TL;DR
Results show that ECD-DVFS provides a balanced trade-off among performance, energy efficiency, reliability, cost, and QoS through adaptive and energy-aware workflow scheduling in hybrid fog–edge–cloud environments.
Efficient workflow scheduling is essential for improving testing efficiency in large-scale Vehicle-to-Everything (V2X) protocol conformance testing. Existing directed acyclic graph (DAG) schedulers treat all test components uniformly, overlooking the inherent structural heterogeneity of V2X workflows which contain mixed independent and precedence-constrained components. This leads to cross-subsystem interference, low resource utilization, and extended makespan. This paper presents AOE–CP (AON DAG with Edge-Weighted Transformation and Critical Path Scheduling), a structure-aware hybrid scheduling architecture for V2X testing. Unlike existing heuristic-based improvements, AOE–CP achieves performance gains through domain-specific structural reorganization rather than new scheduling rules. It integrates three mechanisms: (i) structural decomposition to decouple independent and dependent components and resolve their conflicting optimization objectives; (ii) critical path thread isolation to eliminate cross-subsystem resource competition and guarantee zero critical path waiting time for V2X dominant-topology workflows; (iii) atomic operation-based time estimation supporting fully offline scheduling without runtime profiling overhead. Experiments show that, for large-scale workflow scenarios, AOE–CP reduces normalized makespan by 14–16% and 22–24% compared with HEFT (Heterogeneous Earliest Finish Time) and CPOP (Critical Path On Processor), respectively. Scheduling overhead is only 2.1 ms for 1000-component workflows. AOE-CP reaches makespan saturation with four threads, versus 12 for HEFT and 28 for CPOP, demonstrating superior resource efficiency and scalability. The framework can also be generalized to other mixed-dependency workflow scenarios.
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Cloud-edge-end collaborative Artificial Intelligence (AI) computing requires schedulers that allocate heterogeneous resources for Directed Acyclic Graph (DAG)-structured workflows across network tiers. Cross-tier data transfers create ripple effects where a single placement decision propagates delays to downstream tasks, degrading end-to-end completion rates. This paper presents DREAM, a Dynamic Ripple-Effect-Aware Meta-scheduling scheme in which Critical Path Lookahead Scheduling (CPLS) performs bounded-depth trajectory planning with soft reservations for critical tasks, while Opportunity-Cost-Aware Placement (OCAP) evaluates non-critical tasks through a four-component cost covering immediate efficiency, ripple effect, load stability, and opportunity cost. Extensive simulations demonstrate that under the heavy load of 600 tasks, DREAM sustains a task completion rate of ~66%, exceeding classical heuristics by over 10 percentage points. At the extreme load of 1000 tasks, the system utility score improves by 47% over HEFT. Robustness experiments verify competitive performance across multiple DAG topologies and estimation-noise levels.
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
JointCloud environments, including multi-cloud and federated cloud systems, increasingly rely on high-performance networks (HPNs) to support large-scale cross-cloud data transfers. In such settings, advance bandwidth reservation with timedependent pricing is essential for cost-efficient and predictable data movement, where the transfer cost depends on both dynamic link prices and path reconfiguration overhead. This paper investigates the optimal scheduling of the VPFB BRR-MinC, where VPFB (Variable Path, Fixed Bandwidth) allows routing paths to change across time slots while maintaining a constant reserved bandwidth, and BRR-MinC seeks a minimum-cost schedule for deadline-constrained data transfers. We formalize a timedependent cost model incorporating slot-varying edge weights and switching penalties, and prove that the problem is NPcomplete. To address the temporal coupling introduced by switching costs, we develop a segmentation-based dynamic programming framework and propose a scalable heuristic, Heu-VPFB-MinC-TD-S. Simulation results on an ESnet-inspired topology show that the proposed method achieves identical feasibility while reducing total transfer cost compared with a greedy baseline, at the expense of moderate additional runtime. These results demonstrate the effectiveness of segmentation-aware optimization for cost-efficient cross-cloud data transfer in JointCloud systems.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
