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29 lines
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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 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="12 papers selected.
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FaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms
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Abhisek Panda, Smruti R. Sarangi
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TL;DR — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.
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Why notable — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.
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FUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization
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Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach"><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2024/><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-2024/><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-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="TCC 2024 Digest"><meta property="og:description" content="12 papers selected.
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FaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms Abhisek Panda, Smruti R. Sarangi
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TL;DR — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.
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Why notable — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.
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FUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach"><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="2024-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2024-01-01T00:00:00+00:00"><meta name=twitter:card content="summary"><meta name=twitter:title content="TCC 2024 Digest"><meta name=twitter:description content="12 papers selected.
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FaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms Abhisek Panda, Smruti R. Sarangi
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TL;DR — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.
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Why notable — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.
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FUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach"><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 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"TCC 2024 Digest","name":"TCC 2024 Digest","description":"12 papers selected.\nFaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms Abhisek Panda, Smruti R. Sarangi\nTL;DR — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.\nWhy notable — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.\nFUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach\n","keywords":[],"articleBody":"12 papers selected.\nFaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms Abhisek Panda, Smruti R. Sarangi\nTL;DR — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.\nWhy notable — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.\nFUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach\nTL;DR — FUSIONIZE++ dynamically fuses serverless functions at runtime and co-optimizes infrastructure selection to cut invocation overhead and cost.\nWhy notable — Demonstrates concrete end-to-end performance gains from function fusion in real serverless deployments, directly relevant to practitioners optimizing FaaS pipelines.\nBaaSLess: Backend-as-a-Service (BaaS)-Enabled Workflows in Federated Serverless Infrastructures Thomas Larcher, Philipp Gritsch, Stefan Nastic, Sashko Ristov\nTL;DR — BaaSLess integrates BaaS capabilities into serverless workflow execution across federated multi-provider infrastructures, enabling stateful cross-cloud function orchestration.\nWhy notable — Addresses the underexplored intersection of BaaS, serverless, and federation, offering a practical blueprint for multi-cloud serverless applications.\nSlim and Fast: Low-Overhead Container Overlay Network With Fast Connection Setup Fusheng Lin, Xin Zhang 0117, Guo Chen 0001, Li Chen 0008 et al.\nTL;DR — A redesigned container overlay network that minimizes control-plane overhead and dramatically reduces connection setup latency for microservice-dense deployments.\nWhy notable — The implementation targets real Kubernetes environments and directly improves east-west latency for microservices at scale.\nTrustless Collaborative Cloud Federation Bishakh Chandra Ghosh, Sandip Chakraborty 0001\nTL;DR — A blockchain-backed protocol that enables resource sharing across competing cloud providers without requiring a trusted third party.\nWhy notable — Tackles the fundamental trust barrier in multi-cloud federation with a practical, decentralized design that avoids provider lock-in.\nAn Adaptive Cloud Resource Quota Scheme Based on Dynamic Portraits and Task-Resource Matching Zuodong Jin, Dan Tao, Peng Qi 0006, Ruipeng Gao\nTL;DR — Builds dynamic workload portraits per tenant and matches them to resource quotas in real time, improving utilization while respecting SLAs in IaaS/PaaS clouds.\nWhy notable — Moves beyond static quota assignment with a data-driven approach validated on production cloud workload traces.\nAggregate Monitoring for Geo-Distributed Kubernetes Cluster Federations Chih-Kai Huang 0001, Guillaume Pierre\nTL;DR — Proposes a scalable monitoring architecture for Kubernetes federations that aggregates metrics across geo-distributed clusters with low overhead.\nWhy notable — Practical multi-cloud observability is rarely addressed at the federation layer; this work provides a deployable solution with measured performance on real clusters.