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.
The design and verification of safety-critical real-time systems rely on schedulability analysis. Exact tests may provide correctness guarantees, but they are often scheduler specific, computationally intractable at high scale, and pessimistic when used in iterative analysis pipelines. The paper introduces the SAFE-TSFormer algorithm to handle periodic real-time task-sets of variable cardinality under multiple uniprocessor scheduling policies. The SAFE-TSFormer approach employs a two-stage strategy. At the outset, it performs non-linear feature encoding at the task level, generating encoded representations for each task. These representations are then processed using transformer-based sequence modeling with masked attention to capture inter-task interactions. SAFE-TSFormer is designed as a conservative screening mechanism, prioritizing the suppression of false positives through a safety-aware training objective. We present results from experiments conducted on a large set of inflated periodic real-time task sets comprising over 3, 60, 000 instances. The proposed model generalizes to task-set sizes (number of real-time tasks) significantly higher than those observed during training. Specifically, on entirely new sets of tasks containing up to thirty-two tasks, SAFE-TSFormer’s overall accuracy reaches 96.56% . The results demonstrate the ability of a learner-based schedulability predictor to operate as a complementary technology within a solution optimization system, which rapidly and scalably screens candidates for feasibility without sacrificing safety, with high accuracy ( 98.98% ) and very low false positive rates ( <0.4% ).
We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a predictive accuracy exceeding 70% for systems of as many as 20 tasks without making any unsafe predictions. Furthermore, the implementation has very small ( <1 ms on two widely-used embedded platforms; <4 s on an embedded FPGA) and highly predictable running times.
