New Trends in Parallel and Distributed Simulation: from Many-Cores to Cloud Computing

TL;DR

This paper reviews the state of parallel and distributed simulation techniques in the context of multi-core processors and Cloud computing, proposing an adaptive multi-agent based mechanism to enhance usability and scalability.

Contribution

It introduces an adaptive simulation mechanism based on multi-agent systems to address limitations in existing approaches for multi-core and Cloud environments.

Findings

The proposed mechanism reduces communication costs via component migration.

It supports dynamic addition and removal of nodes during execution.

Simulations can run reliably on unreliable resources with an additional support layer.

Abstract

Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are happening at the ends of the computing spectrum: at the "small" scale, processors now include an increasing number of independent execution units (cores), at the point that a mere CPU can be considered a parallel shared-memory computer; at the "large" scale, the Cloud Computing paradigm allows applications to scale by offering resources from a large pool on a pay-as-you-go model. Multi-core processors and Clouds both require applications to be suitably modified to take advantage of the features they provide. In this paper, we analyze the state of the art of parallel and distributed simulation techniques, and assess their applicability to multi-core architectures or Clouds. It turns out that most of the current approaches exhibit limitations in terms of usability and adaptivity which may hinder their application to these new computing architectures. We propose an adaptive simulation mechanism, based on the multi-agent system paradigm, to partially address some of those limitations. While it is unlikely that a single approach will work well on both settings above, we argue that the proposed adaptive mechanism has useful features which make it attractive both in a multi-core processor and in a Cloud system. These features include the ability to reduce communication costs by migrating simulation components, and the support for adding (or removing) nodes to the execution architecture at runtime. We will also show that, with the help of an additional support layer, parallel and distributed simulations can be executed on top of unreliable resources.