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<!doctype html><html lang=en dir=auto data-theme=auto><head><meta charset=utf-8><meta http-equiv=X-UA-Compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=robots content="index, follow"><title>TCC 2025 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="12 papers selected.
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DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment
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Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu
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TL;DR — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.
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Why notable — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2025/><link crossorigin=anonymous href=/vincent/publish-assistant/assets/css/stylesheet.d72f07832e13c592b3edba91680bfe70f01daac396179bcace0ac36e8e0494c6.css integrity="sha256-1y8Hgy4TxZKz7bqRaAv+cPAdqsOWF5vKzgrDbo4ElMY=" rel="preload stylesheet" as=style><link rel=icon href=https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico><link rel=icon type=image/png sizes=16x16 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-16x16.png><link rel=icon type=image/png sizes=32x32 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-32x32.png><link rel=apple-touch-icon href=https://pub.sqrt.fr/vincent/publish-assistant/apple-touch-icon.png><link rel=mask-icon href=https://pub.sqrt.fr/vincent/publish-assistant/safari-pinned-tab.svg><meta name=theme-color content="#2e2e33"><meta name=msapplication-TileColor content="#2e2e33"><link rel=alternate hreflang=en href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2025/><noscript><style>#theme-toggle,.top-link{display:none}</style><style>@media(prefers-color-scheme:dark){:root{--theme:rgb(29, 30, 32);--entry:rgb(46, 46, 51);--primary:rgb(218, 218, 219);--secondary:rgb(155, 156, 157);--tertiary:rgb(65, 66, 68);--content:rgb(196, 196, 197);--code-block-bg:rgb(46, 46, 51);--code-bg:rgb(55, 56, 62);--border:rgb(51, 51, 51);color-scheme:dark}.list{background:var(--theme)}.toc{background:var(--entry)}}</style></noscript><script>localStorage.getItem("pref-theme")==="dark"?document.querySelector("html").dataset.theme="dark":localStorage.getItem("pref-theme")==="light"?document.querySelector("html").dataset.theme="light":window.matchMedia("(prefers-color-scheme: dark)").matches?document.querySelector("html").dataset.theme="dark":document.querySelector("html").dataset.theme="light"</script><meta property="og:url" content="https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2025/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="TCC 2025 Digest"><meta property="og:description" content="12 papers selected.
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DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu
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TL;DR — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.
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Why notable — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners."><meta property="og:locale" content="en_us"><meta property="og:type" content="article"><meta property="article:section" content="cloud-edge"><meta property="article:published_time" content="2025-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2025-01-01T00:00:00+00:00"><meta name=twitter:card content="summary"><meta name=twitter:title content="TCC 2025 Digest"><meta name=twitter:description content="12 papers selected.
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DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu
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TL;DR — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.
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Why notable — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners."><script type=application/ld+json>{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Edge and Cloud Systems","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/"},{"@type":"ListItem","position":2,"name":"Digests","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/"},{"@type":"ListItem","position":3,"name":"TCC 2025 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2025/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"TCC 2025 Digest","name":"TCC 2025 Digest","description":"12 papers selected.\nDRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu\nTL;DR — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.\nWhy notable — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners.\n","keywords":[],"articleBody":"12 papers selected.\nDRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu\nTL;DR — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.\nWhy notable — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners.\nDesFaaS: Cross-Layer Joint Dynamic Deployment System for Serverless Stateful Functions Yuquan Jing, Binbin Feng, Zhijun Ding\nTL;DR — DesFaaS jointly optimizes the placement and lifecycle of stateful serverless functions across compute, network, and storage layers to reduce latency and cost.\nWhy notable — Addresses the hard problem of state management in FaaS by co-designing across layers, opening a new direction for stateful serverless architectures.