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Publications of year 2016
Articles in journal, book chapters
  1. Shigeru Imai, Pratik Patel, and Carlos A. Varela. Encyclopedia on Cloud Computing, chapter 50: Developing Elastic Software for the Cloud, pages 609-627. Wiley, 2016. Note: Invited book chapter. ISBN: 978-1-118-82197-8. Keyword(s): distributed computing, cloud computing, actor model.
    Abstract:
    Developing standalone applications running on a single computer is very different from developing scalable applications running on the cloud, such as data analytics applications that process terabytes of data, Web applications that receive thousands of requests per second, or distributed computing applications where components run simultaneously across many computers. Cloud computing service providers help facilitate the development of these complex applications through their cloud programming frameworks. A cloud programming framework is a software platform to develop applications in the cloud that takes care of nonfunctional concerns, such as scalability, elasticity, fault tolerance, and load balancing. Using cloud programming frameworks, application developers can focus on the functional aspects of their applications and benefit from the power of cloud computing. In this chapter, we will show how to use some of the existing cloud programming frameworks in three application domains: data analytics, Web applications, and distributed computing. More specifically, we will explain how to use MapReduce (Dean and Ghemawat, 2008) for data analytics, Google App Engine (Google, 2014) for Web applications, and SALSA (Varela and Agha, 2001) for distributed computing. The rest of the chapter is structured as follows. In section 50.2, we describe nonfunctional concerns supported at different levels of cloud services and go through existing cloud programming frameworks. In section 50.3, we explain MapReduce, Google App Engine, and Simple Actor Language System and Architecture (SALSA). In section 50.4, we illustrate how to use these three programming frameworks by showing example applications. Finally, we conclude the chapter in section 50.5.

    @InBook{imai-cloud-encyclopedia-2016,
    author = {Shigeru Imai and Pratik Patel and Carlos A. Varela},
    editor = {San Murugesan and Irena Bojanova},
    title = {Encyclopedia on Cloud Computing},
    chapter = {50: Developing Elastic Software for the Cloud},
    publisher = {Wiley},
    year = {2016},
    pages = {609--627},
    note = {invited book chapter},
    isbn = {978-1-118-82197-8},
    doi = {10.1002/9781118821930.ch50},
    pdf = {http://wcl.cs.rpi.edu/papers/eoc2016.pdf},
    keywords = {distributed computing, cloud computing, actor model},
    abstract = {Developing standalone applications running on a single computer is very different from developing scalable applications running on the cloud, such as data analytics applications that process terabytes of data, Web applications that receive thousands of requests per second, or distributed computing applications where components run simultaneously across many computers. Cloud computing service providers help facilitate the development of these complex applications through their cloud programming frameworks. A cloud programming framework is a software platform to develop applications in the cloud that takes care of nonfunctional concerns, such as scalability, elasticity, fault tolerance, and load balancing. Using cloud programming frameworks, application developers can focus on the functional aspects of their applications and benefit from the power of cloud computing. In this chapter, we will show how to use some of the existing cloud programming frameworks in three application domains: data analytics, Web applications, and distributed computing. More specifically, we will explain how to use MapReduce (Dean and Ghemawat, 2008) for data analytics, Google App Engine (Google, 2014) for Web applications, and SALSA (Varela and Agha, 2001) for distributed computing. The rest of the chapter is structured as follows. In section 50.2, we describe nonfunctional concerns supported at different levels of cloud services and go through existing cloud programming frameworks. In section 50.3, we explain MapReduce, Google App Engine, and Simple Actor Language System and Architecture (SALSA). In section 50.4, we illustrate how to use these three programming frameworks by showing example applications. Finally, we conclude the chapter in section 50.5.} 
    }
    


