Automated, Elastic Resource Provisioning for NoSQL Clusters Using TIRAMOLA

Author

Tsoumakos, Dimitrios ; Konstantinou, Ioannis ; Boumpouka, Christina ; Sioutas, S. ; Koziris, Nectarios

Author_Institution

Dept. of Inf., Ionian Univ., Greece

fYear

2013

fDate

13-16 May 2013

Firstpage

34

Lastpage

41

Abstract

This work presents TIRAMOLA, a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. TIRAMOLA´s modular architecture and standard API support allows interaction with most current IaaS platforms and increased customization. An extensive experimental evaluation on an HBase cluster confirms our assertions: The system resizes clusters in real-time and adapts its performance through different optimization strategies, different permissible actions, different input and training loads. Besides the automation of the process, it exhibits a learning feature which allows it to make very close to optimal decisions even with input loads 130% larger or alternating 10 times faster compared to the accumulated information.

Keywords

Markov processes; SQL; application program interfaces; cloud computing; pattern clustering; public domain software; resource allocation; virtual machines; HBase cluster; IaaS platforms; Markov decision process; NoSQL cluster automatic resizing; TIRAMOLA modular architecture; VM resources; automated provisioning; cloud-enabled framework; elastic resource provisioning; input load; learning feature; open-source framework; optimization strategy; permissible actions; standard API support; training loads; Decision making; Measurement; Monitoring; Open source software; Optimization; Real-time systems; Training; Cloud Resource Provisioning; Distributed Datastores; Elasticity; Markov Decision Process; NoSQL; Open-source; Policy-based Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on

Conference_Location

Delft

Print_ISBN

978-1-4673-6465-2

Type

conf

DOI

10.1109/CCGrid.2013.45

Filename

6546056