\nCARL: Cost-Optimized Online Container Placement on VMs Using Adversarial Reinforcement Learning Prathamesh Saraf Vinayak, Saswat Subhajyoti Mallick, Lakshmi Jagarlamudi, Anirban Chakraborty 0001 et al.\nTL;DR — CARL applies adversarial reinforcement learning to online container bin-packing on cloud VMs, minimizing cost while handling adversarial workload patterns.\nWhy notable — The adversarial training objective makes the scheduler robust to worst-case workload shifts, a significant advance over standard RL-based placement.\nCADER: Cost-Efficient Cloud Application Deployment With Tenant Requirement Guarantee in Multi-Clouds Huaqing Tu, Ziqiang Hua, Qianpiao Ma, Hanguang Luo et al.\nTL;DR — CADER places cloud application components across multiple providers to minimize cost while enforcing per-tenant SLA and data-locality constraints.\nWhy notable — Provides a rigorous multi-cloud placement framework that balances cost and tenant requirements, directly addressing a key challenge in multi-cloud SaaS/PaaS deployments.\nCloud Load Balancers Need to Stay Off the Data Path Yuchen Zhang, Shuai Jin, Zhenyu Wen, Shibo He et al.\nTL;DR — Based on large-scale production experience, this paper argues and demonstrates that cloud load balancers should operate out-of-band to eliminate throughput bottlenecks at scale.\nWhy notable — A rare production-grounded architectural insight from a major cloud provider that challenges conventional in-path load balancer designs.\nPHOENIX: Misconfiguration Detection for AWS Serverless Computing Jinfeng Wen, Haodi Ping\nTL;DR — PHOENIX automatically detects security and correctness misconfigurations in AWS Lambda deployments by analyzing IAM policies, triggers, and function configurations.\nWhy notable — Serverless misconfiguration is a leading cause of cloud security incidents; PHOENIX provides an automated, deployable detection tool for AWS environments.\nFaaSScout: Fast and Full Lifecycle RCA for FaaS Applications Using Salient Feature Mining Min Li 0065, Jin Huang, Pengfei Chen 0002, Chongkang Tan\nTL;DR — FaaSScout performs root cause analysis across the full FaaS invocation lifecycle by mining salient features from traces and logs to localize faults quickly.\nWhy notable — Fills a critical operational gap for serverless: fast, automated fault diagnosis that covers cold starts, platform issues, and application errors in a unified framework.\nHybrid Serverless Platform for Smart Deployment of Service Function Chains Sheshadri K. R, J. Lakshmi\nTL;DR — A hybrid serverless platform that intelligently places NFV service function chains on serverless infrastructure, reducing provisioning overhead while meeting latency targets.\nWhy notable — Bridges serverless computing and NFV, demonstrating that serverless abstractions can be applied to network function deployment with competitive performance.\nPiCoP: Service Mesh for Sharing Microservices in Multiple Environments Using Protocol-Independent Context Propagation Hiroya Onoe, Daisuke Kotani, Yasuo Okabe\nTL;DR — PiCoP extends service mesh capabilities to span heterogeneous protocol environments by providing protocol-independent context propagation for distributed microservice tracing and control.\nWhy notable — Solves a practical multi-cloud and hybrid deployment challenge where microservices communicate over different protocols, enabling unified observability and policy enforcement.\nA Reference Architecture for Governance of Cloud Native Applications William Pourmajidi, Lei Zhang 0078, John Steinbacher, Tony Erwin et al.\nTL;DR — Proposes and validates a reference architecture that unifies policy enforcement, compliance, and lifecycle governance for cloud-native applications across deployment environments.\nWhy notable — Provides a vendor-neutral governance blueprint grounded in industry practice, filling a gap between DevOps tooling and organizational cloud compliance requirements.\nObservability and Incident Response in Managed Serverless Environments Using Ontology-Based Log Monitoring Lavi Ben-Shimol, Edita Grolman, Aviad Elyashar, Inbar Maimon et al.\nTL;DR — Uses an ontology-based approach to monitor serverless function logs, enabling structured incident detection and response in managed FaaS environments.\nWhy notable — Brings structured knowledge representation to serverless observability, enabling richer incident correlation than rule-based or purely ML-based log monitors.\nA Run-Time Framework for Ensuring Zero-Trust State of Client’s Machines in Cloud Environment Devki Nandan Jha, Graham Lenton, James Asker, David Blundell et al.\nTL;DR — A runtime attestation framework continuously verifies the security posture of client machines accessing cloud resources, enforcing zero-trust policies based on live system state.\nWhy notable — Moves zero-trust enforcement from static policy configuration to continuous runtime verification, addressing a key gap in current cloud access control models.\n","wordCount":"793","inLanguage":"en","datePublished":"2025-01-01T00:00:00Z","dateModified":"2025-01-01T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2025/"},"publisher":{"@type":"Organization","name":"Publish Assistant","logo":{"@type":"ImageObject","url":"https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico"}}}</script></head><body id=top><header class=header><nav class=header-nav><div class=logo><a href=https://pub.sqrt.fr/vincent/publish-assistant/ accesskey=h title="Publish Assistant (Alt + H)">Publish Assistant</a>
