Workload characterization and prediction in the cloud: A multiple time series approach

By Arijit Khan, Xifeng Yan, Shu Tao, Nikos Anerousis. 2012 IEEE Network Operations and Management Symposium, 2012

332 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/02b2d7015a2eceb929132142d31445eceed69208

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mark Russinovich
2. Ning Liu
3. Ricardo Bianchini
4. Yanzhi Wang
5. Zhiyuan Xu
6. Jian Tang
7. Anand Bonde
8. Sheng Lin

Citations in the corpus

• A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017, background): “Due to the time-variance of workloads [1,2], it is desirable to perform cloud resource allocation and management in an online adaptive manner.”
• Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms (2017, background): “These works predict resource demand, resource utilization, or job/task length for provisioning or scheduling purposes [4, 9, 12, 14, 23].”