Concurrency control and recovery in database systems
Concurrency control and recovery in database systems
Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem
Communications of the ACM
A critique of ANSI SQL isolation levels
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The dangers of replication and a solution
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Direct execution models of processor behavior and performance
WSC '87 Proceedings of the 19th conference on Winter simulation
Sinfonia: a new paradigm for building scalable distributed systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
A toolkit for modelling and simulating data Grids: an extension to GridSim
Concurrency and Computation: Practice & Experience
Cassandra: a decentralized structured storage system
ACM SIGOPS Operating Systems Review
DISC'06 Proceedings of the 20th international conference on Distributed Computing
Automated simulation-based capacity planning for enterprise data fabrics
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Integrated tools for the simulation analysis of peer-to-peer backup systems
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
ICDCS '12 Proceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems
Transactional auto scaler: elastic scaling of in-memory transactional data grids
Proceedings of the 9th international conference on Autonomic computing
Auto-tuning of Cloud-Based In-Memory Transactional Data Grids via Machine Learning
NCCA '12 Proceedings of the 2012 Second Symposium on Network Cloud Computing and Applications
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One reason for the success of in-memory (transactional) data grids lies on their ability to fit elasticity requirements imposed by the cloud oriented pay-as-you-go cost model. In fact, by relying on in-memory data maintenance, these platforms can be dynamically resized by simply setting up (or shutting down) instances of so called data cache servers. However, defining the well suited amount of cache servers to be deployed, and the degree of in-memory replication of slices of data, in order to optimize reliability/availability and performance tradeoffs, is far from being a trivial task. To cope with this issue, in this article we present a framework for high performance simulation of in-memory data grid systems, which can be employed as a support for timely what-if analysis and exploration of the effects of reconfiguration strategies. The framework consists of a discrete event simulation library modeling differentiated data grid components in a modular fashion, which allows easy (re)-modeling of different data grid architectures (e.g. characterized by different concurrency control schemes). Also, the library has been designed to be layered on top of the open source ROOT-Sim parallel simulation engine, natively offering facilities for optimized resource usage in the context of model execution on top of multi-core and cluster based architectures. Finally, instances of data-grid models supported by the framework have been validated against real measurements obtained by deploying the Infinispan data grid onto Amazon EC2 virtual clusters, and running the well known TPC-C benchmark. By the experiments we demonstrate closeness of simulation outputs and real measurements, while jointly showing extreme scalability of the framework, in terms of speedup and ability to manage extremely large data grid models.