DAMP: Dependency-Aware Microservice Placement in Vehicular Edge Computing

DAMP: Dependency-Aware Microservice Placement in Vehicular Edge Computing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article DAMP: Dependency-Aware Microservice Placement in Vehicular Edge Computing Surayya. A., Md Muzakkir Hussain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8767853/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Efficient microservice placement in Vehicular Edge Computing (VEC) is critical to ensure low latency, high resource utilization, and service-chain integrity under dynamic vehicular mobility. This paper proposes DAMP, a dependencyaware microservice placement framework implemented using a customized PPO agent (DAMP–PPO) that jointly minimizes endto-end service latency, migration overhead, and cloud offloading while maximizing edge resource utilization. The placement problem is formulated as a constrained Markov Decision Process (MDP) and optimized through a unified policy learning strategy that incorporates dependency awareness, mobility-induced variability, and strict resource feasibility. Action masking and deterministic feasibility repair guarantee valid placements under capacity constraints, while the reward formulation integrates latency, migration, and chain-integrity objectives. The framework is evaluated using real SUMO-generated mobility traces from Luxembourg City across five representative scenarios: Balanced, Stress-CPU, Stress-Bandwidth, Stress-Load, and ForceSplit-Edge. Experimental results show that DAMP–PPO significantly reduces average and 95th-percentile service latency, lowers dependency delay and migration overhead, and improves edge resource efficiency compared with Random, Greedy, A2C, and DQN baselines. These results demonstrate the effectiveness and scalability of the proposed framework for real-time, dependencyaware microservice placement in dynamic VEC environments. Computer Architecture and Engineering Applied Mathematics Vehicular Edge Computing Microservice Placement Reinforcement Learning Proximal Policy Optimization Service chaining Resource Management Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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This paper proposes DAMP, a dependencyaware microservice placement framework implemented using a customized PPO agent (DAMP–PPO) that jointly minimizes endto-end service latency, migration overhead, and cloud offloading while maximizing edge resource utilization. The placement problem is formulated as a constrained Markov Decision Process (MDP) and optimized through a unified policy learning strategy that incorporates dependency awareness, mobility-induced variability, and strict resource feasibility. Action masking and deterministic feasibility repair guarantee valid placements under capacity constraints, while the reward formulation integrates latency, migration, and chain-integrity objectives. The framework is evaluated using real SUMO-generated mobility traces from Luxembourg City across five representative scenarios: Balanced, Stress-CPU, Stress-Bandwidth, Stress-Load, and ForceSplit-Edge. Experimental results show that DAMP–PPO significantly reduces average and 95th-percentile service latency, lowers dependency delay and migration overhead, and improves edge resource efficiency compared with Random, Greedy, A2C, and DQN baselines. 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