{"id":938859,"date":"2023-05-12T09:48:30","date_gmt":"2023-05-12T16:48:30","guid":{"rendered":""},"modified":"2023-05-12T09:48:32","modified_gmt":"2023-05-12T16:48:32","slug":"microsoft-at-eurosys-2023-systems-innovation-across-the-stack-to-help-support-an-easier-faster-safer-and-smarter-cloud","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/microsoft-at-eurosys-2023-systems-innovation-across-the-stack-to-help-support-an-easier-faster-safer-and-smarter-cloud\/","title":{"rendered":"Microsoft at EuroSys 2023: Systems innovation across the stack to help support an easier, faster, safer, and smarter cloud"},"content":{"rendered":"\n
\"EuroSys<\/figure>\n\n\n\n

EuroSys 2023 (opens in new tab)<\/span><\/a> is the premier systems conference in Europe, and 2023 marks its 18th edition. Sponsored by ACM SIGOPS Europe and hosted May 8 to May 12, the conference covers a wide range of topics, including operating systems, real-time and networked systems, storage and middleware, and distributed, parallel, and embedded computing, as well as their implications for applications and hardware.<\/p>\n\n\n\n

As in previous years, Microsoft has a strong presence in the conference, drawing from research and production teams in Asia, Europe, and the United States, including Azure Systems Research<\/a>, in collaboration with many universities. This work spans areas including systems for machine learning, serverless computing, datacenter networking, caching, and debugging. We\u2019re also participating in several of the associated workshops and in key aspects of the organization, including Senior Principal Researcher Dushyanth Narayanan<\/a> as the program co-chair for the main conference.<\/p>\n\n\n\n

Here are some of the highlights (see below for more information about Microsoft at EuroSys, including the authors of the published papers):<\/p>\n\n\n\n

Datacenter networking<\/strong><\/p>\n\n\n\n

The paper “Saba: Rethinking Datacenter Network Allocation from Application\u2019s Perspective<\/a>” proposes allocating datacenter network bandwidth according to applications\u2019 sensitivity to bandwidth, achieving significant performance gains compared to fair sharing. In “FlexPass: A Case for Flexible Credit-based Transport for Datacenter Networks<\/a>,” the authors make the case to incrementally deploy new proactive, credit-based transport protocols in the datacenter.<\/p>\n\n\n\n

Serverless computing<\/strong><\/p>\n\n\n\n

In serverless computing, the paper “Palette Load Balancing: Locality Hints for Serverless Functions<\/a>” proposes adding locality to Function-as-a-Service (FaaS) serverless systems, closing the performance gap between serverful data-intensive applications and their serverless implementation. In “With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions<\/a>,” the authors revisit the FaaS interface and find that correctly choosing memory, CPU, and architecture for each serverless function can allow both providers and customers to improve cost and performance. Finally, in “Groundhog: Efficient Request Isolation in FaaS<\/a>,” the authors present a system that enables efficient snapshots for better isolation between function invocations. Microsoft is also represented in the SErverless Systems, Applications and MEthodologies (SESAME) workshop (opens in new tab)<\/span><\/a> with a keynote and work-in-progress paper.<\/p>\n\n\n\n

Concurrency debugging<\/strong><\/p>\n\n\n\n

In “WAFFLE: Exposing Memory Ordering Bugs Efficiently with Active Delay Injection<\/a>,” the authors tackle the difficult problem of finding memory ordering bugs, a type of concurrency bug caused by incorrect timing between a memory access to a particular object and the object\u2019s initialization or deallocation. Their proposed tool uses delay injection techniques and, through key innovations, can expose more bugs with less overhead than the state of the art.<\/p>\n\n\n\n

Systems for machine learning<\/strong><\/p>\n\n\n\n

In “SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters<\/a>,” the proposed framework treats cache and remote I\/O as first-class resources and can integrate different state-of-the-art deep learning scheduling policies in a unified scheduling framework.\u202f<\/p>\n\n\n\n

Caching<\/strong><\/p>\n\n\n\n

In “FrozenHot Cache: Rethinking Cache Management for Modern Hardware<\/a>,” the authors introduce a generic approach to improve the scalability of traditional list-based caches, such as least recently used (LRU), by separating the objects into two regions: a frozen region that serves requests for hot objects with minimal latency by eliminating promotion and locking and a regular dynamic region that uses the existing cache design to achieve workload adaptivity. <\/p>\n\n\n\n

