- ICAC'11 - A Bayesian Approach to Online Performance Modeling for Database Appliances using Gaussian Models
- Problem: modeling DBMS workloads using Gaussian Process
- Keys:
- It only focuses on modeling workloads and does not propose any application.
- It demonstrate GP can predict quite accurate with small data set.
- SIGMOD'18 - P-Store: An Elastic Database System with Predictive Provisioning
- Motivation: previous work always react after an overloaded event happens.
- Problem: they propose to make machine provisioning decision by predicting the following workloads.
- Keys:
- The target workload must be easy to predict.
- There can be a few distributed transactions.
- IEEE CloudCom'18 - DERP: A Deep Reinforcement Learning Cloud System for Elastic Resource Provisioning
- https://ieeexplore.ieee.org/abstract/document/8590989
- Motivation: previous work can not deal with large input space so we need a learning based method.
- Method: Uses a DQN RL agent to decide when to add/remove machines and how many machines are added/removed to a DBMS cluster.
- Key Points:
- Targeting on NoSQL systems.
- VLDB'19 - iBTune: individualized buffer tuning for large-scale cloud databases
- VLDB'21 - Seagull: An Infrastructure for Load Prediction andOptimized Resource Allocation
- http://www.vldb.org/pvldb/vol14/p154-poppe.pdf
- Problem: to predict the load of a DBMS server and use the predicted info to decide when to backup the DB.
- Keys:
- Focuses on system design
- Assumes the target workload have periodical patterns
- Tried methods to predict workloads
- Singular Spectrum Analysis
- Feed-forward Networks
- Prophet: a software with a model proposed by Facebook to predict time series data with yearly, weekly, and daily patterns.