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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>SEC 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="12 papers selected.
EdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds
Amran Haroon
TL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.
Righteous: Automatic Right-Sizing for Complex Edge Deployments
Aniruddha Rakshit
TL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/sec-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/sec-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/sec-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="SEC 2024 Digest"><meta property="og:description" content="12 papers selected.
EdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds Amran Haroon
TL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.
Righteous: Automatic Right-Sizing for Complex Edge Deployments Aniruddha Rakshit
TL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention."><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="SEC 2024 Digest"><meta name=twitter:description content="12 papers selected.
EdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds Amran Haroon
TL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.
Righteous: Automatic Right-Sizing for Complex Edge Deployments Aniruddha Rakshit
TL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention."><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":"SEC 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/sec-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"SEC 2024 Digest","name":"SEC 2024 Digest","description":"12 papers selected.\nEdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds Amran Haroon\nTL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.\nRighteous: Automatic Right-Sizing for Complex Edge Deployments Aniruddha Rakshit\nTL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention.\n","keywords":[],"articleBody":"12 papers selected.\nEdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds Amran Haroon\nTL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.\nRighteous: Automatic Right-Sizing for Complex Edge Deployments Aniruddha Rakshit\nTL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention.\nColibri: Efficient Collection of Fine-Grained Resource Metrics Necessary for Mobile Edge Computing Ke-Jou Hsu\nTL;DR — Proposes a low-overhead monitoring system for collecting fine-grained resource metrics at the edge, enabling more accurate profiling for MEC scheduling decisions.\nHyperDrive: Scheduling Serverless Functions in the Edge-Cloud-Space 3D Continuum Thomas W. Pusztai\nTL;DR — Extends serverless scheduling across a three-dimensional edge-cloud-space continuum, addressing latency and resource constraints introduced by satellite and terrestrial tiers.\nFalcon: Live Reconfiguration for Stateful Stream Processing on the Edge Pritish Mishra\nTL;DR — Enables live, low-disruption reconfiguration of stateful stream processing pipelines at the edge, minimizing downtime during topology changes.\nFusedInf: Efficient Swapping of DNN Models for On-Demand Serverless Inference Services on the Edge Sifat Ut Taki\nTL;DR — Reduces cold-start latency for serverless DNN inference at the edge by fusing model loading with active inference through selective layer swapping.\nEcoEdgeInfer: Dynamically Optimizing Latency and Sustainability for Inference on Edge Devices Sri Pramodh Rachuri\nTL;DR — Co-optimizes inference latency and energy sustainability on edge devices by dynamically trading off accuracy and hardware utilization under carbon-aware constraints.\nElastic Execution of Multi-Tenant DNNs on Heterogeneous Edge MPSoCs Soroush Heidari\nTL;DR — Demonstrates elastic, interference-aware co-execution of multiple DNNs across heterogeneous processing elements in edge MPSoCs to maximize throughput and fairness.\nOptimizing Edge Offloading Decisions for Object Detection Jiaming Qiu\nTL;DR — Formulates and solves an online offloading decision problem for object detection that jointly minimizes latency and energy consumption under variable network conditions.\nVideoJam: Self-Balancing Architecture for Live Video Analytics Youssouph Faye\nTL;DR — Proposes a self-balancing edge architecture for live video analytics that dynamically redistributes pipeline stages to prevent bottlenecks under fluctuating camera workloads.\nOVIDA: Orchestrator for Video Analytics on Disaggregated Architecture Manavjeet Singh\nTL;DR — Designs an orchestration layer for disaggregated edge hardware that places and migrates video analytics microservices to exploit spatial locality and heterogeneous accelerators.\nTA-ASF: Attention-Sensitive Token Sampling and Fusing for Visual Transformer Models on the Edge Junquan Chen\nTL;DR — Accelerates Vision Transformer inference at the edge by pruning and fusing attention tokens based on saliency, achieving accuracy-efficiency trade-offs suitable for resource-constrained devices.\n","wordCount":"418","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/sec-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">SEC 2024 Digest</h1><div class=post-meta><span title='2024-01-01 00:00:00 +0000 UTC'>January 1, 2024</span>&nbsp;·&nbsp;<span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=edgecore-resource-dependency-aware-multi-tenant-orchestration-for-mobile-edge-clouds>EdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds<a hidden class=anchor aria-hidden=true href=#edgecore-resource-dependency-aware-multi-tenant-orchestration-for-mobile-edge-clouds>#</a></h3><p><em>Amran Haroon</em></p><p><strong>TL;DR</strong> — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.