AutoAdmin: Self-Tuning Database SystemsTechnology.

Sanjay Agrawal, Nicolas Bruno, Surajit Chaudhuri, and Vivek R Narasayya.

Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. They can connect applications directly to their database.Configuration tuning is vital to optimize the performance of database management system (DBMS). Users no longer have to deploy the dependent middleware to deliver database requests anywhere in the world. Database-as-a-service can replicate and distribute immediately and offer near real-time access to data worldwide.Ĭloud databases can collect, deliver, replicate, and push to the edge all your data using the new hybrid cloud concept. If a customer in Singapore has to wait more than 4 seconds for their mobile application data to be retrieved from a database in New Jersey, that customer is not likely to use that application again. This data is then distributed to the database as a service and replicated to geographic regions across the world. They can break up their data, distribute it, and move it closer to their users.įor example, financial organizations are embracing the hybrid concept by using the database as a central repository for all their disparate data sources, and then delivering this financial data in JSON format. Organizations can continuously optimize the data layer for cost, performance, security, and reach.

This is the path to hybrid cloud that accommodates growing data management needs, not infrastructure needs. Mix and match cloud providers to optimize geographic reach, service level agreements (SLAs), pricing and regulatory requirements.Manage your cloud yourself, or let a provider manage it for you.Clusters can span multiple data centers.Hybrid databases create a distributed hybrid data cloud for increased performance, reach, uptime, mobility, and cost savings: This can result in performance bottlenecks and also prevent applications from running if the connection to the master database is unavailable.Ī cloud database such as IBM Cloudant ® enables you to push database access to the farthest edge of the network, such as mobile devices, remote facilities, sensors, and Internet-enabled goods, so you can scale bigger and enable applications to continue running while offline. The problem is that most databases require updates to occur in a central “master” database. Managing engagement and application data for massive networks of mobile users or remote devices can be a scalability and availability nightmare.

Cloud database management system pdf