Paper Info
Title
AidOps: a data-driven provisioning of high-availability services in cloud.
Abstract
The virtualization of services with high-availability requirements calls to revisit traditional operation and provisioning processes. Providers are realizing services in software on virtual machines instead of using dedicated appliances to dynamically adjust service capacity to changing demands. Cloud orchestration systems control the number of service instances deployed to make sure each service has enough capacity to meet incoming workloads. However, determining the suitable build-out of a service is challenging as it takes time to install new instances and excessive re-configurations (i.e. scale in/out) can lead to decreased stability. In this paper we present AidOps, a cloud orchestration system that leverages machine learning and domain-specific knowledge to predict the traffic demand, optimizing service performance and cost. AidOps does not require a conservative provisioning of services to cover for the worst-case demand and significantly reduces operational costs while still fulfilling service quality expectations. We have evaluated our framework with real traffic using an enterprise application and a communication service in a private cloud. Our results show up to 4X improvement in service performance indicators compared to existing orchestration systems. AidOps achieves up to 99.985% availability levels while reducing operational costs at least by 20%.
Year
DOI
Venue
2017
10.1145/3127479.3129250
SoCC '17: ACM Symposium on Cloud Computing Santa Clara California September, 2017
Keywords
Field
DocType
Cloud orchestration, workload forecasting, high-availability
Virtualization,Service quality,Computer science,Computer network,Provisioning,Real-time computing,Service level requirement,High availability,Orchestration (computing),Service delivery framework,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-5028-0
0
0.34
References 
Authors
34
5
Name
Order
Citations
PageRank
Diego Lugones1359.77
Jordi Arjona Aroca21187.06
Yue Jin3102.96
Alessandra Sala4214.29
Volker Hilt548041.90