Self-tuning in Distributed Transactional Memory

Many different mechanisms have been developed to implement Distributed Transactional Memory (DTM). Unfortunately, there is no “one-size-fits-all” design that offers the desirable performance across all possible workloads and scales. In fact, the performance of these mechanisms is affected by a number of intertwined factors that make it hard, or even impossible, to statically configure a DTM platform for optimal performance. These observations have motivated the emergence of self-tuning schemes for automatically adapting the algorithms and parameters used by the main building blocks of DTM systems. This chapter surveys existing research in the area of autonomic DTM design, with a focus on the approaches aimed at answering the following two fundamental questions: how many resources (number of nodes, etc.) should a DTM platform be provisioned with, and which protocols should be used to ensure data consistency.