Distributed Transactional Memory (DTM) can play a fundamental role in the coordination of participants in edge clouds as a support for mobile distributed applications. DTM emerges as a concurrency mechanism aimed at simplifying distributed programming by allowing groups of operations to execute atomically, mirroring the well-known transaction model of relational databases. In spite of recent studies showing that partial replication approaches can present gains in the scalability of DTMs by reducing the amount of data stored at each node, most DTM solutions follow a full replication scheme. The few partial replicated DTM frameworks either follow a random or round-robin algorithm for distributing data onto partial replication groups. In order to overcome the poor performance of these schemes, this paper investigates policies to extend the DTM to efficiently and dynamically map resources on partial replication groups. The goal is to understand if a dynamic service that constantly evaluates the data mapped into partial replicated groups can contribute to improve DTM based systems performance.
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