Autonomous, failure-resilient orchestration of distributed discrete event simulations

Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of events and conditions provides a more nuanced model, but also increases its computational footprint. To manage these processing requirements in a scalable manner, discrete event simulations can be distributed across multiple computing resources. Orchestrating the simulations in a distributed setting involves coping with resource uncertainty. We consider three key aspects of resource uncertainty: resource failures, heterogeneity, and slowdowns. Each of these aspects is managed autonomously, which involves making accurate predictions of future execution times and latencies while also accounting for differences in hardware capabilities and dynamic resource consumption profiles. Further complicating matters, individual tasks within the simulation are stateful and stochastic, requiring inter-task communication and synchronization to produce accurate outcomes. We deal with these challenges through intelligent state collection and migration, active resource monitoring, and empirical evaluation of resource capabilities under changing conditions. To underscore the viability of our solution, we provide benchmarks using a production discrete event simulation that can simultaneously sustain failures, manage resource heterogeneity, and handle slowdowns while being orchestrated by our framework.

The emergence of Cloud Computing has brought new dimension to the world of information technology. Even though Cloud Computing provide various benefits like agility, on-demand provisioning of resources, reduced cost, multi-tenancy etc., there are risks and flaws associated with it. One key research challenge in Cloud Computing is to ensure continuous reliability and guaranteed availability of resources provided by it. So there is a need for a robust Fault Tolerant (FT) system in Cloud Computing. To better understand FT in Cloud Computing, it is essential to understand the different types of faults. In this paper, we highlight the basic concepts of fault tolerance by understanding the different FT policies like Reactive FT policy and Proactive FT policy and the associated FT techniques used on different types of faults. A study on various fault tolerant methods, algorithms, frameworks etc., has been carried out which are developed and implemented by research experts in this field. This is an area where lot of research is happening and these studies will guide us to build a robust FT technique in Cloud.

In this paper, we present an approach that supports simultaneously applying different fault tolerance policies in multi-agent organizations. The main strategy of our approach is to implement fault tolerance policies as reusable agent plans using HTN (Hierarchical Task Network) formalism. In this way, different fault tolerance policies such as static and adaptive ones can be implemented as different plans. In a static fault tolerance policy, all parameters related to the fault tolerance are set by a programmer before run-time. However, an adaptive fault tolerance policy requires dynamically adapting resource allocation and replication mechanisms by monitoring the system. Monitoring of a system brings some cost to the system. If all agents in an organization apply the adaptive fault tolerance policy, the monitoring cost will become an important factor for the system performance. Hence by applying our approach, the adaptive policy can be applied only to the critical agents whose criticalities can be observed during the organization's lifetime and the static one can be applied to the remaining agents. This reduces the monitoring cost and increases the overall organization performance. A case study has been implemented to show the effectiveness of our approach.

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Business process management (BPM) based on the usage of simulation modelling allows organizations to sustain and improve their operational performance. Discrete event simulation (DES) can help identify resource's bottlenecks, such as staffing, estimate process execution or delay times, test improvement suggestions in the process design phase, and overall assist in understanding how processes might impact performance in various scenarios. While many research papers report on the industry applications of BPM and DES, we focus to higher educational institutions (HEIS). Goals of the paper are: (1) to review the research of BPM and DES in HEIs, and (2) to discuss the impact of BPM and DES to process performance in one Croatian HEI. The case study methodology has been used in order to shed some light on the issue of applying discrete event simulation in BPM in the HEIs, especially for the purpose of collaboration improvement and impact assessment.

Ant colony optimisation (ACO) is a meta-heuristic approach to tackle hard combinatorial optimisation problems which has been used to solve several optimisation problems in various fields of engineering. Its basic component is a probabilistic solution construction mechanism. Discrete event simulation (DES) is a method which is used to model real world systems capable of being decomposed into a set of logically separate processes autonomously progressing through time. Each event occurs at an instant of time and indicates a change of state in the system. In this paper, we present the application of ant colony optimisation (ACO) model in discrete event simulation of marine traffic. We use this methodology to compare different distribution functions and to find the best distribution with its associated parameters. We experimentally show that exponential distribution has the best results for creating the simulation events of maritime traffic in Arvand river, Iran. Also, the integrated ACO-DES finds the best exponential distribution parameters for arrival (planning and control) of different kinds of ships in one of the most hazardous, crowded, and potentially dangerous rivers in the world. This is the first study that integrates ACO with DES for identification and optimisation of maritime traffic.

