Predicting in-memory database performance for automating cluster management tasks

  • Authors:

  • Jan Schaffner;Benjamin Eckart;Dean Jacobs;Christian Schwarz;Hasso Plattner;Alexander Zeier

  • Affiliations:

  • Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482, Germany;Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482, Germany;SAP AG, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany;Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482, Germany;Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482, Germany;Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482, Germany

  • Venue:
  • ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
  • Year:
  • 2011

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Abstract

In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the cluster to ensure that response time goals are met. In so doing, the number of servers in the cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant clustering framework for SAP's in-memory column database TREX.