DynoPipe: Heterogeneous Edge-Cloud LLM Serving with Dynamically Orchestrated Pipeline Boundaries

Profiling and Scheduling Complex O-RAN Applications Across the 5G Edge and Cloud

The O-RAN paradigm decomposes intelligent RAN control into pipelines of interdependent AI/ML functions, including traffic prediction, signal quality estimation, and slice scheduling, that must execute across a dispersed continuum of far-edge, near-edge, and cloud resources under heterogeneous latency and bandwidth constraints. Despite the natural expression of these pipelines as Directed Acyclic Graphs (DAGs), no integrated methodology exists to profile their execution costs, map them onto dispersed infrastructure via scheduling heuristics, and validate the resulting placement under 5G cellular conditions. We present O-DAG, an end-to-end framework that closes this gap through four tightly coupled stages: (1) DagProfiler, a new open-source tool that instruments O-RAN Slice Scheduler and extracts per-task instruction counts and per-edge communication volumes; (2) a parameterized three-tier network topology encoding far-edge (DU, RIC), near-edge (edge), and cloud nodes with realistic link bandwidths; (3) an extension of the SAGA scheduling framework and (4) a custom DAG simulation module built on the MintEDGE simulator. We evaluate five scheduling algorithms (HEFT, MCT, MinMin, MaxMin, Duplex) for a slice scheduling application across various configurations spanning 5K--50K UEs, 2--20 cells, and 2--10 network slices. HEFT achieves the lowest makespan in all configurations, but scheduler rankings are workload-dependent. The SAGA--simulation gap serves as a regime diagnostic: negative gaps (up to -1.72%) identify compute-dominated configurations where HEFT overestimates conservatively, while a positive gap (+0.64%) at low slice counts exposes a communication-bound regime where bandwidth contention exceeds the scheduling model's assumptions. All artifacts are released for reproducibility.

CELLServe: An SLO-Aware and Cost Efficient LLMs Serving System for Serverless Computing Environments

CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.

Hierarchical Scheduler with Adaptive Time-Budget Reallocation for Time-Triggered Edge-Fog-Cloud Architectures

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.

Enhancing the Kubernetes Scheduler: A State-of-the-Art Review from Cloud to Edge

A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.

TAILOR: Token-Aware Partitioning and Routing for Edge–Cloud Transformer Inference

Workflow-Aware Expert Routing for Distributed LLM Serving Over the Edge-Cloud Continuum

Deploying Large Language Models (LLMs) over the edge-cloud continuum faces severe stability challenges due to the conflict between stochastic network topology and complex workflow dependencies. Existing schedulers, relying either on computationally prohibitive Graph Neural Networks (GNNs) or topology-agnostic heuristics, fail to reconcile this tension. To bridge these gaps, we propose STEM, a service-level and topology-aware orchestration framework that formulates distributed LLM serving as a workflow-aware routing problem over a monitored service overlay, in which heterogeneous service instances act as specialized experts. At the core of STEM lies the STAR-PPO algorithm, utilizing a lightweight graph-free perception mechanism. By leveraging Squeeze-and-Excitation attention, it extracts critical bottleneck features from raw telemetry with linear complexity, bypassing the scalability limits of message-passing paradigms. To further achieve Pareto-efficient trade-offs, we develop a Dynamic Weight Adaptation (DWA) mechanism that autonomously recalibrates optimization preferences based on entropy-regularized metric drift. Extensive experiments on real-world datasets spanning 2,000 nodes demonstrate that our framework significantly outperforms state-of-the-art baselines. Specifically, STAR-PPO reduces network transmission costs by 96.8% and improves comprehensive inference efficiency by 24.4%, while sustaining robust zero-shot generalization across regions, with average latency within $1.09\times $ of a target-domain-retrained reference under a strict cross-region protocol. Code and data are available at https://github.com/gymorsiback/STARPPO