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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>CCGrid 2024 Digest | Publish Assistant</title><meta name=keywords content="cloud-computing,distributed-systems,hpc"><meta name=description content="10 papers selected.
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Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis
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Amit Samanta 0001, Ryan Stutsman
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TL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.
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Why notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/ccgrid-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/ccgrid-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/ccgrid-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="CCGrid 2024 Digest"><meta property="og:description" content="10 papers selected.
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Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis Amit Samanta 0001, Ryan Stutsman
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TL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.
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Why notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity."><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="2024-05-06T00:00:00+00:00"><meta property="article:modified_time" content="2024-05-06T00:00:00+00:00"><meta property="article:tag" content="Cloud-Computing"><meta property="article:tag" content="Distributed-Systems"><meta property="article:tag" content="Hpc"><meta name=twitter:card content="summary"><meta name=twitter:title content="CCGrid 2024 Digest"><meta name=twitter:description content="10 papers selected.
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Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis Amit Samanta 0001, Ryan Stutsman
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TL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.
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Why notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity."><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":"CCGrid 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/ccgrid-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"CCGrid 2024 Digest","name":"CCGrid 2024 Digest","description":"10 papers selected.\nFair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis Amit Samanta 0001, Ryan Stutsman\nTL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.\nWhy notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity.\n","keywords":["cloud-computing","distributed-systems","hpc"],"articleBody":"10 papers selected.\nFair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis Amit Samanta 0001, Ryan Stutsman\nTL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.\nWhy notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity.\n→ Read paper SLO-Power: SLO and Power-aware Elastic Scaling for Web Services Mehmet Savasci, Abel Souza, Li Wu, David Irwin 0001 et al.\nTL;DR — SLO-Power co-optimizes SLO compliance and power consumption during elastic scaling of web services, reducing energy use without violating latency targets.\nWhy notable — Jointly chasing SLOs and power budgets is a critical concern for sustainable cloud operations; this work shows the two objectives can be reconciled in a single scaling controller. The approach is directly applicable to cloud autoscaling stacks where energy cost and QoS guarantees are both first-class concerns.\n→ Read paper HAPPIES: a History-Aware Efficient Cloud Resource Overcommitment System Ziwei Huang 0003, Shibo Tang, Zihao Chang, Lin Tan et al.\nTL;DR — HAPPIES uses historical utilization traces to safely overcommit cloud resources, improving cluster efficiency while bounding the risk of SLO violations.\nWhy notable — Resource overcommitment is a primary lever for improving datacenter utilization, and HAPPIES advances the state of the art by making overcommitment decisions history-aware rather than reactive. Its deployment-oriented design makes it immediately relevant to hyperscale cloud operators seeking higher bin-packing ratios.\n→ Read paper COTuner: Joint Optimization of Resource Configuration and Software Parameters for Recurring Streaming Jobs on the Cloud Hui Dou, Shanshan Zhu, Yuxuan Zhou 0005, Yiwen Zhang 0001 et al.\nTL;DR — COTuner simultaneously tunes cloud resource allocation and application-level software knobs for recurring streaming jobs, reducing cost and latency together.\nWhy notable — Streaming workloads on the cloud are poorly served by tools that tune resources and software parameters in isolation; COTuner’s joint search closes this gap. The focus on recurring jobs makes the approach practical, amortizing tuning cost over repeated executions in production pipelines.