\nRoot Cause Analysis for Cloud-Native Applications Bartosz Zurkowski, Krzysztof Zielinski\nTL;DR — A graph-based RCA framework for microservice architectures that correlates traces, metrics, and logs to pinpoint fault origins in cloud-native deployments.\nWhy notable — Directly applicable to production microservice operations, offering automated diagnosis that reduces mean time to recovery in complex service graphs.\nRAM: A Resource-Aware DDoS Attack Mitigation Framework in Clouds Fangyuan Xing, Fei Tong 0001, Jialong Yang, Guang Cheng 0001 et al.\nTL;DR — RAM dynamically allocates cloud resources for DDoS mitigation based on attack intensity, balancing protection effectiveness against resource cost.\nWhy notable — Combines attack detection and elastic resource provisioning in a single framework, making it immediately relevant for cloud security operations.\nEnabling Multi-Layer Threat Analysis in Dynamic Cloud Environments Salman Manzoor, Antonios Gouglidis, Matthew Bradbury, Neeraj Suri\nTL;DR — A multi-layer threat analysis system that correlates security events across IaaS, PaaS, and application layers to detect composite attacks in dynamic cloud deployments.\nWhy notable — Addresses the gap between per-layer security tools and cross-layer attack detection, which is critical for securing modern cloud stacks.\nHyperion: Hardware-Based High-Performance and Secure System for Container Networks Myoungsung You, Minjae Seo, Jaehan Kim, Seungwon Shin 0001 et al.\nTL;DR — Hyperion offloads container network security enforcement to programmable hardware, achieving line-rate packet processing with strong isolation guarantees.\nWhy notable — Shows that hardware offload can simultaneously improve both throughput and security in container networking, with real implementation results.\nD-STACK: High Throughput DNN Inference by Effective Multiplexing and Spatio-Temporal Scheduling of GPUs Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan\nTL;DR — D-STACK multiplexes multiple DNN inference jobs on shared GPUs via spatio-temporal scheduling, significantly increasing throughput without latency SLO violations.\nWhy notable — Addresses a critical cloud resource management challenge for AI inference services, with a real system implementation and evaluation against production workloads.\n","wordCount":"715","inLanguage":"en","datePublished":"2024-01-01T00:00:00Z","dateModified":"2024-01-01T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tcc-2024/"},"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 2024 Digest</h1><div class=post-meta><span title='2024-01-01 00:00:00 +0000 UTC'>January 1, 2024</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=faasctrl-a-comprehensive-latency-controller-for-serverless-platforms>FaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms<a hidden class=anchor aria-hidden=true href=#faasctrl-a-comprehensive-latency-controller-for-serverless-platforms>#</a></h3><p><em>Abhisek Panda, Smruti R. Sarangi</em></p><p><strong>TL;DR</strong> — FaaSCtrl is a feedback-control system for serverless platforms that jointly manages cold-start, queuing, and execution latency to meet end-to-end SLOs.</p><p><strong>Why notable</strong> — One of the few serverless controllers that addresses all three latency components together, providing a principled alternative to ad-hoc autoscaling heuristics.</p><hr><h3 id=fusionize-improving-serverless-application-performance-using-dynamic-task-inlining-and-infrastructure-optimization>FUSIONIZE++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization<a hidden class=anchor aria-hidden=true href=#fusionize-improving-serverless-application-performance-using-dynamic-task-inlining-and-infrastructure-optimization>#</a></h3><p><em>Trever Schirmer, Joel Scheuner, Tobias Pfandzelter, David Bermbach</em></p><p><strong>TL;DR</strong> — FUSIONIZE++ dynamically fuses serverless functions at runtime and co-optimizes infrastructure selection to cut invocation overhead and cost.</p><p><strong>Why notable</strong> — Demonstrates concrete end-to-end performance gains from function fusion in real serverless deployments, directly relevant to practitioners optimizing FaaS pipelines.</p><hr><h3 id=baasless-backend-as-a-service-baas-enabled-workflows-in-federated-serverless-infrastructures>BaaSLess: Backend-as-a-Service (BaaS)-Enabled Workflows in Federated Serverless Infrastructures<a hidden class=anchor aria-hidden=true href=#baasless-backend-as-a-service-baas-enabled-workflows-in-federated-serverless-infrastructures>#</a></h3><p><em>Thomas Larcher, Philipp Gritsch, Stefan Nastic, Sashko Ristov</em></p><p><strong>TL;DR</strong> — BaaSLess integrates BaaS capabilities into serverless workflow execution across federated multi-provider infrastructures, enabling stateful cross-cloud function orchestration.</p><p><strong>Why notable</strong> — Addresses the underexplored intersection of BaaS, serverless, and federation, offering a practical blueprint for multi-cloud serverless applications.</p><hr><h3 id=slim-and-fast-low-overhead-container-overlay-network-with-fast-connection-setup>Slim and Fast: Low-Overhead Container Overlay Network With Fast Connection Setup<a hidden class=anchor aria-hidden=true href=#slim-and-fast-low-overhead-container-overlay-network-with-fast-connection-setup>#</a></h3><p><em>Fusheng Lin, Xin Zhang 0117, Guo Chen 0001, Li Chen 0008 <em>et al.</em></em></p><p><strong>TL;DR</strong> — A redesigned container overlay network that minimizes control-plane overhead and dramatically reduces connection setup latency for microservice-dense deployments.</p><p><strong>Why notable</strong> — The implementation targets real Kubernetes environments and directly improves east-west latency for microservices at scale.