Revisiting Actor Programming in C++

The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the "C++ Actor Framework". CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts---in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities---that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPGPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime behaviour for up to 64 cores at very low memory footprint, or in the presence of GPUs. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself

Unifying Distributed Processing and Open Hypertext through a Heterogeneous Communication Model

A successful distributed open hypermedia system can be characterised by a scaleable architecture which is inherently distributed. While the architects of distributed hypermedia systems have addressed the issues of providing and retrieving distributed resources, they have often neglected to design systems with the inherent capability to exploit the distributed processing of this information. The research presented in this paper describes the construction and use of an open hypermedia system concerned equally with both of these facets

Coordinating complex problem-solving among distributed intelligent agents

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet