Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Cassandra: a decentralized structured storage system
ACM SIGOPS Operating Systems Review
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
An evaluation of alternative architectures for transaction processing in the cloud
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Automated control for elastic storage
Proceedings of the 7th international conference on Autonomic computing
Elastic Site: Using Clouds to Elastically Extend Site Resources
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Flexible use of cloud resources through profit maximization and price discrimination
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
On the elasticity of NoSQL databases over cloud management platforms
Proceedings of the 20th ACM international conference on Information and knowledge management
On estimating actuation delays in elastic computing systems
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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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.