\n→ Read paper DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud Yoochan Kim, Kihyun Kim, Yonghyeon Cho, Jinwoo Kim et al.\nTL;DR — DeepVM dynamically mixes spot and on-demand VM instances to build cost-efficient, fault-tolerant deep learning training clusters in the cloud.\nWhy notable — Training large models on cloud infrastructure is expensive, and spot instance preemptions are a major obstacle to reliability; DeepVM provides a principled integration strategy that achieves both cost savings and resilience. The approach is practically significant given the rapid growth of cloud-hosted AI training workloads.\n→ Read paper Opportunistic Energy-Aware Scheduling for Container Orchestration Platforms Using Graph Neural Networks Philipp Raith, Gourav Rattihalli, Aditya Dhakal, Sai Rahul Chalamalasetti et al.\nTL;DR — A GNN-based scheduler for Kubernetes-style container orchestration platforms exploits opportunistic energy signals to reduce power consumption without degrading application performance.\nWhy notable — Applying graph neural networks to container scheduling captures the complex topology of cluster resources in a way that heuristic schedulers cannot, while the energy-awareness angle addresses the sustainability imperative facing cloud providers. The work bridges recent ML advances with production container orchestration.\n→ Read paper Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing Yixuan Wang, Abhishek Chandra, Jon B. Weissman\nTL;DR — Jingle leverages real-time IoT device signals to drive predictive autoscaling decisions at the edge, reducing both over-provisioning and latency spikes.\nWhy notable — Edge autoscaling is hampered by the lack of load predictors tuned to IoT event patterns; Jingle fills this gap by treating IoT telemetry as a first-class input to the scaling loop. The result is a tighter edge-cloud integration model with direct relevance to smart-city and industrial IoT deployments.\n→ Read paper XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms Varad Kulkarni, Nikhil Reddy, Tuhin Khare, Harini Mohan et al.\nTL;DR — XFBench is a portable benchmark suite that evaluates FaaS workflow platforms across multiple cloud providers using representative workload patterns.\nWhy notable — The absence of standardized, cross-cloud benchmarks for serverless workflow platforms hinders fair comparison and informed vendor selection; XFBench addresses this directly with a reusable, community-shareable artifact. It covers diverse workflow shapes and exposes platform-specific performance cliffs that single-provider benchmarks miss.\n→ Read paper Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis Jaime Cernuda, Luke Logan, Ana Gainaru, Scott Klasky et al.\nTL;DR — Hades pushes data transformations into the storage layer using context-aware active storage, dramatically reducing I/O traffic and accelerating large-scale scientific data analysis.\nWhy notable — As HPC datasets grow to petabyte scale, moving data to compute becomes the dominant bottleneck; Hades revives and modernizes the active-storage model with context awareness to match modern workflow patterns. The work is highly relevant to HPC-cloud convergence efforts where storage and compute are increasingly disaggregated.\n→ Read paper Workflow Mini-Apps: Portable, Scalable, Tunable \u0026 Faithful Representations of Scientific Workflows Ozgur O. Kilic, Tianle Wang 0001, Matteo Turilli, Mikhail Titov et al.\nTL;DR — Workflow Mini-Apps are compact, parameterizable proxies that faithfully capture the performance behavior of full scientific workflows, enabling portable scheduling research without the overhead of running complete pipelines.\nWhy notable — Scientific workflow scheduling research is bottlenecked by the cost and complexity of running real applications at scale; mini-apps lower this barrier while preserving the key performance characteristics needed for valid scheduler evaluation. The methodology is immediately reusable by the broader distributed workflow community.\n→ Read paper ","wordCount":"934","inLanguage":"en","datePublished":"2024-05-06T00:00:00Z","dateModified":"2024-05-06T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/ccgrid-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">CCGrid 2024 Digest</h1><div class=post-meta><span title='2024-05-06 00:00:00 +0000 UTC'>May 6, 2024</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>10 papers selected.</p><hr><h3 id=fair-efficient-multi-resource-scheduling-for-stateless-serverless-functions-with-anubis>Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis<a hidden class=anchor aria-hidden=true href=#fair-efficient-multi-resource-scheduling-for-stateless-serverless-functions-with-anubis>#</a></h3><p><em>Amit Samanta 0001, Ryan Stutsman</em></p><p><strong>TL;DR</strong> — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.