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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>TOCS 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="8 papers selected.
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PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation
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Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong et al.
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TL;DR — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator’s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.
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Why notable — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tocs-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/tocs-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/tocs-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="TOCS 2024 Digest"><meta property="og:description" content="8 papers selected.
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PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong et al.
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TL;DR — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator’s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.
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Why notable — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks."><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="TOCS 2024 Digest"><meta name=twitter:description content="8 papers selected.
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PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong et al.
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TL;DR — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator’s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.
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Why notable — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks."><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":"TOCS 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/tocs-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"TOCS 2024 Digest","name":"TOCS 2024 Digest","description":"8 papers selected.\nPMAlloc: A Holistic Approach to Improving Persistent Memory Allocation Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong et al.\nTL;DR — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator\u0026rsquo;s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.\nWhy notable — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks.\n","keywords":[],"articleBody":"8 papers selected.\nPMAlloc: A Holistic Approach to Improving Persistent Memory Allocation Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong et al.\nTL;DR — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator’s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.\nWhy notable — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks.\nBoki: Towards Data Consistency and Fault Tolerance with Shared Logs in Stateful Serverless Computing Zhipeng Jia, Emmett Witchel\nTL;DR — Boki introduces a shared-log abstraction for serverless functions that provides strong consistency and fault tolerance without requiring developers to manage state explicitly.\nWhy notable — It reframes stateful serverless as a log-centric problem, offering a clean systems primitive that substantially simplifies correctness guarantees in function-as-a-service platforms.\nDiciclo: Flexible User-level Services for Efficient Multitenant Isolation Giorgos Kappes, Stergios V. Anastasiadis\nTL;DR — Diciclo provides a user-level framework that lets services customize their isolation mechanisms without kernel modifications, reducing interference among co-located tenants.\nWhy notable — Multitenant isolation in cloud systems is typically a blunt instrument; this work shows that flexible, low-overhead isolation can be achieved entirely in user space.\nSPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis Tuo Li, Jia-Ju Bai, Yulei Sui, Shi-Min Hu\nTL;DR — SPATA applies a summary-based, alias-aware, and path-sensitive typestate analysis to detect resource-management bugs in OS kernels at scale.\nWhy notable — Finding use-after-free and double-free bugs in OS code remains an open challenge; SPATA’s precision improvements over prior static analyses make it a practical tool for kernel hardening.\nTrinity: High-Performance and Reliable Mobile Emulation through Graphics Projection Hao Lin, Zhenhua Li, Di Gao, Yunhao Liu et al.\nTL;DR — Trinity projects GPU rendering workloads from a mobile device onto a remote high-performance GPU, enabling faithful, high-throughput mobile emulation.\nWhy notable — Mobile app testing at scale requires accurate emulation of GPU behavior; Trinity’s graphics-projection design closes a long-standing fidelity gap in mobile emulators.\nOptimizing Resource Management for Shared Microservices: A Scalable System Design Shutian Luo, Chenyu Lin, Kejiang Ye, Guoyao Xu et al.\nTL;DR — This paper presents a scalable resource-management system for shared microservices that reduces interference and improves utilization in large-scale production deployments.\nWhy notable — Microservice co-location is the norm in modern clouds, yet managing their shared resources at scale remains unsolved; this work delivers practical, production-validated answers.\nHardware-Software Collaborative Tiered-Memory Management Framework for Virtualization Sai Sha, Chuandong Li, Xiaolin Wang, Zhenlin Wang et al.\nTL;DR — A hardware-software co-design framework that transparently manages hot/cold data placement across DRAM and slower memory tiers inside virtual machines.\nWhy notable — As CXL-attached and NVM memory tiers become mainstream in data centers, principled tiered-memory management in hypervisors becomes critical; this paper provides a solid baseline.\nComponent-distinguishable Co-location and Resource Reclamation for High-throughput Computing Laiping Zhao, Yushuai Cui, Yanan Yang, Xiaobo Zhou et al.\nTL;DR — This system differentiates micro-components of co-located workloads to reclaim idle resources precisely, boosting overall cluster throughput without violating SLOs.\nWhy notable — Coarse-grained co-location wastes significant cluster capacity; the component-level granularity introduced here sets a new standard for resource-reclamation systems.\n","wordCount":"527","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/tocs-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">TOCS 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>8 papers selected.