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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2405.01813 |
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| _version_ | 1866929334704406528 |
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| author | Zhu, Yiwen Tian, Yuanyuan Cahoon, Joyce Krishnan, Subru Agarwal, Ankita Alotaibi, Rana Camacho-Rodríguez, Jesús Chundatt, Bibin Chung, Andrew Dutta, Niharika Fogarty, Andrew Gruenheid, Anja Haynes, Brandon Interlandi, Matteo Iyer, Minu Jurgens, Nick Khushalani, Sumeet Kroth, Brian Kumar, Manoj Leeka, Jyoti Matusevych, Sergiy Mittal, Minni Mueller, Andreas Muthyala, Kartheek Nagulapalli, Harsha Park, Yoonjae Patel, Hiren Pavlenko, Anna Poppe, Olga Ravindran, Santhosh Saur, Karla Sen, Rathijit Suh, Steve Tarafdar, Arijit Waghray, Kunal Wang, Demin Curino, Carlo Ramakrishnan, Raghu |
| author_facet | Zhu, Yiwen Tian, Yuanyuan Cahoon, Joyce Krishnan, Subru Agarwal, Ankita Alotaibi, Rana Camacho-Rodríguez, Jesús Chundatt, Bibin Chung, Andrew Dutta, Niharika Fogarty, Andrew Gruenheid, Anja Haynes, Brandon Interlandi, Matteo Iyer, Minu Jurgens, Nick Khushalani, Sumeet Kroth, Brian Kumar, Manoj Leeka, Jyoti Matusevych, Sergiy Mittal, Minni Mueller, Andreas Muthyala, Kartheek Nagulapalli, Harsha Park, Yoonjae Patel, Hiren Pavlenko, Anna Poppe, Olga Ravindran, Santhosh Saur, Karla Sen, Rathijit Suh, Steve Tarafdar, Arijit Waghray, Kunal Wang, Demin Curino, Carlo Ramakrishnan, Raghu |
| contents | Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range of tasks. However, the cloud brings in both complexity and opportunity. While cloud users can quickly start an application by using various data services, it can be difficult to configure and optimize these services to gain the most value from them. For cloud providers, managing every aspect of an ever-increasing set of data services, while meeting customer SLAs and minimizing operational cost is becoming more challenging. Cloud technology enables the collection of significant amounts of workload traces and system telemetry. With the progress in data science (DS) and machine learning (ML), it is feasible and desirable to utilize a data-driven, ML-based approach to automate various aspects of data services, resulting in the creation of autonomous data services. This paper presents our perspectives and insights on creating autonomous data services on Azure. It also covers the future endeavors we plan to undertake and unresolved issues that still need attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01813 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Building Autonomous Data Services on Azure Zhu, Yiwen Tian, Yuanyuan Cahoon, Joyce Krishnan, Subru Agarwal, Ankita Alotaibi, Rana Camacho-Rodríguez, Jesús Chundatt, Bibin Chung, Andrew Dutta, Niharika Fogarty, Andrew Gruenheid, Anja Haynes, Brandon Interlandi, Matteo Iyer, Minu Jurgens, Nick Khushalani, Sumeet Kroth, Brian Kumar, Manoj Leeka, Jyoti Matusevych, Sergiy Mittal, Minni Mueller, Andreas Muthyala, Kartheek Nagulapalli, Harsha Park, Yoonjae Patel, Hiren Pavlenko, Anna Poppe, Olga Ravindran, Santhosh Saur, Karla Sen, Rathijit Suh, Steve Tarafdar, Arijit Waghray, Kunal Wang, Demin Curino, Carlo Ramakrishnan, Raghu Distributed, Parallel, and Cluster Computing Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range of tasks. However, the cloud brings in both complexity and opportunity. While cloud users can quickly start an application by using various data services, it can be difficult to configure and optimize these services to gain the most value from them. For cloud providers, managing every aspect of an ever-increasing set of data services, while meeting customer SLAs and minimizing operational cost is becoming more challenging. Cloud technology enables the collection of significant amounts of workload traces and system telemetry. With the progress in data science (DS) and machine learning (ML), it is feasible and desirable to utilize a data-driven, ML-based approach to automate various aspects of data services, resulting in the creation of autonomous data services. This paper presents our perspectives and insights on creating autonomous data services on Azure. It also covers the future endeavors we plan to undertake and unresolved issues that still need attention. |
| title | Towards Building Autonomous Data Services on Azure |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.01813 |