{"type":"ScholarlyArticle","title":"Intelligent Virtual Machine Provisioning in Cloud Computing","authors":[{"name":"Chuan Luo","slug":"chuan-luo","given":"Chuan","family":"Luo","affiliation":"Microsoft Research , China"},{"name":"Bo Qiao","slug":"bo-qiao","given":"Bo","family":"Qiao","affiliation":"Microsoft Research , China"},{"name":"Xin Chen","slug":"xin-chen","given":"Xin","family":"Chen","affiliation":"Microsoft Research , China"},{"name":"Pu Zhao","slug":"pu-zhao","given":"Pu","family":"Zhao","affiliation":"Microsoft Research , China"},{"name":"Randolph Yao","slug":"randolph-yao","given":"Randolph","family":"Yao","affiliation":"Microsoft Azure , United States"},{"name":"Hongyu Zhang","slug":"hongyu-zhang","given":"Hongyu","family":"Zhang","affiliation":"The University of Newcastle , Australia"},{"name":"Wei Wu","slug":"wei-wu","given":"Wei","family":"Wu","affiliation":"University of Technology Sydney , Australia"},{"name":"Andrew Zhou","slug":"andrew-zhou","given":"Andrew","family":"Zhou","affiliation":"Microsoft Office , China"},{"name":"Qingwei Lin","slug":"qingwei-lin","given":"Qingwei","family":"Lin","affiliation":"Microsoft Research , China"}],"abstract":"Virtual machine (VM) provisioning is a common and critical problem in cloud computing. In industrial cloud platforms, there are a huge number of VMs provisioned per day. Due to the complexity and resource constraints, it needs to be carefully optimized to make cloud platforms effectively utilize the resources. Moreover, in practice, provisioning a VM from scratch requires fairly long time, which would degrade the customer experience. Hence, it is advisable to provision VMs ahead for upcoming demands. In this work, we formulate the practical scenario as the predictive VM provisioning (PreVMP) problem, where upcoming demands are unknown and need to be predicted in advance, and then the VM provisioning plan is optimized based on the predicted demands. Further, we propose Uncertainty-Aware Heuristic Search (UAHS) for solving the PreVMP problem. UAHS first models the prediction uncertainty, and then utilizes the prediction uncertainty in optimization. Moreover, UAHS leverages Bayesian optimization to interact prediction and optimization to improve its practical performance. Extensive experiments show that UAHS performs much better than state-of-the-art competitors on two public datasets and an industrial dataset. UAHS has been successfully applied in Microsoft Azure and brought practical benefits in real-world applications.","doi":"10.24963/ijcai.2020/208","url":"https://ijcai.org/proceedings/2020/208","pdf":"https://ijcai.org/proceedings/2020/0208.pdf","markdown":"https://ijcai.org/proceedings/2020/208.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/208","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Heuristic Search and Game Playing","track":"Main track","number":208,"pages":{"first":1495,"last":1502},"keywords":["Heuristic Search and Game Playing: Heuristic Search","Heuristic Search and Game Playing: Heuristic Search and Machine Learning","Multidisciplinary Topics and Applications: Autonomic Computing","Heuristic Search and Game Playing: Combinatorial Search and Optimisation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24550/file_24550.m3u8","length_sec":282,"provider":"hls"}],"references":[{"position":1,"raw":"[Box et al., 2015] George E. 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