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Microsoft papers published at EuroSys with their authors:<\/p>\n\n\n\n

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  1. Saba: Rethinking Datacenter Network Allocation from Application\u2019s Perspective<\/a>
    M.R. Siavash Katebzadeh, University of Edinburgh<\/em>;     Paolo Costa<\/a>, Microsoft Research<\/em>; Boris Grot, University of Edinburgh<\/em><\/li>\n\n\n\n
  2. FlexPass: A Case for Flexible Credit-based Transport for Datacenter Networks<\/a>
    Hwijoon Lim, Jaehong Kim, KAIST<\/em>; Inho Cho, MIT CSAIL<\/em>; Keon Jang, MPI-SWS, Rubrik<\/em>;     Wei Bai<\/a>, Microsoft Research<\/em>; Dongsu Han, KAIST<\/em><\/li>\n\n\n\n
  3. Palette Load Balancing: Locality Hints for Serverless Functions<\/a>
    Mania Abdi, Northeastern University<\/em>; Samuel Ginzburg, Princeton<\/em>; Xiayue Charles Lin, Anyscale<\/em>; Jose Faleiro, unaffiliated<\/em>; Gohar Irfan Chaudhry,     \u00cd\u00f1igo Goiri<\/a>, Ricardo Bianchini<\/a>, Daniel S. Berger<\/a>, Rodrigo Fonseca<\/a>, Azure Systems Research<\/em><\/li>\n\n\n\n
  4. With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions<\/a>
    Muhammad Bilal, Instituto Superior T\u00e9cnico (ULisboa), INESC-ID, UCLouvain<\/em>; Marco Canini, KAUST<\/em>;     Rodrigo Fonseca<\/a>, Azure Systems Research<\/em>; Rodrigo Rodrigues, Instituto Superior T\u00e9cnico (ULisboa), INESC-ID<\/em><\/li>\n\n\n\n
  5. Groundhog: Efficient Request Isolation in FaaS<\/a>
    Mohamed Alzayat, Max Planck Institute for Software Systems (MPI-SWS)<\/em>;     Jonathan Mace<\/a>, Microsoft Research<\/em>; Peter Druschel, Deepak Garg, Max Planck Institute for Software Systems (MPI-SWS)<\/em><\/li>\n\n\n\n
  6. WAFFLE: Exposing Memory Ordering Bugs Efficiently with Active Delay Injection<\/a>
    Bogdan Alexandru Stoica, Shan Lu, University of Chicago<\/em>;     Madanlal Musuvathi<\/a>, Suman Nath<\/a>, Microsoft Research<\/em><\/li>\n\n\n\n
  7. SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters<\/a>
    Hanyu Zhao, Peking University<\/em>;     Zhenhua Han<\/a>, Microsoft Research<\/em>; Zhi Yang, Peking University<\/em>; Quanlu Zhang<\/a>, Microsoft Research<\/em>; Mingxia Li, USTC<\/em>; Fan Yang<\/a>, Qianxi Zhang<\/a>, Microsoft Research<\/em>; Binyang Li, Microsoft<\/em>; Yuqing Yang<\/a>, Lili Qiu<\/a>, Microsoft Research<\/em>; Lintao Zhang, BaseBit Technologies<\/em>; Lidong Zhou<\/a>, Microsoft Research<\/em><\/li>\n\n\n\n
  8. FrozenHot Cache: Rethinking Cache Management for Modern Hardware<\/a>
    Ziyue Qiu, University of Science and Technology of China, Microsoft Research, Carnegie Mellon University<\/em>; Juncheng Yang, Carnegie Mellon University<\/em>; Juncheng Zhang, University of Science and Technology of China<\/em>; Cheng Li, University of Science and Technology of China, Anhui Province Key Laboratory of High Performance Computing<\/em>; Xiaosong Ma, Qatar Computing Research Institute, HBKU<\/em>;     Qi Chen<\/a>, Mao Yang<\/a>, Microsoft Research<\/em>; Yinlong Xu, University of Science and Technology of China, Anhui Province Key Laboratory of High Performance Computing<\/em><\/li>\n<\/ol>\n\n\n\n

    EuroSys 2023 Organization Committee<\/strong>:<\/p>\n\n\n\n