Conference articles
  1. Sida Chen, Shigeru Imai, Wennan Zhu, and Carlos A. Varela. Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics. In Dynamic Data-Driven Application Systems (DDDAS 2016), Hartford, CT, August 2016. Note: Appears in Handbook of Dynamic Data-Driven Application Systems. Keyword(s): programming languages, cyber physical systems, data streaming.
    Abstract:
    Spatio-temporal data streams are often related in complex ways, for example, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe’s angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity, a malfunctioning pitot-static system, or other abnormal flight conditions. In this paper, we extend our PILOTS programming language to support machine learning techniques that will help data scientists: (1) create parameterized failure models from data and (2) continuously train a statistical model as new evidence (data) arrives. The linear regression approach learns parameters of a linear model to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error states on the fly while the error signatures approach requires every error state predefined. Using synthetic data, we compare the accuracy, response time, and adaptability of these machine learning techniques. Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions.

    @InProceedings{chen-pilots-dddas-2016,
    author = {Sida Chen and Shigeru Imai and Wennan Zhu and Carlos A. Varela},
    title = {Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics},
    booktitle = {Dynamic Data-Driven Application Systems (DDDAS 2016)},
    year = 2016,
    address = {Hartford, CT},
    month = {Aug},
    pdf = {http://wcl.cs.rpi.edu/papers/dddas2016.pdf},
    keywords = {programming languages, cyber physical systems, data streaming},
    note = {Appears in Handbook of Dynamic Data-Driven Application Systems.},
    abstract = {Spatio-temporal data streams are often related in complex ways, for example, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe’s angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity, a malfunctioning pitot-static system, or other abnormal flight conditions. In this paper, we extend our PILOTS programming language to support machine learning techniques that will help data scientists: (1) create parameterized failure models from data and (2) continuously train a statistical model as new evidence (data) arrives. The linear regression approach learns parameters of a linear model to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error states on the fly while the error signatures approach requires every error state predefined. Using synthetic data, we compare the accuracy, response time, and adaptability of these machine learning techniques. Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions.} 
    }
    


Miscellaneous
  1. Shigeru Imai, Stacy Patterson, and Carlos A. Varela. Cost-Efficient Elastic Stream Processing Using Application-Agnostic Performance Prediction. 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016), May 2016. Note: Doctoral symposium paper (best doctoral symposium paper award). Keyword(s): distributed computing, cloud computing, stream processing.
    Abstract:
    Cloud computing adds great on-demand scalability to stream processing systems with its pay-per-use cost model. However, to promise service level agreements to users while keeping resource allocation cost low is a challenging task due to uncertainties coming from various sources, such as the target application's scalability, future computational demand, and the target cloud infrastructure's performance variability. To deal with these uncertainties, it is essential to create accurate application performance prediction models. In cloud computing, the current state of the art in performance modelling remains application-specific. We propose an application-agnostic performance modeling that is applicable to a wide range of applications. We also propose an extension to probabilistic performance prediction. This paper reports the progress we have made so far.

    @Misc{imai-patterson-varela-ccgrid-2016b,
    author = {Shigeru Imai and Stacy Patterson and Carlos A. Varela},
    title = {Cost-Efficient Elastic Stream Processing Using Application-Agnostic Performance Prediction},
    howpublished = {16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016)},
    year = 2016,
    address = {Cartagena, Colombia},
    month = {May},
    pdf = {http://wcl.cs.rpi.edu/papers/ccgrid2016-phd.pdf},
    note = {Doctoral symposium paper (best doctoral symposium paper award)},
    keywords = {distributed computing, cloud computing, stream processing},
    abstract = {Cloud computing adds great on-demand scalability to stream processing systems with its pay-per-use cost model. However, to promise service level agreements to users while keeping resource allocation cost low is a challenging task due to uncertainties coming from various sources, such as the target application's scalability, future computational demand, and the target cloud infrastructure's performance variability. To deal with these uncertainties, it is essential to create accurate application performance prediction models. In cloud computing, the current state of the art in performance modelling remains application-specific. We propose an application-agnostic performance modeling that is applicable to a wide range of applications. We also propose an extension to probabilistic performance prediction. This paper reports the progress we have made so far.} 
    }
    


