{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:38:59Z","timestamp":1767339539232},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Virtual machine (VM) scheduling is one of the critical tasks in cloud computing. Many works have attempted to incorporate machine learning, especially reinforcement learning, to empower VM scheduling procedures. Although improved results are shown in several demo simulators, the performances in real-world scenarios are still underexploited. In this paper, we design a practical VM scheduling platform, i.e., VMAgent, to assist researchers in developing their methods on the VM scheduling problem. VMAgent consists of three components: simulator, scheduler, and visualizer. The simulator abstracts three general realistic scheduling scenarios (fading, recovering, and expansion) based on Huawei Cloud\u2019s scheduling data, which is the core of our platform. Flexible configurations are further provided to make the simulator compatible with practical cloud computing architecture (i.e., Multi Non-Uniform Memory Access) and scenarios. Researchers then need to instantiate the scheduler to interact with the simulator, which is also pre-built in various types (e.g., heuristic, machine learning, and operations research) of scheduling algorithms to speed up the algorithm design. The visualizer, as an auxiliary component of the simulator and scheduler, facilitates researchers to conduct an in-depth analysis of the scheduling procedure and comprehensively compare different scheduling algorithms. We believe that VMAgent would shed light on the AI for the VM scheduling community, and the demo video is presented in https:\/\/bit.ly\/vmagent-demo-video.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/860","type":"proceedings-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:07:08Z","timestamp":1658142428000},"page":"5944-5947","source":"Crossref","is-referenced-by-count":4,"title":["VMAgent: A Practical Virtual Machine Scheduling Platform"],"prefix":"10.24963","author":[{"given":"Junjie","family":"Sheng","sequence":"first","affiliation":[{"name":"East China Normal University"}]},{"given":"Shengliang","family":"Cai","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Haochuan","family":"Cui","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Wenhao","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Yun","family":"Hua","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[{"name":"East China Normal University"}]},{"given":"Wenli","family":"Zhou","sequence":"additional","affiliation":[{"name":"Algorithm Innovation Lab, Cloud BU, Huawei Technologies Co., Ltd."}]},{"given":"Yiqiu","family":"Hu","sequence":"additional","affiliation":[{"name":"Algorithm Innovation Lab, Cloud BU, Huawei Technologies Co., Ltd."}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Alkaid Lab, Cloud BU, Huawei Technologies Co., Ltd"}]},{"given":"Qian","family":"Peng","sequence":"additional","affiliation":[{"name":"Alkaid Lab, Cloud BU, Huawei Technologies Co., Ltd"}]},{"given":"Hongyuan","family":"Zha","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen"}]},{"given":"Xiangfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:11:57Z","timestamp":1658142717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/860"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/860","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}