</p><p><strong>Why notable</strong> — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00021>→ Read paper</a></p><hr><h3 id=slo-power-slo-and-power-aware-elastic-scaling-for-web-services>SLO-Power: SLO and Power-aware Elastic Scaling for Web Services<a hidden class=anchor aria-hidden=true href=#slo-power-slo-and-power-aware-elastic-scaling-for-web-services>#</a></h3><p><em>Mehmet Savasci, Abel Souza, Li Wu, David Irwin 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — SLO-Power co-optimizes SLO compliance and power consumption during elastic scaling of web services, reducing energy use without violating latency targets.</p><p><strong>Why notable</strong> — Jointly chasing SLOs and power budgets is a critical concern for sustainable cloud operations; this work shows the two objectives can be reconciled in a single scaling controller. The approach is directly applicable to cloud autoscaling stacks where energy cost and QoS guarantees are both first-class concerns.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00025>→ Read paper</a></p><hr><h3 id=happies-a-history-aware-efficient-cloud-resource-overcommitment-system>HAPPIES: a History-Aware Efficient Cloud Resource Overcommitment System<a hidden class=anchor aria-hidden=true href=#happies-a-history-aware-efficient-cloud-resource-overcommitment-system>#</a></h3><p><em>Ziwei Huang 0003, Shibo Tang, Zihao Chang, Lin Tan <em>et al.</em></em></p><p><strong>TL;DR</strong> — HAPPIES uses historical utilization traces to safely overcommit cloud resources, improving cluster efficiency while bounding the risk of SLO violations.</p><p><strong>Why notable</strong> — Resource overcommitment is a primary lever for improving datacenter utilization, and HAPPIES advances the state of the art by making overcommitment decisions history-aware rather than reactive. Its deployment-oriented design makes it immediately relevant to hyperscale cloud operators seeking higher bin-packing ratios.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00064>→ Read paper</a></p><hr><h3 id=cotuner-joint-optimization-of-resource-configuration-and-software-parameters-for-recurring-streaming-jobs-on-the-cloud>COTuner: Joint Optimization of Resource Configuration and Software Parameters for Recurring Streaming Jobs on the Cloud<a hidden class=anchor aria-hidden=true href=#cotuner-joint-optimization-of-resource-configuration-and-software-parameters-for-recurring-streaming-jobs-on-the-cloud>#</a></h3><p><em>Hui Dou, Shanshan Zhu, Yuxuan Zhou 0005, Yiwen Zhang 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — COTuner simultaneously tunes cloud resource allocation and application-level software knobs for recurring streaming jobs, reducing cost and latency together.</p><p><strong>Why notable</strong> — Streaming workloads on the cloud are poorly served by tools that tune resources and software parameters in isolation; COTuner’s joint search closes this gap. The focus on recurring jobs makes the approach practical, amortizing tuning cost over repeated executions in production pipelines.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00019>→ Read paper</a></p><hr><h3 id=deepvm-integrating-spot-and-on-demand-vms-for-cost-efficient-deep-learning-clusters-in-the-cloud>DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud<a hidden class=anchor aria-hidden=true href=#deepvm-integrating-spot-and-on-demand-vms-for-cost-efficient-deep-learning-clusters-in-the-cloud>#</a></h3><p><em>Yoochan Kim, Kihyun Kim, Yonghyeon Cho, Jinwoo Kim <em>et al.</em></em></p><p><strong>TL;DR</strong> — DeepVM dynamically mixes spot and on-demand VM instances to build cost-efficient, fault-tolerant deep learning training clusters in the cloud.</p><p><strong>Why notable</strong> — Training large models on cloud infrastructure is expensive, and spot instance preemptions are a major obstacle to reliability; DeepVM provides a principled integration strategy that achieves both cost savings and resilience. The approach is practically significant given the rapid growth of cloud-hosted AI training workloads.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00034>→ Read paper</a></p><hr><h3 id=opportunistic-energy-aware-scheduling-for-container-orchestration-platforms-using-graph-neural-networks>Opportunistic Energy-Aware Scheduling for Container Orchestration Platforms Using Graph Neural Networks<a hidden class=anchor aria-hidden=true href=#opportunistic-energy-aware-scheduling-for-container-orchestration-platforms-using-graph-neural-networks>#</a></h3><p><em>Philipp Raith, Gourav Rattihalli, Aditya Dhakal, Sai Rahul Chalamalasetti <em>et al.</em></em></p><p><strong>TL;DR</strong> — A GNN-based scheduler for Kubernetes-style container orchestration platforms exploits opportunistic energy signals to reduce power consumption without degrading application performance.