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Elastic architectures and the "pay-as-you-go" resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing...
In this paper we explore machine-learning approaches for dynamically selecting the well suited amount of concurrent threads in applications relying on Software Transactional Memory (STM). Specifically, we present a solution that dynamically shrinks or enlarges the set of input features to be exploited by the machine-learner. This allows for tuning the concurrency level while also minimizing the overhead...
Software Transactional Memory (STM) is recognized as an effective programming paradigm for concurrent applications. On the other hand, a core problem to cope with in STM deals with (dynamically) regulating the degree of concurrency, in order to deliver optimal performance. We address this problem by proposing a self-regulation approach of the concurrency level, which relies on a parametric analytical...
In-memory transactional data grids have revealed extremely suited for cloud based environments, given that they well fit elasticity requirements imposed by the pay-as-you-go cost model. Particularly, the non-reliance on stablestorage devices simplifies dynamic resize of these platforms, which typically only involves setting up (or shutting down) some data-cache instance. On the other hand, defifining...
One of the problems of Software-Transactional-Memory (STM) systems is the performance degradation that can be experienced when applications run with a non-optimal concurrency level, namely number of concurrent threads. When this level is too high a loss of performance may occur due to excessive data contention and consequent transaction aborts. Conversely, if concurrency is too low, the performance...
Cloud computing represents a cost-effective paradigm to deploy a wide class of large-scale distributed applications, for which the pay-per-use model combined with automatic resource provisioning promise to reduce the cost of dependability and scalability. However, a key challenge to be addressed to materialize the advantages promised by Cloud computing is the design of effective auto-scaling and self-tuning...
We present an analytical performance modeling approach for concurrency control algorithms in the context of Software Transactional Memories (STMs). We consider a realistic execution pattern where each thread alternates the execution of transactional and non-transactional code portions. Our model captures dynamics related to the execution of both (i) transactional read/write memory accesses and (ii)...
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