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<p>Cloud-edge environments require efficient task scheduling and resource management as they serve as key factors supporting the diverse, dynamic, and latency sensitive workloads of the modern smart infrastructures. Nevertheless, the non-uniformity of work, inconsistent work loads, and cold-start delays are major challenges to scalable and sustainable scheduling of tasks. In this paper, the authors introduce a new Context-Aware Predictive Reinforcement Framework (CAPRF++) of generalized task scheduling under unobservable workload scenarios, which is developed to be able to manage multi-modal workloads in distributed cloud-edge systems. In order to be compatible with heterogeneous forms of workload, a Context-Aware Task Feature Construction pipeline is suggested to encode metadata including task type, compute demand, deadline, and sustainability constraints into structured forms. Resource indexing strategy based on B+ tree can be used to achieve rapid and scalable task-device matching using real-time performance and energy indicators. It presents a new Weighted Multi-Objective PPO scheduling policy using a Weighted Tradeoff Controller that limits the policy choice to feasible SLA-energy-latency tradeoff space. Moreover, a FIFO replay buffer-based Adaptive Trajectory-Buffered PPO algorithm, in combination with a multi-objective reward model can be used to perform robust policy learning in changing workloads, balancing SLA compliance, execution time, energy consumption, and carbon footprint. In order to reduce cold-start overheads, a Predictive Prewarming module will be implemented which will pre- initialize runtime containers according to the workload expectations. The entire assessment is done with a massive simulated cloudedge condition that attempts to simulate actual workload dynamics and system heterogeneity. Significant simulation-based analyses show that CAPRF++ is an efficient system in terms of task completion latency, SLA satisfaction, energy consumption and generalization to unseen workloads in dynamic environments.</p>

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CAPRF++: a context-aware B+ tree scheduler with weighted multi-objective tradeoff guidance and predictive prewarming for generalized task scheduling in cloud systems

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • C. Felsy,
  • R. Isaac Sajan

摘要

Cloud-edge environments require efficient task scheduling and resource management as they serve as key factors supporting the diverse, dynamic, and latency sensitive workloads of the modern smart infrastructures. Nevertheless, the non-uniformity of work, inconsistent work loads, and cold-start delays are major challenges to scalable and sustainable scheduling of tasks. In this paper, the authors introduce a new Context-Aware Predictive Reinforcement Framework (CAPRF++) of generalized task scheduling under unobservable workload scenarios, which is developed to be able to manage multi-modal workloads in distributed cloud-edge systems. In order to be compatible with heterogeneous forms of workload, a Context-Aware Task Feature Construction pipeline is suggested to encode metadata including task type, compute demand, deadline, and sustainability constraints into structured forms. Resource indexing strategy based on B+ tree can be used to achieve rapid and scalable task-device matching using real-time performance and energy indicators. It presents a new Weighted Multi-Objective PPO scheduling policy using a Weighted Tradeoff Controller that limits the policy choice to feasible SLA-energy-latency tradeoff space. Moreover, a FIFO replay buffer-based Adaptive Trajectory-Buffered PPO algorithm, in combination with a multi-objective reward model can be used to perform robust policy learning in changing workloads, balancing SLA compliance, execution time, energy consumption, and carbon footprint. In order to reduce cold-start overheads, a Predictive Prewarming module will be implemented which will pre- initialize runtime containers according to the workload expectations. The entire assessment is done with a massive simulated cloudedge condition that attempts to simulate actual workload dynamics and system heterogeneity. Significant simulation-based analyses show that CAPRF++ is an efficient system in terms of task completion latency, SLA satisfaction, energy consumption and generalization to unseen workloads in dynamic environments.