Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework

• URL: http://arxiv.org/abs/2510.21135v1
• Date: Fri, 24 Oct 2025 03:58:31 GMT
• Title: Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework
• Abstract summary: Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare deployed over heterogeneous cloud-fog-edge infrastructures.<n>We propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process.<n> Experiments results validate our approach, demonstrating increasingly superior performance over baselines as complexity rises.
• Score: 2.5263430338308557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process: a global controller performs layer selection (edge, fog, or cloud), while specialized local controllers handle node assignment within the chosen layer. The primary optimization objective is the minimization of the workflow makespan. Experiments results validate our approach, demonstrating increasingly superior performance over baselines as workflow complexity rises. This trend highlights the frameworks ability to learn effective long-term strategies, which is critical for complex, large-scale MIoT scheduling scenarios.

Related papers

• Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning [10.978371228753518]
  We develop a Graph Attention-Driven Reinforcement Learning (GA-HRL) framework and model scheduling process.<n>It balances deadline satisfaction, container utilization, and energy consumption while dealing with task-execution speeds, placement-dependent communication, and coupled task and container decisions.<n>Experiments on the 2018 Alibaba cluster trace show that GA-HRL maintains competitive workflow success rate and, in settings where success is comparable, achieves higher container utilization and lower energy consumption.
  arXiv  Detail & Related papers  (2026-09-14T02:50:40Z)
• HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization [11.533835891575565]
  HeraSys is a serving system designed to optimize the end-to-end performance of concurrent processing.<n>HeraSys reduces P99 latency by up to 2.17$timestimes serving throughput by up to 1.85timestimes under strict latency guarantees.
  arXiv  Detail & Related papers  (2026-06-07T03:14:57Z)
• Workflow-R1: Group Sub-sequence Policy Optimization for Multi-turn Workflow Construction [25.928675237308074]
  We present gradient-R1, a framework that reformulates workflow construction as a multi-turn, natural language-based sequential decision-making process.<n>GSsPO functions as a structure-aware RL algorithm generalizable to a broad class of multi-turn agentic sequential decision-making tasks.
  arXiv  Detail & Related papers  (2026-02-01T12:44:59Z)
• CSGO: Generalized Optimization for Cold Start in Wireless Collaborative Edge LLM Systems [62.24576366776727]
  We propose a latency-aware scheduling framework to minimize total inference latency.<n>We show that the proposed method significantly reduces cold-start latency compared to baseline strategies.
  arXiv  Detail & Related papers  (2025-08-15T07:49:22Z)
• Flow: Modularized Agentic Workflow Automation [53.073598156915615]
  Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution.<n>However, the effective adjustment of agentic during execution has not been well studied.<n>In this paper, we define an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by agents.<n>Our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance.
  arXiv  Detail & Related papers  (2025-01-14T04:35:37Z)
• An Expectation-Maximization Algorithm-based Autoregressive Model for the Fuzzy Job Shop Scheduling Problem [12.862865254507177]
  The fuzzy job shop scheduling problem (FJSSP) emerges as an innovative extension to the job shop scheduling problem (JSSP)<n>This paper investigates the feasibility of employing neural networks to assimilate and process fuzzy information for the resolution of FJSSP.
  arXiv  Detail & Related papers  (2025-01-11T10:20:16Z)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorfBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.<n>We also present WorfEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.<n>We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning [2.365237699556817]
  Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications. We propose a new DAG scheduling framework that attempts to minimize the DAG width by iteratively generating edges. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG schedulings and an optimal mixed-integer linear programming baseline.
  arXiv  Detail & Related papers  (2023-08-28T15:19:18Z)
• A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing [9.866286878494979]
  In multi-stage jobs, each job consists of multiple coflows and is represented by a Directed Acyclic Graph (DAG) In this paper, we propose a novel Pipelined-DAGNN to process the input and propose a novel coflow scheduling algorithm.
  arXiv  Detail & Related papers  (2021-12-21T09:36:55Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Conditioned Options [54.40780660868349]
  We introduce a compositional framework called Linearly-Solvable Goal Kernel Dynamic Programming (LS-GKDP)<n>LS-GKDP combines the Linearly-Solvable Markov Decision Process (LMDP) formalism with the Options Framework of Reinforcement Learning.<n>We show how an LMDP with a goal kernel enables the efficient optimization of meta-policies in a lower-dimensional subspace defined by the task grounding.
  arXiv  Detail & Related papers  (2020-07-06T05:13:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.