Classical approaches to performance prediction rely on two, typically antithetic, techniques:
Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach …

Over the last years Transactional Memory (TM) gained growing popularity as a simpler,
attractive alternative to classic lock-based synchronization schemes. Recently, the TM …

In this article, we introduce TAS (Transactional Auto Scaler), a system for automating the
elastic scaling of replicated in-memory transactional data grids, such as NoSQL data stores …

In this paper we investigate the issue of automatically identifying the “natural” degree of
parallelism of an application using software transactional memory (STM), ie, the workload …

Processing-In-Memory (PIM) is a novel approach that augments existing DRAM memory
chips with lightweight logic. By allowing to offload computations to the PIM system, this …

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 …

Classical approaches to performance prediction of computer systems rely on two, typically
antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML undertakes …

In this article we exploit a combination of analytical and Machine Learning (ML) techniques
in order to build a performance model allowing to dynamically tune the level of concurrency …

Performance modeling typically relies on two antithetic methodologies: white box models,
which exploit knowledge on system's internals and capture its dynamics using analytical …

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 …