This paper advances a novel approach that facilitates the location of services and/or digital assets advertised by directories in a Mobile Ad hoc Network. The proposed Service Directory Placement Protocol (SDPP) improves scalability and reduces packet traffic overhead by advancing a multi-directory extension of an earlier approach that relied on the migration of a single directory through the network. This investigation demonstrates that modelling the directory replication problem as a Semi-Markov Decision Problem solved by means of a Reinforcement Learning technique known as Q-learning improves the performance of SDPP

Java embedded systems often include Java middleware classes installed on the client device.

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications.

The NAS Parallel Benchmarks (NPB) are well-known applications with fixed algorithms for evaluating parallel systems and tools. Multicore clusters provide a natural programming paradigm for hybrid programs, whereby OpenMP can be used with the data sharing with the multicores that comprise a node, and MPI can be used with the communication between nodes. In this paper, we use Scalar Pentadiagonal (SP) and Block Tridiagonal (BT) benchmarks of MPI NPB 3.3 as a basis for a comparative approach to implement hybrid MPI/OpenMP versions of SP and BT