Software defined networking (SDN) is an emerging technology for controlling flows through networks. Used in the context of industrial control systems, an objective is to design configurations that have built-in protection for hardware failures in the sense that the configuration has "baked-in" back-up routes. The objective is to leave the configuration static as long as possible, minimizing the need to have the controller push in new routing and filtering rules We have designed and implemented a tool that enables us to determine the complete connectivity map from an analysis of all switch configurations in the network. We can use this tool to explore the impact of a link failure, in particular to determine whether the failure induces loss of the ability to deliver a flow even after the built-in back-up routes are used. A measure of the original configuration's resilience to link failure is the mean number of link failures required to induce the first such loss of service. The computational cost of each link failure and subsequent analysis is large, so there is much to be gained by reducing the overall cost of obtaining a statistically valid estimate of resiliency. This paper shows that when analysis of a network state can identify all as-yet-unfailed links any one of whose failure would induce loss of a flow, then we can use the technique of importance sampling to estimate the mean number of links required to fail before some flow is lost, and analyze the potential for reducing the variance of the sample statistic. We provide both theoretical and empirical evidence for significant variance reduction.
{"title":"Efficient Monte Carlo Evaluation of SDN Resiliency","authors":"D. Nicol, Rakesh Kumar","doi":"10.1145/2901378.2901401","DOIUrl":"https://doi.org/10.1145/2901378.2901401","url":null,"abstract":"Software defined networking (SDN) is an emerging technology for controlling flows through networks. Used in the context of industrial control systems, an objective is to design configurations that have built-in protection for hardware failures in the sense that the configuration has \"baked-in\" back-up routes. The objective is to leave the configuration static as long as possible, minimizing the need to have the controller push in new routing and filtering rules We have designed and implemented a tool that enables us to determine the complete connectivity map from an analysis of all switch configurations in the network. We can use this tool to explore the impact of a link failure, in particular to determine whether the failure induces loss of the ability to deliver a flow even after the built-in back-up routes are used. A measure of the original configuration's resilience to link failure is the mean number of link failures required to induce the first such loss of service. The computational cost of each link failure and subsequent analysis is large, so there is much to be gained by reducing the overall cost of obtaining a statistically valid estimate of resiliency. This paper shows that when analysis of a network state can identify all as-yet-unfailed links any one of whose failure would induce loss of a flow, then we can use the technique of importance sampling to estimate the mean number of links required to fail before some flow is lost, and analyze the potential for reducing the variance of the sample statistic. We provide both theoretical and empirical evidence for significant variance reduction.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Session on DDDAS","authors":"Kevin Jin","doi":"10.1145/3252254","DOIUrl":"https://doi.org/10.1145/3252254","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129493162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kishwar Ahmed, M. Obaida, Jason Liu, S. Eidenbenz, N. Santhi, Guillaume Chapuis
Interconnection network is a critical component of high-performance computing architecture and application co-design. For many scientific applications, the increasing communication complexity poses a serious concern as it may hinder the scaling properties of these applications on novel architectures. It is apparent that a scalable, efficient, and accurate interconnect model would be essential for performance evaluation studies. In this paper, we present an interconnect model for predicting the performance of large-scale applications on high-performance architectures. In particular, we present a sufficiently detailed interconnect model for Cray's Gemini 3-D torus network. The model has been integrated with an implementation of the Message-Passing Interface (MPI) that can mimic most of its functions with packet-level accuracy on the target platform. Extensive experiments show that our integrated model provides good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance.
{"title":"An Integrated Interconnection Network Model for Large-Scale Performance Prediction","authors":"Kishwar Ahmed, M. Obaida, Jason Liu, S. Eidenbenz, N. Santhi, Guillaume Chapuis","doi":"10.1145/2901378.2901396","DOIUrl":"https://doi.org/10.1145/2901378.2901396","url":null,"abstract":"Interconnection network is a critical component of high-performance computing architecture and application co-design. For many scientific applications, the increasing communication complexity poses a serious concern as it may hinder the scaling properties of these applications on novel architectures. It is apparent that a scalable, efficient, and accurate interconnect model would be essential for performance evaluation studies. In this paper, we present an interconnect model for predicting the performance of large-scale applications on high-performance architectures. In particular, we present a sufficiently detailed interconnect model for Cray's Gemini 3-D torus network. The model has been integrated with an implementation of the Message-Passing Interface (MPI) that can mimic most of its functions with packet-level accuracy on the target platform. Extensive experiments show that our integrated model provides good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132528622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel discrete event simulation (PDES) has been a subject of research since the late 1970s. By now we know a lot about the general outlines of the subject. However, there is still an immense amount of research and development to do. In this talk I will summarize what I believe PDES has contributed to computer science and other fields so far, what the current status is, where I think we are still deficient as a field, and where I think our R&D priorities should be in the future.
{"title":"The Future of Parallel Discrete Event Simulation","authors":"D. Jefferson","doi":"10.1145/2901378.2901404","DOIUrl":"https://doi.org/10.1145/2901378.2901404","url":null,"abstract":"Parallel discrete event simulation (PDES) has been a subject of research since the late 1970s. By now we know a lot about the general outlines of the subject. However, there is still an immense amount of research and development to do. In this talk I will summarize what I believe PDES has contributed to computer science and other fields so far, what the current status is, where I think we are still deficient as a field, and where I think our R&D priorities should be in the future.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115384586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiping Yao, Dong Meng, Qingjun Qu, Jin Li, Zhiwen Jiang
Parallel-discrete-event-simulation-based analytic simulation (PAS) is an effective approach to studycomplex issues and analyze complex systems.But the complexity and high demand for credibility of PAS make its development and experimentation quite different from traditional information systems. Firstly, this article briefly introduces analytic simulation concept and the difference with training simulation. And then five computational characteristics which cause the huge computation demand are summarized: multi-sample, multi-entity, as fast as possible, synchronization for constraint of causality and complex model calculation. According to these characteristics, a "Sample, Entity, Model" three-level-Parallelization solution(SEMP) is introduced for PAS.The solution can be used to fully exploit the parallelization of PAS and utilize the computing resources in different levels, which is able to meet the growing computation demand of PAS. Finally, in order to improve the development efficiency and credibility of PAS application, based on the accumulation of several years' R&D, we conclude a summary of development and experimentation flow of PAS, and propose four additional VV&A principles to improve credibility, which can be used to guide the development and experimentation of PDES-based analytic simulation.
{"title":"Development and Experimentation of PDES-based Analytic Simulation","authors":"Yiping Yao, Dong Meng, Qingjun Qu, Jin Li, Zhiwen Jiang","doi":"10.1145/2901378.2901385","DOIUrl":"https://doi.org/10.1145/2901378.2901385","url":null,"abstract":"Parallel-discrete-event-simulation-based analytic simulation (PAS) is an effective approach to studycomplex issues and analyze complex systems.But the complexity and high demand for credibility of PAS make its development and experimentation quite different from traditional information systems. Firstly, this article briefly introduces analytic simulation concept and the difference with training simulation. And then five computational characteristics which cause the huge computation demand are summarized: multi-sample, multi-entity, as fast as possible, synchronization for constraint of causality and complex model calculation. According to these characteristics, a \"Sample, Entity, Model\" three-level-Parallelization solution(SEMP) is introduced for PAS.The solution can be used to fully exploit the parallelization of PAS and utilize the computing resources in different levels, which is able to meet the growing computation demand of PAS. Finally, in order to improve the development efficiency and credibility of PAS application, based on the accumulation of several years' R&D, we conclude a summary of development and experimentation flow of PAS, and propose four additional VV&A principles to improve credibility, which can be used to guide the development and experimentation of PDES-based analytic simulation.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117173011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jared S. Ivey, Hemin Yang, Chuanji Zhang, G. Riley
As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.
