Paper Info
Title
Spot Virtual Machine Eviction Prediction in Microsoft Cloud
Abstract
BSTRACT Azure Spot Virtual Machines (Spot VMs) utilize unused compute capacity at significant cost savings. They can be evicted when Azure needs the capacity back, therefore suitable for workloads that can tolerate interruptions. A good prediction of Spot VM evictions is beneficial for Azure to optimize capacity utilization and offers users information to better plan Spot VM deployments by selecting clusters to reduce potential evictions. The current in-service cluster-level prediction method ignores the node heterogeneity by aggregating node information. In this paper, we propose a spatial-temporal node-level Spot VM eviction prediction model to capture the inter-node relations and time dependency. The experiments with Azure data show that our node-level eviction prediction model performs better than the node-level and cluster-level baselines.
Year
DOI
Venue
2022
10.1145/3487553.3524229
International World Wide Web Conference
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
17
Name
Order
Citations
PageRank
Fangkai Yang100.34
Bowen Pang200.34
Jue Zhang300.34
Bo Qiao4339.09
Lu Wang514432.99
Camille Couturier600.34
Chetan Bansal700.34
Soumya Ram800.34
Si Qin934.16
Zhen Ma1000.34
Íñigo Goiri1100.34
Eli Cortez1200.34
Senthil Baladhandayutham1300.34
Victor Rühle1400.34
Saravan Rajmohan1501.69
Qingwei Lin1628527.76
Dongmei Zhang171439132.94