scales, heterogeneity settings, and overlap levels, it improves long-term load balancing by 20–50% and reduces energy consumption by 30–45%.</p>

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ElitePT: A scheduling strategy for planned task in airborne cloud computing environment

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Ning Wang,
  • Zhongqing Shu,
  • WenJian Liao,
  • Bohan Li,
  • Qi Guo,
  • Xiaolin Qin

摘要

To ensure real-time performance and reliability, workloads in an airborne-cloud are often planned in advance, thereby making key task attributes—such as estimated arrival time, execution duration, and resource requirements—available prior to scheduling. Such foresight provides an opportunity to optimize scheduling from a long-term perspective, whereas most existing airborne-cloud schedulers are designed for online settings and assume that future task information is unavailable. To bridge this gap, we formalize the planned task scheduling problem and propose ElitePT (Elite Genetic Algorithm for Planned Tasks), a scheduling strategy that explicitly exploits planned information. ElitePT evaluates candidate schedules with a long-term performance objective that jointly accounts for load balancing and energy consumption over a planning horizon, and it strengthens the evolutionary search by injecting heuristic-constructed elite individuals while enforcing population diversity through Hamming-distance-based initialization. Experiments in CloudSim show that, compared with representative baselines including least-loaded scheduling, rotating scheduling, and a standard genetic algorithm, ElitePT delivers consistently better long-term scheduling quality and stability; across diverse cluster scales, heterogeneity settings, and overlap levels, it improves long-term load balancing by 20–50% and reduces energy consumption by 30–45%.