Enhancing the performance of HLA-based simulation systems via software diversity and active replication

In this paper we explore active replication based on software diversity for improving the responsiveness of simulation systems. Our proposal is framed by the High-Level-Architecture (HLA), namely the emerging standard for interoperability of simulation packages, and results in the design and implementation of an Active Replication Management Layer (ARML), which supports the execution of multiple software diversity-based replicas of a same simulator in a totally transparent manner. Beyond presenting the replication framework and the design/implementation of ARML, we also report the results of an experimental evaluation on a case study, quantifying the benefits from our proposal in terms of execution speed. © 2006 IEEE

Managing Bandwidth and Traffic via Bundling and Filtration in Large-Scale Distributed Simulations

Research has shown that bandwidth can be a limiting factor in the performance of distributed simulations. The Air Force\u27s Distributed Mission Operations Center (DMOC) periodically hosts one of the largest distributed simulation events in the world. The engineers at the DMOC have dealt with the difficult problem of limited bandwidth by implementing application level filters that process all DIS PDUs between the various networks connected to the exercise. This thesis examines their implemented filter and proposes: adaptive range-based filtering and bundling together of PDUs. The goals are to reduce the number of PDUs passed by the adaptive filter and to reduce network overhead and the total amount of data transferred by maximizing packet size up to the MTU. The proposed changes were implemented and logged data from previous events were used on a test network in order to measure the improvement from the base filter to the improved filter. The results showed that the adaptive range based filter was effective, though minimally so, and that the PDU bundling resulted in a reduction of 17% to 20% of the total traffic transmitted across the network

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

On improving the performance of optimistic distributed simulations

This report investigates means of improving the performance of optimistic distributed simulations without affecting the simulation accuracy. We argue that existing clustering algorithms are not adequate for application in distributed simulations, and outline some characteristics of an ideal algorithm that could be applied in this field. This report is structured as follows. We start by introducing the area of distributed simulation. Following a comparison of the dominant protocols used in distributed simulation, we elaborate on the current approaches of improving the simulation performance, using computation efficient techniques, exploiting the hardware configuration of processors, optimizations that can be derived from the simulation scenario, etc. We introduce the core characteristics of clustering approaches and argue that these cannot be applied in real-life distributed simulation problems. We present a typical distributed simulation setting and elaborate on the reasons that existing clustering approaches are not expected to improve the performance of a distributed simulation. We introduce a prototype distributed simulation platform that has been developed in the scope of this research, focusing on the area of emergency response and specifically building evacuation. We continue by outlining our current work on this issue, and finally, we end this report by outlining next actions which could be made in this field

Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

[EN] Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA's value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.Meirelles, G.; Brentan, B.; Izquierdo Sebastián, J.; Luvizotto, EJ. (2020). Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems. Processes. 8(8):1-19. https://doi.org/10.3390/pr8080980S11988Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). 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(2011). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17-35. doi:10.1007/s00366-011-0241-yKirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Wu, Z. Y., & Simpson, A. R. (2002). A self-adaptive boundary search genetic algorithm and its application to water distribution systems. Journal of Hydraulic Research, 40(2), 191-203. doi:10.1080/00221680209499862Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317-325. doi:10.1016/s0020-0190(02)00447-7Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems. Journal of Water Resources Planning and Management, 144(9), 04018055. doi:10.1061/(asce)wr.1943-5452.0000974Freire, R. Z., Oliveira, G. H. C., & Mendes, N. (2008). Predictive controllers for thermal comfort optimization and energy savings. Energy and Buildings, 40(7), 1353-1365. doi:10.1016/j.enbuild.2007.12.007Bollinger, L. A., & Evins, R. (2015). Facilitating Model Reuse and Integration in an Urban Energy Simulation Platform. Procedia Computer Science, 51, 2127-2136. doi:10.1016/j.procs.2015.05.484Yang, Y., & Chui, T. F. M. (2019). Developing a Flexible Simulation-Optimization Framework to Facilitate Sustainable Urban Drainage Systems Designs Through Software Reuse. Reuse in the Big Data Era, 94-99. doi:10.1007/978-3-030-22888-0_7Mavrovouniotis, M., Li, C., & Yang, S. (2017). A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, 1-17. doi:10.1016/j.swevo.2016.12.005Hybinette, M., & Fujimoto, R. M. (2001). 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Distributed Approaches to Supply Chain Simulation: A Review

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe field of Supply Chain Management (SCM) is experiencing rapid strides in the use of Industry 4.0 technologies and the conceptualization of new supply chain configurations for online retail, sustainable and green supply chains and the Circular Economy. Thus, there is an increasing impetus to use simulation techniques such as discrete-event simulation, agent-based simulation and hybrid simulation in the context of SCM. In conventional supply chain simulation, the underlying constituents of the system like manufacturing, distribution, retail and logistics processes are often modelled and executed as a single model. Unlike this conventional approach, a distributed supply chain simulation (DSCS) enables the coordinated execution of simulation models using specialist software. To understand the current state-of-the-art of DSCS, this paper presents a methodological review and categorization of literature in DSCS using a framework-based approach. Through a study of over 130 articles, we report on the motivation for using DSCS, the modelling techniques, the underlying distributed computing technologies and middleware, its advantages and a future agenda, as also limitations and trade-offs that may be associated with this approach. The increasing adoption of technologies like Internet-of-Things and Cloud Computing will ensure the availability of both data and models for distributed decision-making, and which is likely to enable data-driven DSCS of the future. This review aims to inform organizational stakeholders, simulation researchers and practitioners, distributed systems developers and software vendors, as to the current state of the art of DSCS, and which will inform the development of future DSCS using new applied computing approaches

Uncovering the Tumor Antigen Landscape : What to Know about the Discovery Process

According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.Peer reviewe

The Distributed Independent-Platform Event-Driven Simulation Engine Library (DIESEL)

The Distributed, Independent-Platform, Event-Driven Simulation Engine Library (DIESEL) is a simulation executive, capable of supporting both sequential and distributed discrete-event simulations. A system level specification is provided along with the expected behavior of each component within DIESEL. This behavioral specification of each component, along with the interconnection and interaction between the different components, provides a complete description of the DIESEL behavioral model. The model provides a considerable amount of freedom for an application developer to partition the simulation model, when building sequential and distributed applications with respect to balancing the number of events generated across different components. It also allows a developer to modify underlying algorithms in the simulation executive, while causing no changes to the overall system behavior so long as the algorithms meet the behavioral specifications. The behavioral model is object-oriented and developed using a hierarchical approach. The model is not targeted towards any programming language or hardware platform for implementation. The behavioral specification provides no specifics about how the model should be implemented. A complete and stable implementation of the behavioral model is provided as a proof-of-concept, and can be used to develop commercial applications. New and independent implementations of the complete model can be developed to support specific commercial and research efforts. Specific components of the model can also be implemented by students in an educational environment, using strategies different from the ones used within the current implementation. DIESEL provides a research environment for studying different aspects of Parallel Discrete-Event Simulation, such as event management strategies, synchronization algorithms, communication mechanisms, and simulation state capture capabilities

Pathogen-Mediated Evolution of Immunogenetic Variation in Plains Zebra (Equus quagga) of Southern Africa

Investigating patterns of variability in functional protein-coding genes is fundamental to identifying the basis for population and species adaptation and ultimately, for predicting evolutionary potential in the face of environmental change. The Major Histocompatibility Complex (MHC), a family of immune genes, has been one of the most emphasized gene systems for studying selection and adaptation in vertebrates due to its significance in pathogen recognition and consequently, in eliciting host immune response. Pathogen evasion of host resistance is thought to be the primary mechanism preserving extreme levels of MHC polymorphism and shaping immunogenetic patterns across host populations and species. In this thesis, I examined the evolution of two equine MHC genes, DRA and DQA, over the history of the genus Equus and across free-ranging plains zebra (E. quagga) populations of southern Africa: Etosha National Park (ENP), Namibia and Kruger National Park (KNP), South Africa. Furthermore, I evaluated the relationships between the DRA locus and parasite intensity in E. quagga of ENP, to elucidate the mechanisms by which parasites have shaped diversity at the MHC. In equids, the full extent of diversity and selection on the MHC in wild populations is unknown. Therefore, in this study, I molecularly characterized MHC diversity and selection across equid species to shed light on its mode of evolution in Equus and to identify specific sites under positive selection. Both the DRA and DQA exhibited a high degree of polymorphism and more intriguingly, greater allelic diversity was observed at the DRA than has previously been shown in any other vertebrate taxon. Global selection analyses of both loci indicated that the majority of codon sites are under purifying selection which may be explained by functional constraints on the protein. However, maximum likelihood based codon models of selection, allowing for heterogeneity in selection across codons, suggested that selective pressures varied across sites. Furthermore, at the DQA locus, all sites predicted to be under positive selection were antigen binding sites, implying that a few selected amino acid residues may play a significant role in equid immune function. Observations of trans-species polymorphisms and elevated genetic diversity were concordant with the hypothesis that balancing selection is acting on these genes. Over the past half century, the role of neutral versus selective processes in shaping genetic diversity has been at the center of an ongoing dialogue among evolutionary biologists. To determine the relative influence of demography versus selection on the DRA and DQA loci, I contrasted diversity patterns of neutral and MHC data across the E. quagga populations of ENP and KNP. Neutrality tests, along with observations of elevated diversity and low differentiation across populations relative to nuclear intron data, provided further evidence for balancing selection at these loci among E. quagga populations. However, at the DRA locus, differentiation was comparable to results at microsatellite loci. Furthermore, zebra in ENP exhibited reduced levels of diversity relative to KNP due to a highly skewed allele frequency distribution that could not be explained by demography. These findings were indicative of spatially heterogeneous selection and suggested directional selection and local adaptation at the DRA locus. There still remains a great deal of discussion over the mechanisms by which pathogens preserve immune gene diversity. The leading hypotheses that have been predominantly considered are: (i) heterozygote advantage (i.e. overdominant selection), (ii) rare allele advantage (i.e. frequency-dependent selection), and (iii) spatiotemporally fluctuating selection. An increasing number of studies have investigated MHC-parasite relationships to reconcile this debate, with conflicting results. To elucidate the mechanism driving the population-level patterns of diversity at the DRA locus, I examined relationships between this locus and both gastrointestinal (GI) and ectoparasite intensity in plains zebra of ENP. I discovered antagonistic pleiotropic effects of particular DRA alleles, with rare alleles predicting increased GI parasitism and common alleles associated with higher tick burdens. These results supported a frequency-dependent process and because maladaptive ‘susceptibility alleles’ were found at reduced frequencies, suggested that GI parasites exert strong selective pressure at this locus. Furthermore, heterozygote advantage also played a role in decreasing GI parasite burden, but only when a common allele was paired with a more divergent allele, implying that frequency-dependent and overdominant selection are acting in synchrony. These results indicated that an immunogenetic tradeoff may modulate resistance/susceptibility to parasites in this system, such that with MHC-based resistance to GI parasitism, a fitness cost is incurred to the host in the form of increased ectoparasite susceptibility. It is also suggested that these selective mechanisms are not mutually exclusive. In conclusion, these results provided species and population-level evidence for selection on the equid MHC, and highlighted the complexity in which selection operates in natural systems. In addition to heterogeneity in selective pressures at the molecular-level (across a gene region), selection likely varies spatiotemporally across populations due to fluctuations in pathogen regimes. Furthermore, pleiotropic effects of multiple pathogens can obscure our ability to understand adaptive processes. Given the level of complexity in which selection operates, I emphasize the necessity of incorporating multiple lines of evidence, using both neutral and adaptive data, to illuminate how selection operates. Finally, I also highlight the importance of considering the selective effects of multiple pathogens on host immunogenetics to better understand MHC function and adaptation

Data Bandwidth Reduction Techniques For Distributed Embedded Simulatio

Maintaining coherence between the independent views of multiple participants at distributed locations is essential in an Embedded Simulation environment. Currently, the Distributed Interactive Simulation (DIS) protocol maintains coherence by broadcasting the entity state streams from each simulation station. In this dissertation, a novel alternative to DIS that replaces the transmitting sources with local sources is developed, validated, and assessed by analytical and experimental means. The proposed Concurrent Model approach reduces the communication burden to transmission of only synchronization and model-update messages. Necessary and sufficient conditions for the correctness of Concurrent Models in a discrete event simulation environment are established by developing Behavioral Congruence ¨B(EL, ER) and Temporal Congruence ¨T(t, ER) functions. They indicate model discrepancies with respect to the simulation time t, and the local and remote entity state streams EL and ER, respectively. Performance benefits were quantified in terms of the bandwidth reduction ratio BR=N/I obtained from the comparison of the OneSAF Testbed Semi-Automated Forces (OTBSAF) simulator under DIS requiring a total of N bits and a testbed modified for the Concurrent Model approach which required I bits. In the experiments conducted, a range of 100 d BR d 294 was obtained representing two orders of magnitude reduction in simulation traffic. Investigation showed that the models rely heavily on the priority data structure of the discrete event simulation and that performance of the overall simulation can be enhanced by an additional 6% by improving the queue management. A low run-time overhead, self-adapting storage policy called the Smart Priority Queue (SPQ) was developed and evaluated within the Concurrent Model. The proposed SPQ policies employ a lowcomplexity linear queue for near head activities and a rapid-indexing variable binwidth calendar queue for distant events. The SPQ configuration is determined by monitoring queue access behavior using cost scoring factors and then applying heuristics to adjust the organization of the underlying data structures. Results indicate that optimizing storage to the spatial distribution of queue access can decrease HOLD operation cost between 25% and 250% over existing algorithms such as calendar queues. Taken together, these techniques provide an entity state generation mechanism capable of overcoming the challenges of Embedded Simulation in harsh mobile communications environments with restricted bandwidth, increased message latency, and extended message drop-outs