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<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-chevron-right"><polyline points="9 18 15 12 9 6"/></svg></nav><h1 class="post-title entry-hint-parent">TCC 2025 Digest</h1><div class=post-meta><span title='2025-01-01 00:00:00 +0000 UTC'>January 1, 2025</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=drkc-deep-reinforcement-learning-enhanced-microservice-scheduling-on-kubernetes-clusters-in-cloud-edge-environment>DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment<a hidden class=anchor aria-hidden=true href=#drkc-deep-reinforcement-learning-enhanced-microservice-scheduling-on-kubernetes-clusters-in-cloud-edge-environment>#</a></h3><p><em>Jian Jiang, Qianmu Li, Pengchuan Wang, Yunhuai Liu</em></p><p><strong>TL;DR</strong> — DRKC uses deep reinforcement learning to schedule microservices across Kubernetes clusters spanning cloud and edge nodes, optimizing latency and resource utilization.</p><p><strong>Why notable</strong> — One of the few papers to tackle DRL-based microservice placement at the Kubernetes level in a real cloud-edge topology, making it directly actionable for practitioners.</p><hr><h3 id=desfaas-cross-layer-joint-dynamic-deployment-system-for-serverless-stateful-functions>DesFaaS: Cross-Layer Joint Dynamic Deployment System for Serverless Stateful Functions<a hidden class=anchor aria-hidden=true href=#desfaas-cross-layer-joint-dynamic-deployment-system-for-serverless-stateful-functions>#</a></h3><p><em>Yuquan Jing, Binbin Feng, Zhijun Ding</em></p><p><strong>TL;DR</strong> — DesFaaS jointly optimizes the placement and lifecycle of stateful serverless functions across compute, network, and storage layers to reduce latency and cost.</p><p><strong>Why notable</strong> — Addresses the hard problem of state management in FaaS by co-designing across layers, opening a new direction for stateful serverless architectures.</p><hr><h3 id=carl-cost-optimized-online-container-placement-on-vms-using-adversarial-reinforcement-learning>CARL: Cost-Optimized Online Container Placement on VMs Using Adversarial Reinforcement Learning<a hidden class=anchor aria-hidden=true href=#carl-cost-optimized-online-container-placement-on-vms-using-adversarial-reinforcement-learning>#</a></h3><p><em>Prathamesh Saraf Vinayak, Saswat Subhajyoti Mallick, Lakshmi Jagarlamudi, Anirban Chakraborty 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — CARL applies adversarial reinforcement learning to online container bin-packing on cloud VMs, minimizing cost while handling adversarial workload patterns.</p><p><strong>Why notable</strong> — The adversarial training objective makes the scheduler robust to worst-case workload shifts, a significant advance over standard RL-based placement.</p><hr><h3 id=cader-cost-efficient-cloud-application-deployment-with-tenant-requirement-guarantee-in-multi-clouds>CADER: Cost-Efficient Cloud Application Deployment With Tenant Requirement Guarantee in Multi-Clouds<a hidden class=anchor aria-hidden=true href=#cader-cost-efficient-cloud-application-deployment-with-tenant-requirement-guarantee-in-multi-clouds>#</a></h3><p><em>Huaqing Tu, Ziqiang Hua, Qianpiao Ma, Hanguang Luo <em>et al.</em></em></p><p><strong>TL;DR</strong> — CADER places cloud application components across multiple providers to minimize cost while enforcing per-tenant SLA and data-locality constraints.</p><p><strong>Why notable</strong> — Provides a rigorous multi-cloud placement framework that balances cost and tenant requirements, directly addressing a key challenge in multi-cloud SaaS/PaaS deployments.</p><hr><h3 id=cloud-load-balancers-need-to-stay-off-the-data-path>Cloud Load Balancers Need to Stay Off the Data Path<a hidden class=anchor aria-hidden=true href=#cloud-load-balancers-need-to-stay-off-the-data-path>#</a></h3><p><em>Yuchen Zhang, Shuai Jin, Zhenyu Wen, Shibo He <em>et al.</em></em></p><p><strong>TL;DR</strong> — Based on large-scale production experience, this paper argues and demonstrates that cloud load balancers should operate out-of-band to eliminate throughput bottlenecks at scale.</p><p><strong>Why notable</strong> — A rare production-grounded architectural insight from a major cloud provider that challenges conventional in-path load balancer designs.</p><hr><h3 id=phoenix-misconfiguration-detection-for-aws-serverless-computing>PHOENIX: Misconfiguration Detection for AWS Serverless Computing<a hidden class=anchor aria-hidden=true href=#phoenix-misconfiguration-detection-for-aws-serverless-computing>#</a></h3><p><em>Jinfeng Wen, Haodi Ping</em></p><p><strong>TL;DR</strong> — PHOENIX automatically detects security and correctness misconfigurations in AWS Lambda deployments by analyzing IAM policies, triggers, and function configurations.</p><p><strong>Why notable</strong> — Serverless misconfiguration is a leading cause of cloud security incidents; PHOENIX provides an automated, deployable detection tool for AWS environments.