</p><hr><h3 id=pmalloc-a-holistic-approach-to-improving-persistent-memory-allocation>PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation<a hidden class=anchor aria-hidden=true href=#pmalloc-a-holistic-approach-to-improving-persistent-memory-allocation>#</a></h3><p><em>Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong <em>et al.</em></em></p><p><strong>TL;DR</strong> — PMAlloc redesigns persistent memory allocation end-to-end, co-optimizing the allocator’s data structures, concurrency, and crash consistency to dramatically reduce allocation overhead.</p><p><strong>Why notable</strong> — Persistent memory is still poorly understood at the allocator level; this paper offers a rare holistic treatment that will inform future PM software stacks.</p><hr><h3 id=boki-towards-data-consistency-and-fault-tolerance-with-shared-logs-in-stateful-serverless-computing>Boki: Towards Data Consistency and Fault Tolerance with Shared Logs in Stateful Serverless Computing<a hidden class=anchor aria-hidden=true href=#boki-towards-data-consistency-and-fault-tolerance-with-shared-logs-in-stateful-serverless-computing>#</a></h3><p><em>Zhipeng Jia, Emmett Witchel</em></p><p><strong>TL;DR</strong> — Boki introduces a shared-log abstraction for serverless functions that provides strong consistency and fault tolerance without requiring developers to manage state explicitly.</p><p><strong>Why notable</strong> — It reframes stateful serverless as a log-centric problem, offering a clean systems primitive that substantially simplifies correctness guarantees in function-as-a-service platforms.</p><hr><h3 id=diciclo-flexible-user-level-services-for-efficient-multitenant-isolation>Diciclo: Flexible User-level Services for Efficient Multitenant Isolation<a hidden class=anchor aria-hidden=true href=#diciclo-flexible-user-level-services-for-efficient-multitenant-isolation>#</a></h3><p><em>Giorgos Kappes, Stergios V. Anastasiadis</em></p><p><strong>TL;DR</strong> — Diciclo provides a user-level framework that lets services customize their isolation mechanisms without kernel modifications, reducing interference among co-located tenants.</p><p><strong>Why notable</strong> — Multitenant isolation in cloud systems is typically a blunt instrument; this work shows that flexible, low-overhead isolation can be achieved entirely in user space.</p><hr><h3 id=spata-effective-os-bug-detection-with-summary-based-alias-aware-and-path-sensitive-typestate-analysis>SPATA: Effective OS Bug Detection with Summary-Based, Alias-Aware, and Path-Sensitive Typestate Analysis<a hidden class=anchor aria-hidden=true href=#spata-effective-os-bug-detection-with-summary-based-alias-aware-and-path-sensitive-typestate-analysis>#</a></h3><p><em>Tuo Li, Jia-Ju Bai, Yulei Sui, Shi-Min Hu</em></p><p><strong>TL;DR</strong> — SPATA applies a summary-based, alias-aware, and path-sensitive typestate analysis to detect resource-management bugs in OS kernels at scale.</p><p><strong>Why notable</strong> — Finding use-after-free and double-free bugs in OS code remains an open challenge; SPATA’s precision improvements over prior static analyses make it a practical tool for kernel hardening.</p><hr><h3 id=trinity-high-performance-and-reliable-mobile-emulation-through-graphics-projection>Trinity: High-Performance and Reliable Mobile Emulation through Graphics Projection<a hidden class=anchor aria-hidden=true href=#trinity-high-performance-and-reliable-mobile-emulation-through-graphics-projection>#</a></h3><p><em>Hao Lin, Zhenhua Li, Di Gao, Yunhao Liu <em>et al.</em></em></p><p><strong>TL;DR</strong> — Trinity projects GPU rendering workloads from a mobile device onto a remote high-performance GPU, enabling faithful, high-throughput mobile emulation.</p><p><strong>Why notable</strong> — Mobile app testing at scale requires accurate emulation of GPU behavior; Trinity’s graphics-projection design closes a long-standing fidelity gap in mobile emulators.</p><hr><h3 id=optimizing-resource-management-for-shared-microservices-a-scalable-system-design>Optimizing Resource Management for Shared Microservices: A Scalable System Design<a hidden class=anchor aria-hidden=true href=#optimizing-resource-management-for-shared-microservices-a-scalable-system-design>#</a></h3><p><em>Shutian Luo, Chenyu Lin, Kejiang Ye, Guoyao Xu <em>et al.</em></em></p><p><strong>TL;DR</strong> — This paper presents a scalable resource-management system for shared microservices that reduces interference and improves utilization in large-scale production deployments.</p><p><strong>Why notable</strong> — Microservice co-location is the norm in modern clouds, yet managing their shared resources at scale remains unsolved; this work delivers practical, production-validated answers.</p><hr><h3 id=hardware-software-collaborative-tiered-memory-management-framework-for-virtualization>Hardware-Software Collaborative Tiered-Memory Management Framework for Virtualization<a hidden class=anchor aria-hidden=true href=#hardware-software-collaborative-tiered-memory-management-framework-for-virtualization>#</a></h3><p><em>Sai Sha, Chuandong Li, Xiaolin Wang, Zhenlin Wang <em>et al.</em></em></p><p><strong>TL;DR</strong> — A hardware-software co-design framework that transparently manages hot/cold data placement across DRAM and slower memory tiers inside virtual machines.</p><p><strong>Why notable</strong> — As CXL-attached and NVM memory tiers become mainstream in data centers, principled tiered-memory management in hypervisors becomes critical; this paper provides a solid baseline.</p><hr><h3 id=component-distinguishable-co-location-and-resource-reclamation-for-high-throughput-computing>Component-distinguishable Co-location and Resource Reclamation for High-throughput Computing<a hidden class=anchor aria-hidden=true href=#component-distinguishable-co-location-and-resource-reclamation-for-high-throughput-computing>#</a></h3><p><em>Laiping Zhao, Yushuai Cui, Yanan Yang, Xiaobo Zhou <em>et al.</em></em></p><p><strong>TL;DR</strong> — This system differentiates micro-components of co-located workloads to reclaim idle resources precisely, boosting overall cluster throughput without violating SLOs.</p><p><strong>Why notable</strong> — Coarse-grained co-location wastes significant cluster capacity; the component-level granularity introduced here sets a new standard for resource-reclamation systems.</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/tcc-2024/><span class=title>« Prev</span>
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