</p><hr><h3 id=righteous-automatic-right-sizing-for-complex-edge-deployments>Righteous: Automatic Right-Sizing for Complex Edge Deployments<a hidden class=anchor aria-hidden=true href=#righteous-automatic-right-sizing-for-complex-edge-deployments>#</a></h3><p><em>Aniruddha Rakshit</em></p><p><strong>TL;DR</strong> — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention.</p><hr><h3 id=colibri-efficient-collection-of-fine-grained-resource-metrics-necessary-for-mobile-edge-computing>Colibri: Efficient Collection of Fine-Grained Resource Metrics Necessary for Mobile Edge Computing<a hidden class=anchor aria-hidden=true href=#colibri-efficient-collection-of-fine-grained-resource-metrics-necessary-for-mobile-edge-computing>#</a></h3><p><em>Ke-Jou Hsu</em></p><p><strong>TL;DR</strong> — Proposes a low-overhead monitoring system for collecting fine-grained resource metrics at the edge, enabling more accurate profiling for MEC scheduling decisions.</p><hr><h3 id=hyperdrive-scheduling-serverless-functions-in-the-edge-cloud-space-3d-continuum>HyperDrive: Scheduling Serverless Functions in the Edge-Cloud-Space 3D Continuum<a hidden class=anchor aria-hidden=true href=#hyperdrive-scheduling-serverless-functions-in-the-edge-cloud-space-3d-continuum>#</a></h3><p><em>Thomas W. Pusztai</em></p><p><strong>TL;DR</strong> — Extends serverless scheduling across a three-dimensional edge-cloud-space continuum, addressing latency and resource constraints introduced by satellite and terrestrial tiers.</p><hr><h3 id=falcon-live-reconfiguration-for-stateful-stream-processing-on-the-edge>Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge<a hidden class=anchor aria-hidden=true href=#falcon-live-reconfiguration-for-stateful-stream-processing-on-the-edge>#</a></h3><p><em>Pritish Mishra</em></p><p><strong>TL;DR</strong> — Enables live, low-disruption reconfiguration of stateful stream processing pipelines at the edge, minimizing downtime during topology changes.</p><hr><h3 id=fusedinf-efficient-swapping-of-dnn-models-for-on-demand-serverless-inference-services-on-the-edge>FusedInf: Efficient Swapping of DNN Models for On-Demand Serverless Inference Services on the Edge<a hidden class=anchor aria-hidden=true href=#fusedinf-efficient-swapping-of-dnn-models-for-on-demand-serverless-inference-services-on-the-edge>#</a></h3><p><em>Sifat Ut Taki</em></p><p><strong>TL;DR</strong> — Reduces cold-start latency for serverless DNN inference at the edge by fusing model loading with active inference through selective layer swapping.</p><hr><h3 id=ecoedgeinfer-dynamically-optimizing-latency-and-sustainability-for-inference-on-edge-devices>EcoEdgeInfer: Dynamically Optimizing Latency and Sustainability for Inference on Edge Devices<a hidden class=anchor aria-hidden=true href=#ecoedgeinfer-dynamically-optimizing-latency-and-sustainability-for-inference-on-edge-devices>#</a></h3><p><em>Sri Pramodh Rachuri</em></p><p><strong>TL;DR</strong> — Co-optimizes inference latency and energy sustainability on edge devices by dynamically trading off accuracy and hardware utilization under carbon-aware constraints.</p><hr><h3 id=elastic-execution-of-multi-tenant-dnns-on-heterogeneous-edge-mpsocs>Elastic Execution of Multi-Tenant DNNs on Heterogeneous Edge MPSoCs<a hidden class=anchor aria-hidden=true href=#elastic-execution-of-multi-tenant-dnns-on-heterogeneous-edge-mpsocs>#</a></h3><p><em>Soroush Heidari</em></p><p><strong>TL;DR</strong> — Demonstrates elastic, interference-aware co-execution of multiple DNNs across heterogeneous processing elements in edge MPSoCs to maximize throughput and fairness.</p><hr><h3 id=optimizing-edge-offloading-decisions-for-object-detection>Optimizing Edge Offloading Decisions for Object Detection<a hidden class=anchor aria-hidden=true href=#optimizing-edge-offloading-decisions-for-object-detection>#</a></h3><p><em>Jiaming Qiu</em></p><p><strong>TL;DR</strong> — Formulates and solves an online offloading decision problem for object detection that jointly minimizes latency and energy consumption under variable network conditions.</p><hr><h3 id=videojam-self-balancing-architecture-for-live-video-analytics>VideoJam: Self-Balancing Architecture for Live Video Analytics<a hidden class=anchor aria-hidden=true href=#videojam-self-balancing-architecture-for-live-video-analytics>#</a></h3><p><em>Youssouph Faye</em></p><p><strong>TL;DR</strong> — Proposes a self-balancing edge architecture for live video analytics that dynamically redistributes pipeline stages to prevent bottlenecks under fluctuating camera workloads.</p><hr><h3 id=ovida-orchestrator-for-video-analytics-on-disaggregated-architecture>OVIDA: Orchestrator for Video Analytics on Disaggregated Architecture<a hidden class=anchor aria-hidden=true href=#ovida-orchestrator-for-video-analytics-on-disaggregated-architecture>#</a></h3><p><em>Manavjeet Singh</em></p><p><strong>TL;DR</strong> — Designs an orchestration layer for disaggregated edge hardware that places and migrates video analytics microservices to exploit spatial locality and heterogeneous accelerators.</p><hr><h3 id=ta-asf-attention-sensitive-token-sampling-and-fusing-for-visual-transformer-models-on-the-edge>TA-ASF: Attention-Sensitive Token Sampling and Fusing for Visual Transformer Models on the Edge<a hidden class=anchor aria-hidden=true href=#ta-asf-attention-sensitive-token-sampling-and-fusing-for-visual-transformer-models-on-the-edge>#</a></h3><p><em>Junquan Chen</em></p><p><strong>TL;DR</strong> — Accelerates Vision Transformer inference at the edge by pruning and fusing attention tokens based on saliency, achieving accuracy-efficiency trade-offs suitable for resource-constrained devices.</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/sc-2024/><span class=title>« Prev</span>
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