Parallel And Distributed Simulation Research Articles

Security analysis of distributed ledgers and blockchains through agent-based simulation

In this paper,11An early version of this work appeared in Rosa et al. (2019). This paper is an extensively revised and extended version where more than 50% is new material. we describe LUNES-Blockchain, an agent-based simulator of blockchains that relies on Parallel and Distributed Simulation (PADS) techniques to obtain high scalability. The software is organized as a multi-level simulator that permits to simulate a virtual environment, made of many nodes running the protocol of a specific Distributed Ledger Technology (DLT), such as the Bitcoin or the Ethereum blockchains. This virtual environment is executed on top of a lower-level Peer-to-Peer (P2P) network overlay, which can be structured based on different topologies and with a given number of nodes and edges. Functionalities at different levels of abstraction are managed separately, by different software modules and with different time granularity. This allows for accurate simulations, where (and when) it is needed, and enhances the simulation performance. Using LUNES-Blockchain, it is possible to simulate different types of attacks on the DLT. In this paper, we specifically focus on the P2P layer, considering the selfish mining, the 51% attack and the Sybil attack. For which concerns selfish mining and the 51% attack, our aim is to understand how much the hash-rate (i.e. a general measure of the processing power in the blockchain network) of the attacker can influence the outcome of the misbehavior. On the other hand, in the filtering denial of service (i.e. Sybil Attack), we investigate which dissemination protocol in the underlying P2P network makes the system more resilient to a varying number of nodes that drop the messages. The results confirm the viability of the simulation-based techniques for the investigation of security aspects of DLTs.

An automated parallel simulation flow for cyber-physical system design

Parallel and distributed simulation (PDS) is often employed to tackle the computational intensity of system-level simulation of real-world complex embedded and cyber-physical systems (CPSs). However, CPS models comprise heterogeneous components with diverge semantics for which incompatible PDS approaches are developed. We propose an automated PDS flow based on a formal modeling framework—with necessary extensions—targeting heterogeneous embedded and CPS design. The proposed flow characterizes the sequential executable specification of a heterogeneous model and generates a PDS cluster. State-of-the-art graph partitioning methods are adopted and a new extensible constraint-base formulation of the model partitioning problem is developed. The applicability, effectiveness, and scalability of the proposed flow is demonstrated using case studies.

Testbeds And Simulation Tools for SIoT Strategies To Shorten Time-To-Market For SIoT Solutions.

The integration of social networking and Internet of Things (IoT) concepts has attracted a growing interest of researchers and has promised to support new and powerful applications. However, the time it takes from development to the market for SIoT solutions is longer, especially if proper simulations are made to troubleshoot challenges in a timely manner. We propose the use of a multi-thread simulation tool that combines the existing testbeds, such as Network Simulator (Version 2), Devices Profile for Web Services, Objective Modular Network Testbed in Cplus plus, OPTNET, Parallel and Distributed Simulation (PADS), and J-Simtorun simulations in multiple threads to improve performance and save on time when simulating complex SIoT devices that are able to communicate with humans as well as interact with other devices. Through this approach, developers will shorten time-to-market for SIoT solutions and thus afford these solutions to users and improve the quality of life.

MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations

The scalable wireless network simulation poses huge computation challenges as the execution time needed to perform the simulation can be prohibitively high. Parallel and distributed simulation (PADS) approaches have been proposed that use huge memory and high processing power of multiple execution units [i.e., logical processes (LPs)] to handle scalable simulations. Each LP is comprised of a set of simulation entities (SEs) that can interact local or remote SEs. However, the remote communication among SEs and synchronization management across LPs are two main issues related to PADS execution of large-scale simulations. A number of migration techniques have been used to mitigate the problem of high-end remote communication. The problem is that most of the existing migration strategies result in higher number of migrations that ultimately lead to higher computation overhead. In this paper, we propose a migration-based adaptive heuristic algorithm (MAHA). Considering the run-time dynamics of the wireless network simulations, MAHA provides dynamic partitioning of the simulation model to achieve better local communication ratio (LCR). In addition, an adaptive academic simulation cloud platform, namely A-SIM-Cumulus cloud, is deployed for scalable simulations. The MAHA is implemented on A-SIM-Cumulus Cloud and simulations are executed multiple times with different configurations and execution environments. The results with optimum LCR show that the proposed algorithm significantly reduces the number of migrations and achieves a good speedup in terms of parallel (i.e., both multi-core and distributed) execution.

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

SummaryAllocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification‐based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP‐based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang‐scheduling‐based, Makespan‐based, and Max‐Min‐based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication‐sensitive PADS applications, the advantage of ERPA is more significant.

Fault tolerant adaptive parallel and distributed simulation through functional replication

This paper presents FT-GAIA, a software-based fault-tolerant parallel and distributed simulation middleware. FT-GAIA has being designed to reliably handle Parallel And Distributed Simulation (PADS) models, which are needed to properly simulate and analyze complex systems arising in any kind of scientific or engineering field. PADS takes advantage of multiple execution units run in multicore processors, cluster of workstations or HPC systems. However, large computing systems, such as HPC systems that include hundreds of thousands of computing nodes, have to handle frequent failures of some components. To cope with this issue, FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some protection against Byzantine failures, since interaction messages among the simulated entities are replicated as well, so that the receiving entity can identify and discard corrupted messages. Results from an analytical model and from an experimental evaluation show that FT-GAIA provides a high degree of fault tolerance, at the cost of a moderate increase in the computational load of the execution units.

Supply Chain Simulation using Business Process Modeling in Service Oriented Architecture

For supply chain optimization, as a key determinant of strategic resources mobility along the value-added chain, simulation is widely used to test the impact on supply chain performance for the strategic level decisions, such as the number of plants, the modes of transport, or the relocation of warehouses. Traditionally, a single centralized model that encompasses multiple participants in the supply chain is built when optimization of the supply chain through simulation is required. However, due to the heterogeneity of supply chain, a centralized simulation model has a limit to reflect the dynamics of each supply chain participant. This research focuses on the conceptual and the technical issues about the supply chain simulation with a parallel and distributed simulation (PADS) concept. In particular, to ensure the decomposition of supply chain simulation model by each supply chain participant, the simulation framework is proposed using Service Oriented Architecture (SOA) in e-collaboration environment. For a clear proof of concept, the virtual supply chain simulation model is implemented and experienced using business process modeling tools in SOA. Due to the reusable, interoperable, and scalable characteristics of the proposed supply chain simulation framework, various alternatives for supply chin optimization can be generated and evaluated.

Parallel and Distributed Simulation: Methodologies and Techniques

An introduction to the field of Parallel and Distributed Simulation (PADS) is given. The capabilities and limitations of currently used PADS techniques are discussed. A review of the recently developed hybrid and adaptive PADS techniques is also given. Sample performance results of some PADS techniques are presented using a network of workstations.

A visual environment for distributed simulation systems

Parallel and Distributed Simulation (PADS) algorithms are typically categorized to belong to one of two categories. They are either conservative or optimistic with respect to the method of handling causality. Conservative systems strictly preserve causality, while optimistic systems detect and correct causality errors when they occur. Time Warp is the basis of optimistic algorithms where rolling back the simulation clock allows the simulation to correct for errors. The Global Virtual Time (GVT) is the variable that maintains information about simulation progress, termination decision, and for committing input/output data. In this paper the basis for an environment for visualization distributed simulations with time warp on a network of UNIX workstations is presented. The visualization environment provides a graphical overview of simulation processes, and provides insight for algorithm performance. Extensions to the visualizations are also possible for animation of simulation results.