</p><hr><h3 id=faasscout-fast-and-full-lifecycle-rca-for-faas-applications-using-salient-feature-mining>FaaSScout: Fast and Full Lifecycle RCA for FaaS Applications Using Salient Feature Mining<a hidden class=anchor aria-hidden=true href=#faasscout-fast-and-full-lifecycle-rca-for-faas-applications-using-salient-feature-mining>#</a></h3><p><em>Min Li 0065, Jin Huang, Pengfei Chen 0002, Chongkang Tan</em></p><p><strong>TL;DR</strong> — FaaSScout performs root cause analysis across the full FaaS invocation lifecycle by mining salient features from traces and logs to localize faults quickly.</p><p><strong>Why notable</strong> — Fills a critical operational gap for serverless: fast, automated fault diagnosis that covers cold starts, platform issues, and application errors in a unified framework.</p><hr><h3 id=hybrid-serverless-platform-for-smart-deployment-of-service-function-chains>Hybrid Serverless Platform for Smart Deployment of Service Function Chains<a hidden class=anchor aria-hidden=true href=#hybrid-serverless-platform-for-smart-deployment-of-service-function-chains>#</a></h3><p><em>Sheshadri K. R, J. Lakshmi</em></p><p><strong>TL;DR</strong> — A hybrid serverless platform that intelligently places NFV service function chains on serverless infrastructure, reducing provisioning overhead while meeting latency targets.</p><p><strong>Why notable</strong> — Bridges serverless computing and NFV, demonstrating that serverless abstractions can be applied to network function deployment with competitive performance.</p><hr><h3 id=picop-service-mesh-for-sharing-microservices-in-multiple-environments-using-protocol-independent-context-propagation>PiCoP: Service Mesh for Sharing Microservices in Multiple Environments Using Protocol-Independent Context Propagation<a hidden class=anchor aria-hidden=true href=#picop-service-mesh-for-sharing-microservices-in-multiple-environments-using-protocol-independent-context-propagation>#</a></h3><p><em>Hiroya Onoe, Daisuke Kotani, Yasuo Okabe</em></p><p><strong>TL;DR</strong> — PiCoP extends service mesh capabilities to span heterogeneous protocol environments by providing protocol-independent context propagation for distributed microservice tracing and control.</p><p><strong>Why notable</strong> — Solves a practical multi-cloud and hybrid deployment challenge where microservices communicate over different protocols, enabling unified observability and policy enforcement.</p><hr><h3 id=a-reference-architecture-for-governance-of-cloud-native-applications>A Reference Architecture for Governance of Cloud Native Applications<a hidden class=anchor aria-hidden=true href=#a-reference-architecture-for-governance-of-cloud-native-applications>#</a></h3><p><em>William Pourmajidi, Lei Zhang 0078, John Steinbacher, Tony Erwin <em>et al.</em></em></p><p><strong>TL;DR</strong> — Proposes and validates a reference architecture that unifies policy enforcement, compliance, and lifecycle governance for cloud-native applications across deployment environments.</p><p><strong>Why notable</strong> — Provides a vendor-neutral governance blueprint grounded in industry practice, filling a gap between DevOps tooling and organizational cloud compliance requirements.</p><hr><h3 id=observability-and-incident-response-in-managed-serverless-environments-using-ontology-based-log-monitoring>Observability and Incident Response in Managed Serverless Environments Using Ontology-Based Log Monitoring<a hidden class=anchor aria-hidden=true href=#observability-and-incident-response-in-managed-serverless-environments-using-ontology-based-log-monitoring>#</a></h3><p><em>Lavi Ben-Shimol, Edita Grolman, Aviad Elyashar, Inbar Maimon <em>et al.</em></em></p><p><strong>TL;DR</strong> — Uses an ontology-based approach to monitor serverless function logs, enabling structured incident detection and response in managed FaaS environments.</p><p><strong>Why notable</strong> — Brings structured knowledge representation to serverless observability, enabling richer incident correlation than rule-based or purely ML-based log monitors.</p><hr><h3 id=a-run-time-framework-for-ensuring-zero-trust-state-of-clients-machines-in-cloud-environment>A Run-Time Framework for Ensuring Zero-Trust State of Client’s Machines in Cloud Environment<a hidden class=anchor aria-hidden=true href=#a-run-time-framework-for-ensuring-zero-trust-state-of-clients-machines-in-cloud-environment>#</a></h3><p><em>Devki Nandan Jha, Graham Lenton, James Asker, David Blundell <em>et al.</em></em></p><p><strong>TL;DR</strong> — A runtime attestation framework continuously verifies the security posture of client machines accessing cloud resources, enforcing zero-trust policies based on live system state.</p><p><strong>Why notable</strong> — Moves zero-trust enforcement from static policy configuration to continuous runtime verification, addressing a key gap in current cloud access control models.</p></div><footer class=post-footer><ul class=post-tags></ul><nav class=paginav><a class=prev href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tc-2025/><span class=title>« Prev</span>
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