</p><hr><h3 id=trustless-collaborative-cloud-federation>Trustless Collaborative Cloud Federation<a hidden class=anchor aria-hidden=true href=#trustless-collaborative-cloud-federation>#</a></h3><p><em>Bishakh Chandra Ghosh, Sandip Chakraborty 0001</em></p><p><strong>TL;DR</strong> — A blockchain-backed protocol that enables resource sharing across competing cloud providers without requiring a trusted third party.</p><p><strong>Why notable</strong> — Tackles the fundamental trust barrier in multi-cloud federation with a practical, decentralized design that avoids provider lock-in.</p><hr><h3 id=an-adaptive-cloud-resource-quota-scheme-based-on-dynamic-portraits-and-task-resource-matching>An Adaptive Cloud Resource Quota Scheme Based on Dynamic Portraits and Task-Resource Matching<a hidden class=anchor aria-hidden=true href=#an-adaptive-cloud-resource-quota-scheme-based-on-dynamic-portraits-and-task-resource-matching>#</a></h3><p><em>Zuodong Jin, Dan Tao, Peng Qi 0006, Ruipeng Gao</em></p><p><strong>TL;DR</strong> — Builds dynamic workload portraits per tenant and matches them to resource quotas in real time, improving utilization while respecting SLAs in IaaS/PaaS clouds.</p><p><strong>Why notable</strong> — Moves beyond static quota assignment with a data-driven approach validated on production cloud workload traces.</p><hr><h3 id=aggregate-monitoring-for-geo-distributed-kubernetes-cluster-federations>Aggregate Monitoring for Geo-Distributed Kubernetes Cluster Federations<a hidden class=anchor aria-hidden=true href=#aggregate-monitoring-for-geo-distributed-kubernetes-cluster-federations>#</a></h3><p><em>Chih-Kai Huang 0001, Guillaume Pierre</em></p><p><strong>TL;DR</strong> — Proposes a scalable monitoring architecture for Kubernetes federations that aggregates metrics across geo-distributed clusters with low overhead.</p><p><strong>Why notable</strong> — Practical multi-cloud observability is rarely addressed at the federation layer; this work provides a deployable solution with measured performance on real clusters.</p><hr><h3 id=root-cause-analysis-for-cloud-native-applications>Root Cause Analysis for Cloud-Native Applications<a hidden class=anchor aria-hidden=true href=#root-cause-analysis-for-cloud-native-applications>#</a></h3><p><em>Bartosz Zurkowski, Krzysztof Zielinski</em></p><p><strong>TL;DR</strong> — A graph-based RCA framework for microservice architectures that correlates traces, metrics, and logs to pinpoint fault origins in cloud-native deployments.</p><p><strong>Why notable</strong> — Directly applicable to production microservice operations, offering automated diagnosis that reduces mean time to recovery in complex service graphs.</p><hr><h3 id=ram-a-resource-aware-ddos-attack-mitigation-framework-in-clouds>RAM: A Resource-Aware DDoS Attack Mitigation Framework in Clouds<a hidden class=anchor aria-hidden=true href=#ram-a-resource-aware-ddos-attack-mitigation-framework-in-clouds>#</a></h3><p><em>Fangyuan Xing, Fei Tong 0001, Jialong Yang, Guang Cheng 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — RAM dynamically allocates cloud resources for DDoS mitigation based on attack intensity, balancing protection effectiveness against resource cost.</p><p><strong>Why notable</strong> — Combines attack detection and elastic resource provisioning in a single framework, making it immediately relevant for cloud security operations.</p><hr><h3 id=enabling-multi-layer-threat-analysis-in-dynamic-cloud-environments>Enabling Multi-Layer Threat Analysis in Dynamic Cloud Environments<a hidden class=anchor aria-hidden=true href=#enabling-multi-layer-threat-analysis-in-dynamic-cloud-environments>#</a></h3><p><em>Salman Manzoor, Antonios Gouglidis, Matthew Bradbury, Neeraj Suri</em></p><p><strong>TL;DR</strong> — A multi-layer threat analysis system that correlates security events across IaaS, PaaS, and application layers to detect composite attacks in dynamic cloud deployments.</p><p><strong>Why notable</strong> — Addresses the gap between per-layer security tools and cross-layer attack detection, which is critical for securing modern cloud stacks.</p><hr><h3 id=hyperion-hardware-based-high-performance-and-secure-system-for-container-networks>Hyperion: Hardware-Based High-Performance and Secure System for Container Networks<a hidden class=anchor aria-hidden=true href=#hyperion-hardware-based-high-performance-and-secure-system-for-container-networks>#</a></h3><p><em>Myoungsung You, Minjae Seo, Jaehan Kim, Seungwon Shin 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Hyperion offloads container network security enforcement to programmable hardware, achieving line-rate packet processing with strong isolation guarantees.</p><p><strong>Why notable</strong> — Shows that hardware offload can simultaneously improve both throughput and security in container networking, with real implementation results.</p><hr><h3 id=d-stack-high-throughput-dnn-inference-by-effective-multiplexing-and-spatio-temporal-scheduling-of-gpus>D-STACK: High Throughput DNN Inference by Effective Multiplexing and Spatio-Temporal Scheduling of GPUs<a hidden class=anchor aria-hidden=true href=#d-stack-high-throughput-dnn-inference-by-effective-multiplexing-and-spatio-temporal-scheduling-of-gpus>#</a></h3><p><em>Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan</em></p><p><strong>TL;DR</strong> — D-STACK multiplexes multiple DNN inference jobs on shared GPUs via spatio-temporal scheduling, significantly increasing throughput without latency SLO violations.</p><p><strong>Why notable</strong> — Addresses a critical cloud resource management challenge for AI inference services, with a real system implementation and evaluation against production workloads.</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-2024/><span class=title>« Prev</span>
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