A general‐purpose distributed computing Java middleware

Abstract

Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

PESC -- Parallel Experiment for Sequential Code

“…Nowadays, not all the solutions reviewed in these survey publications remain active. Besides, almost all analyzed solutions do not adhere to the proposal presented here, where some require specific libraries need to be installed for the execution of the user code [of WISCONSIN-MADISON, 2022], or they are built for a particular programming language [Almeida et al, 2019], or the user must rewrite the code based on the requirements defined by the project [Aldinucci et al, 2021]. With the evolution of technology, it is possible to rethink how these desktop grids can be implemented, focusing on user-friendly adoption, portability, and no (or minimum) user-code intrusion to run the computational experiments.…”