Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)

In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the everything as a service paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul (Turkey), IEEE, July 2011. ISBN 978-1-61284-382-

The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures

Parallel Discrete Event Simulation with Erlang

Discrete Event Simulation (DES) is a widely used technique in which the state of the simulator is updated by events happening at discrete points in time (hence the name). DES is used to model and analyze many kinds of systems, including computer architectures, communication networks, street traffic, and others. Parallel and Distributed Simulation (PADS) aims at improving the efficiency of DES by partitioning the simulation model across multiple processing elements, in order to enabling larger and/or more detailed studies to be carried out. The interest on PADS is increasing since the widespread availability of multicore processors and affordable high performance computing clusters. However, designing parallel simulation models requires considerable expertise, the result being that PADS techniques are not as widespread as they could be. In this paper we describe ErlangTW, a parallel simulation middleware based on the Time Warp synchronization protocol. ErlangTW is entirely written in Erlang, a concurrent, functional programming language specifically targeted at building distributed systems. We argue that writing parallel simulation models in Erlang is considerably easier than using conventional programming languages. Moreover, ErlangTW allows simulation models to be executed either on single-core, multicore and distributed computing architectures. We describe the design and prototype implementation of ErlangTW, and report some preliminary performance results on multicore and distributed architectures using the well known PHOLD benchmark.Comment: Proceedings of ACM SIGPLAN Workshop on Functional High-Performance Computing (FHPC 2012) in conjunction with ICFP 2012. ISBN: 978-1-4503-1577-

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

DynCNET: a negotiation and coordination protocol for dynamic task assignment.

Task assignment in Multi-Agent Systems is a complex coordination problem, especially in systems that operate under dynamic and changing conditions. Adaptive task assignment is used to handle these dynamic and changing circumstances. This technical document describes an adaptive task assignment protocol, DynCNET which is an extension of the Contract Net Protocol. In this document, the DynCNET protocol will be build step by step, starting from the Contract Net protocol. We will add dynamic task assignment, synchronization of abort messages and scope handling. The final result will be the DynCNET protocol with support for synchronization of abort messages and scope handling.

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption