Details
| Original language | English |
|---|---|
| Title of host publication | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
| Pages | 12363-12372 |
| Number of pages | 10 |
| ISBN (electronic) | 9781713835974 |
| Publication status | Published - 18 May 2021 |
| Event | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021 |
Abstract
The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 12363-12372.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems
AU - Luo, Chuan
AU - Qiao, Bo
AU - Xing, Wenqian
AU - Chen, Xin
AU - Zhao, Pu
AU - Du, Chao
AU - Yao, Randolph
AU - Zhang, Hongyu
AU - Wu, Wei
AU - Cai, Shaowei
AU - He, Bing
AU - Rajmohan, Saravanakumar
AU - Lin, Qingwei
PY - 2021/5/18
Y1 - 2021/5/18
N2 - The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.
AB - The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.
UR - http://www.scopus.com/inward/record.url?scp=85130092725&partnerID=8YFLogxK
U2 - 10.1609/aaai.v35i14.17467
DO - 10.1609/aaai.v35i14.17467
M3 - Conference contribution
AN - SCOPUS:85130092725
SP - 12363
EP - 12372
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -