Paper Info
Title
Workload management for cloud databases via machine learning
Abstract
As elastic IaaS clouds continue to become more cost efficient than on-site datacenters, a wide range of data management applications are migrating to pay-as-you-go cloud computing resources. These diverse applications come with an equally diverse set of performance goals, resource demands, and budget constraints. While existing research has tackled individual tasks such as query placement, scheduling, and resource provisioning to meet these goals and constraints, these techniques fail to provide end-to-end customizable workload management solutions, leading application developers to hand-craft custom heuristics that fit their workload specifications and performance goals. In this vision paper, we argue that workload management challenges can be addressed by leveraging machine learning algorithms. These algorithms can be trained on application-specific properties and performance metrics to automatically learn how to provision resources as well as distribute and schedule the execution of incoming query workloads. Towards this goal, we sketch our vision of WiSeDB, a learning-based service that relies on supervised and reinforcement learning to generate workload management strategies for both static and dynamic workloads.
Year
DOI
Venue
2016
10.1109/ICDEW.2016.7495611
2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)
Keywords
Field
DocType
cloud databases,elastic IaaS clouds,end-to-end customizable workload management solutions,machine learning algorithms,query workloads,WiSeDB,reinforcement learning,supervised learning
Data mining,Active learning (machine learning),Computer science,Artificial intelligence,Reinforcement learning,Online machine learning,Workload,Provisioning,Schedule,Data management,Machine learning,Database,Cloud computing
Conference
Citations 
PageRank 
References 
2
0.37
7
Authors
2
Name
Order
Citations
PageRank
Ryan Marcus16411.51
Olga Papaemmanouil243127.21