In mission-critical IoT-MEC environments, jointly optimizing service placement and resource allocation is intractable due to the high-dimensional coupling of discrete topological decisions with continuous resource dimensioning. Furthermore, traditional methods oversimplify dependencies, overlooking multi-source “Wait-for-All” synchronization and the stochastic variance of bursty workloads. To bridge these gaps, we propose HyDK, a variance-aware framework synergizing Deep Reinforcement Learning (DRL) with convex optimization. The core innovation is our Action Space Pruning mechanism. We theoretically decompose the hybrid decision space by solving the continuous sub-problem to optimality via a KKT-based convex optimization routine. This acts as a deterministic optimality backstop, effectively pruning continuous dimensions and allowing the agent to focus exclusively on the complex discrete topological search. To address physical realities, we construct a finegrained Directed Acyclic Graph (DAG) model to capture data aggregation bottlenecks and integrate an M/G/1 queuing model incorporating the second moment of service time to mitigate longtail latency risks. Trace-driven simulations using the Edge-IIoTset demonstrate that HyDK improves system responsiveness by up to 25.6% with significantly tighter confidence intervals compared to existing baselines.

Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different providers. This article introduces an innovative two-layer game framework that captures independent self-interested competition among MDs and ESs, providing a more accurate reflection of multi-vendor environments. Additionally, the framework explores the influence of cloud-edge collaboration on ES competition, offering new insights into these dynamics. The proposed model extends previous research by developing algorithms that optimize task offloading and resource allocation strategies for both MDs and ESs, ensuring the convergence to Nash equilibrium in both layers. Simulation results demonstrate the potential of the framework to improve resource efficiency and system responsiveness in multi-provider MECC environments.

The proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge Computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints.

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge Computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. Key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments.