TIRAMOLA: Elastic nosql provisioning through a cloud management platform

Konstantinou, I; Angelou, E; Tsoumakos, D; Boumpouka, C; Koziris, N; Sioutas, S

dc.contributor.author	Konstantinou, I	en
dc.contributor.author	Angelou, E	en
dc.contributor.author	Tsoumakos, D	en
dc.contributor.author	Boumpouka, C	en
dc.contributor.author	Koziris, N	en
dc.contributor.author	Sioutas, S	en
dc.date.accessioned	2014-03-01T02:54:03Z
dc.date.available	2014-03-01T02:54:03Z
dc.date.issued	2012	en
dc.identifier.issn	07308078	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36562
dc.subject	automatic cluster resize	en
dc.subject	cloud monitoring	en
dc.subject	elasticity	en
dc.subject	markov decision process	en
dc.subject	nosql	en
dc.subject	open-source	en
dc.subject.other	automatic cluster resize	en
dc.subject.other	Cloud monitoring	en
dc.subject.other	Markov Decision Processes	en
dc.subject.other	nosql	en
dc.subject.other	Open-source	en
dc.subject.other	Elasticity	en
dc.subject.other	Markov processes	en
dc.subject.other	Optimization	en
dc.title	TIRAMOLA: Elastic nosql provisioning through a cloud management platform	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/2213836.2213943	en
heal.identifier.secondary	http://dx.doi.org/10.1145/2213836.2213943	en
heal.publicationDate	2012	en
heal.abstract	NoSQL databases focus on analytical processing of large scale datasets, offering increased scalability over commodity hardware. One of their strongest features is elasticity, which allows for fairly portioned premiums and high-quality performance. Yet, the process of adaptive expansion and contraction of resources usually involves a lot of manual effort, often requiring the definition of the conditions for scaling up or down to be provided by the users. To date, there exists no open-source system for automatic resizing of NoSQL clusters. In this demonstration, we present TIRAMOLA, a modular, cloud-enabled framework for monitoring and adaptively resizing NoSQL clusters. Our system incorporates a decision-making module which allows for optimal cluster resize actions in order to maximize any quantifiable reward function provided together with life-long adaptation to workload or infrastructural changes. The audience will be able to initiate HBase clusters of various sizes and apply varying workloads through multiple YCSB clients. The attendees will be able to watch, in real-time, the system perform automatic VM additions and removals as well as how cluster performance metrics change relative to the optimization parameters of their choice. © 2012 ACM.	en
heal.journalName	Proceedings of the ACM SIGMOD International Conference on Management of Data	en
dc.identifier.doi	10.1145/2213836.2213943	en
dc.identifier.spage	725	en
dc.identifier.epage	728	en