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-

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

Distributed simulation and industry: Potentials and pitfalls

We present the views of five researchers and practitioners of distributed simulation. Collectively we attempt to address what the implications of distributed simulation are for industry. It is hoped that the views contained herein, and the presentations made by the panelists at the 2002 Winter Simulation Conference will raise awareness and stimulate further discussion on the application of distributed simulation methods and technology in an area that is yet to benefit from the arguable economic benefits that this technique promises

LUNES: Agent-based Simulation of P2P Systems (Extended Version)

We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which allows to simulate complex networks composed of a high number of nodes. LUNES is modular, since it splits the three phases of network topology creation, protocol simulation and performance evaluation. This permits to easily integrate external software tools into the main software architecture. The simulation of the interaction protocols among network nodes is performed via a simulation middleware that supports both the sequential and the parallel/distributed simulation approaches. In the latter case, a specific mechanism for the communication overhead-reduction is used; this guarantees high levels of performance and scalability. To demonstrate the efficiency of LUNES, we test the simulator with gossip protocols executed on top of networks (representing peer-to-peer overlays), generated with different topologies. Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011 International Conference on High Performance Computing and Simulation (HPCS 2011

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

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Modeling, Design And Evaluation Of Networking Systems And Protocols Through Simulation

Computer modeling and simulation is a practical way to design and test a system without actually having to build it. Simulation has many benefits which apply to many different domains: it reduces costs creating different prototypes for mechanical engineers, increases the safety of chemical engineers exposed to dangerous chemicals, speeds up the time to model physical reactions, and trains soldiers to prepare for battle. The motivation behind this work is to build a common software framework that can be used to create new networking simulators on top of an HLA-based federation for distributed simulation. The goals are to model and simulate networking architectures and protocols by developing a common underlying simulation infrastructure and to reduce the time a developer has to learn the semantics of message passing and time management to free more time for experimentation and data collection and reporting. This is accomplished by evolving the simulation engine through three different applications that model three different types of network protocols. Computer networking is a good candidate for simulation because of the Internet\u27s rapid growth that has spawned off the need for new protocols and algorithms and the desire for a common infrastructure to model these protocols and algorithms. One simulation, the 3DInterconnect simulator, simulates data transmitting through a hardware k-array n-cube network interconnect. Performance results show that k-array n-cube topologies can sustain higher traffic load than the currently used interconnects. The second simulator, Cluster Leader Logic Algorithm Simulator, simulates an ad-hoc wireless routing protocol that uses a data distribution methodology based on the GPS-QHRA routing protocol. CLL algorithm can realize a maximum of 45% power savings and maximum 25% reduced queuing delay compared to GPS-QHRA. The third simulator simulates a grid resource discovery protocol for helping Virtual Organizations to find resource on a grid network to compute or store data on. Results show that worst-case 99.43% of the discovery messages are able to find a resource provider to use for computation. The simulation engine was then built to perform basic HLA operations. Results show successful HLA functions including creating, joining, and resigning from a federation, time management, and event publication and subscription