  2. Shigeru Imai, Stacy Patterson, and Carlos A. Varela. Elastic Virtual Machine Scheduling for Continuous Air Traffic Optimization. 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016), May 2016. Note: Short paper. Keyword(s): distributed computing, cloud computing, stream processing.
    Abstract:
    As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate human controllers' workload by viewing air traffic optimization as a continuous/online streaming problem. Air traffic optimization is commonly formulated as an integer linear programming(ILP) problem. Since ILP is NP-hard, it is computationally intractable. Moreover, a fluctuating number of flights changes computational demand dynamically. In this paper, we present an elastic middleware framework that is specifically designed to solve ILP problems generated from continuous air traffic streams. Experiments show that our VM scheduling algorithm with time-series prediction can achieve similar performance to a static schedule while using 49% fewer VM hours for a realistic air traffic pattern.

    @Misc{imai-patterson-varela-ccgrid-2016a,
    author = {Shigeru Imai and Stacy Patterson and Carlos A. Varela},
    title = {Elastic Virtual Machine Scheduling for Continuous Air Traffic Optimization},
    howpublished = {16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016)},
    year = 2016,
    address = {Cartagena, Colombia},
    month = {May},
    pdf = {http://wcl.cs.rpi.edu/papers/ccgrid2016-short.pdf},
    keywords = {distributed computing, cloud computing, stream processing},
    note = {Short paper},
    abstract = {As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate human controllers' workload by viewing air traffic optimization as a continuous/online streaming problem. Air traffic optimization is commonly formulated as an integer linear programming(ILP) problem. Since ILP is NP-hard, it is computationally intractable. Moreover, a fluctuating number of flights changes computational demand dynamically. In this paper, we present an elastic middleware framework that is specifically designed to solve ILP problems generated from continuous air traffic streams. Experiments show that our VM scheduling algorithm with time-series prediction can achieve similar performance to a static schedule while using 49% fewer VM hours for a realistic air traffic pattern.} 
    }
    


  3. Stacy Patterson and Carlos A. Varela. Steering Complex Systems using a Dynamic, Data-Driven Modeling Approach. Streaming Technology Requirements, Application and Middleware (STREAM2016), March 2016. Keyword(s): programming languages, cyber physical systems, data streaming.
    Abstract:
    Dynamic data-driven applications and systems (DDDAS) use models to control systems using a feedback loop, however, they go beyond traditional control-theoretic approaches in that the data acquisition process itself may be controlled by steering the system resources. Thus, there is the opportunity to strategically collect data and modify the models dynamically to incorporate recent observations. This improved modeling capability can then be used in feedback to steer the system towards a desired trajectory or outcome. Such an adaptive, data-driven approach has the potential to significantly improve the modeling capability of complex systems that include streaming data and opens the door to new applications, including smarter, safer vehicles and environments. Examples of such applications include

    @Misc{patterson-varela-stream-2016,
    author = {Stacy Patterson and Carlos A. Varela},
    title = {Steering Complex Systems using a Dynamic, Data-Driven Modeling Approach},
    howpublished = {Streaming Technology Requirements, Application and Middleware (STREAM2016)},
    year = 2016,
    month = {March},
    address = {Washington DC},
    pdf = {http://streamingsystems.org/Papers/Varela.pdf},
    keywords = {programming languages, cyber physical systems, data streaming},
    abstract = {Dynamic data-driven applications and systems (DDDAS) use models to control systems using a feedback loop, however, they go beyond traditional control-theoretic approaches in that the data acquisition process itself may be controlled by steering the system resources. Thus, there is the opportunity to strategically collect data and modify the models dynamically to incorporate recent observations. This improved modeling capability can then be used in feedback to steer the system towards a desired trajectory or outcome. Such an adaptive, data-driven approach has the potential to significantly improve the modeling capability of complex systems that include streaming data and opens the door to new applications, including smarter, safer vehicles and environments. Examples of such applications include} 
    }
    



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Last modified: Tue Oct 13 12:16:53 2020
Author: pauls4.


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