{"title":"Comparing a Scalable SDN Simulation Framework Built on ns-3 and DCE with Existing SDN Simulators and Emulators","authors":"Jared S. Ivey, Hemin Yang, Chuanji Zhang, G. Riley","doi":"10.1145/2901378.2901391","DOIUrl":"https://doi.org/10.1145/2901378.2901391","url":null,"abstract":"As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An energy profile indicates the amount of energy consumed by different parts of a parallel or distributed simulation program. Creating energy profiles is not straightforward because high precision, low overhead energy measurement mechanisms may not be available, and it is not straightforward to determine the amount of energy consumed by different hardware components such as the CPU, memory system, or communication circuits that are operating concurrently throughout the execution of the distributed simulation. Techniques to create energy profiles of distributed simulation programs are described. A model is proposed that differentiates the energy consumed by the distributed simulation engine versus simulation application code, and energy consumed for computation versus that required for communication. A methodology and techniques are described to create energy profiles for these aspects of the distributed simulation. A study is described to illustrate this methodology to profile a distributed simulation synchronized by the Chandy/Misra/Bryant synchronization algorithm for a queuing network simulation. Empirical data are presented to validate the energy profile that is obtained.
{"title":"Profiling Energy Consumption in Distributed Simulations","authors":"A. Biswas, R. Fujimoto","doi":"10.1145/2901378.2901395","DOIUrl":"https://doi.org/10.1145/2901378.2901395","url":null,"abstract":"An energy profile indicates the amount of energy consumed by different parts of a parallel or distributed simulation program. Creating energy profiles is not straightforward because high precision, low overhead energy measurement mechanisms may not be available, and it is not straightforward to determine the amount of energy consumed by different hardware components such as the CPU, memory system, or communication circuits that are operating concurrently throughout the execution of the distributed simulation. Techniques to create energy profiles of distributed simulation programs are described. A model is proposed that differentiates the energy consumed by the distributed simulation engine versus simulation application code, and energy consumed for computation versus that required for communication. A methodology and techniques are described to create energy profiles for these aspects of the distributed simulation. A study is described to illustrate this methodology to profile a distributed simulation synchronized by the Chandy/Misra/Bryant synchronization algorithm for a queuing network simulation. Empirical data are presented to validate the energy profile that is obtained.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127946025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Session on M&S Areas and Applications 2","authors":"C. Carothers","doi":"10.1145/3252262","DOIUrl":"https://doi.org/10.1145/3252262","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128851244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Session on M&S Areas and Applications 1","authors":"P. Wilsey","doi":"10.1145/3252261","DOIUrl":"https://doi.org/10.1145/3252261","url":null,"abstract":"","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Zehe, T. VaisaghViswanathan, Wentong Cai, A. Knoll
Cloud-based simulation systems reduce the upfront hardware costs of running high-performance experiments and increases the ease with which simulation experiments can be repeated. The data being generated by simulations can be large. Commonly used data storage systems such as relational databases can handle large amounts of data, but the analysis is a challenging problem. Moreover, handling this amount of data in cloud services can be both expensive (bandwidth and storage costs) and time-consuming. However, a lot of the data that is generated by agent-based simulations does not contribute directly to the purpose of the experiment being conducted. We propose an extension to cloud-based simulation systems that rather than storing raw simulation output data, uses stream data processing to generate the result dataset while the simulation is running. This can then be used to store only the data required for later use, this saving both time and money.
{"title":"Online Data Extraction for Large-Scale Agent-Based Simulations","authors":"D. Zehe, T. VaisaghViswanathan, Wentong Cai, A. Knoll","doi":"10.1145/2901378.2901384","DOIUrl":"https://doi.org/10.1145/2901378.2901384","url":null,"abstract":"Cloud-based simulation systems reduce the upfront hardware costs of running high-performance experiments and increases the ease with which simulation experiments can be repeated. The data being generated by simulations can be large. Commonly used data storage systems such as relational databases can handle large amounts of data, but the analysis is a challenging problem. Moreover, handling this amount of data in cloud services can be both expensive (bandwidth and storage costs) and time-consuming. However, a lot of the data that is generated by agent-based simulations does not contribute directly to the purpose of the experiment being conducted. We propose an extension to cloud-based simulation systems that rather than storing raw simulation output data, uses stream data processing to generate the result dataset while the simulation is running. This can then be used to store only the data required for later use, this saving both time and money.","PeriodicalId":325258,"journal":{"name":"Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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