</p><p><strong>Why notable</strong> — Applying graph neural networks to container scheduling captures the complex topology of cluster resources in a way that heuristic schedulers cannot, while the energy-awareness angle addresses the sustainability imperative facing cloud providers. The work bridges recent ML advances with production container orchestration.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00042>→ Read paper</a></p><hr><h3 id=jingle-iot-informed-autoscaling-for-efficient-resource-management-in-edge-computing>Jingle: IoT-Informed Autoscaling for Efficient Resource Management in Edge Computing<a hidden class=anchor aria-hidden=true href=#jingle-iot-informed-autoscaling-for-efficient-resource-management-in-edge-computing>#</a></h3><p><em>Yixuan Wang, Abhishek Chandra, Jon B. Weissman</em></p><p><strong>TL;DR</strong> — Jingle leverages real-time IoT device signals to drive predictive autoscaling decisions at the edge, reducing both over-provisioning and latency spikes.</p><p><strong>Why notable</strong> — Edge autoscaling is hampered by the lack of load predictors tuned to IoT event patterns; Jingle fills this gap by treating IoT telemetry as a first-class input to the scaling loop. The result is a tighter edge-cloud integration model with direct relevance to smart-city and industrial IoT deployments.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00052>→ Read paper</a></p><hr><h3 id=xfbench-a-cross-cloud-benchmark-suite-for-evaluating-faas-workflow-platforms>XFBench: A Cross-Cloud Benchmark Suite for Evaluating FaaS Workflow Platforms<a hidden class=anchor aria-hidden=true href=#xfbench-a-cross-cloud-benchmark-suite-for-evaluating-faas-workflow-platforms>#</a></h3><p><em>Varad Kulkarni, Nikhil Reddy, Tuhin Khare, Harini Mohan <em>et al.</em></em></p><p><strong>TL;DR</strong> — XFBench is a portable benchmark suite that evaluates FaaS workflow platforms across multiple cloud providers using representative workload patterns.</p><p><strong>Why notable</strong> — The absence of standardized, cross-cloud benchmarks for serverless workflow platforms hinders fair comparison and informed vendor selection; XFBench addresses this directly with a reusable, community-shareable artifact. It covers diverse workflow shapes and exposes platform-specific performance cliffs that single-provider benchmarks miss.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00067>→ Read paper</a></p><hr><h3 id=hades-a-context-aware-active-storage-framework-for-accelerating-large-scale-data-analysis>Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis<a hidden class=anchor aria-hidden=true href=#hades-a-context-aware-active-storage-framework-for-accelerating-large-scale-data-analysis>#</a></h3><p><em>Jaime Cernuda, Luke Logan, Ana Gainaru, Scott Klasky <em>et al.</em></em></p><p><strong>TL;DR</strong> — Hades pushes data transformations into the storage layer using context-aware active storage, dramatically reducing I/O traffic and accelerating large-scale scientific data analysis.</p><p><strong>Why notable</strong> — As HPC datasets grow to petabyte scale, moving data to compute becomes the dominant bottleneck; Hades revives and modernizes the active-storage model with context awareness to match modern workflow patterns. The work is highly relevant to HPC-cloud convergence efforts where storage and compute are increasingly disaggregated.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00070>→ Read paper</a></p><hr><h3 id=workflow-mini-apps-portable-scalable-tunable--faithful-representations-of-scientific-workflows>Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows<a hidden class=anchor aria-hidden=true href=#workflow-mini-apps-portable-scalable-tunable--faithful-representations-of-scientific-workflows>#</a></h3><p><em>Ozgur O. Kilic, Tianle Wang 0001, Matteo Turilli, Mikhail Titov <em>et al.</em></em></p><p><strong>TL;DR</strong> — Workflow Mini-Apps are compact, parameterizable proxies that faithfully capture the performance behavior of full scientific workflows, enabling portable scheduling research without the overhead of running complete pipelines.</p><p><strong>Why notable</strong> — Scientific workflow scheduling research is bottlenecked by the cost and complexity of running real applications at scale; mini-apps lower this barrier while preserving the key performance characteristics needed for valid scheduler evaluation. The methodology is immediately reusable by the broader distributed workflow community.</p><p><a href=https://doi.org/10.1109/CCGrid59990.2024.00059>→ Read paper</a></p></div><footer class=post-footer><ul class=post-tags><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/cloud-computing/>Cloud-Computing</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/distributed-systems/>Distributed-Systems</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/hpc/>Hpc</a></li></ul><nav class=paginav><a class=prev href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/osdi-2024/><span class=title>« Prev</span>
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