Actors vs Shared Memory: two models at work on Big Data application frameworks

This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications. The paper compares the two models by analyzing a couple of concrete projects based on the MapReduce and Bulk Synchronous Parallel algorithmic schemes. Both projects are doubly implemented on two concrete platforms: Akka Cluster and Managed X10. The result is both a conceptual comparison of models in the Big Data Analytics scenario, and an experimental analysis based on concrete executions on a cluster platform

Debugging real-time software in a host-target environment

A common paradigm for the development of process-control or embedded computer software is to do most of the implementation and testing on a large host computer, then retarget the code for final checkout and production execution on the target machine. The host machine is usually large and provides a variety of program development tools, while the target may be a small, bare machine. A difficulty with the paradigm arises when the software developed has real-time constraints and is composed of multiple communicating processes. If a test execution on the target fails, it may be exceptionally tedious to determine the cause of the failure. Host machine debuggers cannot normally be applied, because the same program processing the same data will frequently exhibit different behavior on the host. Differences in processor speed, scheduling algorithm, and the like, account for the disparity. This paper proposes a partial solution to this problem, in which the errant execution reconstructed and made amenable to source language level debugging on the host. The solution involves the integrated application of a static concurrency analyzer, an interactive interpreter, and a graphical program visualization aid. Though generally applicable, the solution is described here in the context of multi-tasked real-time Ada* programs

Visual Execution Analysis for Multiagent Systems

Multiagent systems have become increasingly important in developing complex software systems. Multiagent systems introduce collective intelligence and provide benefits such as flexibility, scalability, decentralization, and increased reliability. A software agent is a high-level software abstraction that is capable of performing given tasks in an environment without human intervention. Although multiagent systems provide a convenient and powerful way to organize complex software systems, developing such system is very complicated. To help manage this complexity this research develops a methodology and technique for analyzing, monitoring and troubleshooting multiagent systems execution. This is accomplished by visualizing a multiagent system at multiple levels of abstraction to capture the relationships and dependencies among the agents