Many fault-tolerant systems have been designed for specific fault tolerance requirements and application domains. The author develops a fault tolerance framework that can be easily customized for various system specifications and requirements. The framework is constructed in an object-oriented paradigm. An application program can be plugged in the framework and a desired configuration can be specified to obtain the desired level of fault-tolerance for the application. The framework can also be easily extended to accommodate a wider spectrum of fault assumptions and system models.

Discrete event simulation (DES) is a well-recognized technique in evaluating the performance of flexible manufacturing systems (FMS). FMSs lend themselves to such a method because of the inherent complex element interactions, the queueing principles involved and the changes of state that occur within them. In particular, to attempt to rely on intuition, experience or static optimization alone can lead only to a limited FMS design solution. DES allows the analyst to recognize the dynamics of FMS while also allowing him to interact with the system model in such a way that changes in, say, layout, system design and operation strategies (scheduling, routing, etc.) may be assessed. Hence, “what if?” scenarios are orchestrated [1]. The analyst not only is able to make changes to the simulation rules but also is capable of altering data records and displays.

Java is a very promising language for use in the simulation of physical models due to its object-oriented nature, portability, robustness and support for multithreading. This paper presents JWarp, a Java library for discrete-event parallel simulations. It is based on an optimistic model for synchronization of the simulation entities: the Time Warp mechanism. We introduce the main features of the library and discuss some of the implementation details. © 1998 John Wiley & Sons, Ltd.

Fault Tolerance Requirements in Postal Automation: A Case Study

Compressionless Routing (GR) is a new adaptive routing framework which provides a unified framework for efficient deadlock-free adaptive routing and fault-tolerance. CR exploits the tight-coupling between wormhole routers for flow control to detect potential deadlock situations and recover from them. Fault-tolerant Compressionless Routing (FCR) extends Compressionless Routing to support end-to-end fault-tolerant delivery. Detailed routing algorithms, implementation complexity and performance simulation results for CR and FCR are presented.CR has the following advantages: deadlock-free adaptive routing in torus networks with no virtual channels, simple router designs, order-preserving message transmission, applicability to a wide variety of network topologies, and elimination of the need for buffer allocation messages. FCR has the following advantages: tolerates transient faults while maintaining data integrity (nonstop fault-tolerance), tolerates permanent faults, can be applied to a wide variety of network topologies, and eliminates the need for software buffering and retry for reliability. These advantages of CR and FCR not only simplify hardware support for adaptive routing and fault-tolerance, they also can simplify communication software layers.

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

The Air Force is developing a Distributed Information Enterprise Modeling and Simulation (DIEMS) framework under sponsorship of the High Performance Computer Modernization Office Common High Performance Computing Software Support Initiative (HPCMO/CHSSI). The DIEMS framework provides a design analysis environment for deployable distributed information management systems. DIEMS establishes the necessary analysis capability allowing developers to identify and mitigate programmatic risk early within the development cycle to allow successful deployment of the associated systems. The enterprise-modeling framework builds upon the Synchronous Parallel Environment for Emulation and Discrete-Event Simulation (SPEEDES) foundation. This simulation framework will utilize 'Challenge Problem' class resources to address more than five million information objects and hundreds of thousands of clients comprising the future information based force structure. The simulation framework will be capable of assessing deployment aspects such as security, quality of service, and fault tolerance. SPEEDES provides an ideal foundation to support simulation of distributed information systems on a multiprocessor platform. SPEEDES allows the simulation builder to perform optimistic parallel processing on high performance computers, networks of workstations, or combinations of networked computers and HPC platforms.

Fault-tolerant frameworks provide highly available services by means of fault detection and fault recovery mechanisms. These frameworks need to meet different constraints related to the fault model strength, performance, and resource consumption. One of the factors that led to this work is the observation that current fault-tolerant frameworks are not always adapted to existing Internet services. In fact, most of the proposed frameworks are not transport-level- or session-level-aware, although the concerned services range from regular services like HTTP and FTP to more recent Internet services such as multimodal conferencing and voice over IP. In this work we give a comprehensive overview of fault tolerance concepts, approaches, and issues. We show how the redundancy of application servers can be invested to ensure efficient failover of Internet services when the legitimate processing server goes down.