Departmental Papers (CIS)

Our faculty have research activities across many areas of computer science and are from schools throughout Penn, including Penn Engineering and the School of Arts and Sciences. For more information about CIS research, visit our research areas page.





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Now showing 1 - 10 of 852
  • Publication
    Towards Assurance Cases for Resilient Control Systems
    (2014-08-01) Weimer, James; Sokolsky, Oleg; Bezzo, Nicola; Lee, Insup
    The paper studies the problem of constructing assurance cases for embedded control systems developed using a model-based approach. Assurance cases aim to provide a convincing argument that the system delivers certain guarantees, based on the evidence obtained during the design and evaluation of the system. We suggest an argument strategy centered around properties of models used in the development and properties of tools that manipulate these models. The paper presents the case study of a resilient speed estimator for an autonomous ground vehicle and takes the reader through a detailed assurance case arguing that the estimator computes speed estimates with bounded error.
  • Publication
    Extensible Energy Planning Framework for Preemptive Tasks
    (2017-05-01) Kim, Jin Hyun; Gangadharan, Deepak; Sokolsky, Oleg; Lee, Insup; Legay, Axel
    Cyber-physical systems (CSPs) are demanding energy-efficient design not only of hardware (HW), but also of software (SW). Dynamic Voltage and and Frequency Scaling (DVFS) and Dynamic Power Manage (DPM) are most popular techniques to improve the energy efficiency. However, contemporary complicated HW and SW designs requires more elaborate and sophisticated energy management and efficiency evaluation techniques. This paper is concerned about energy supply planning for real-time scheduling systems (units) of which tasks need to meet deadlines. This paper presents a modelbased compositional energy planning technique that computes a minimal ratio of processor frequency that preserves schedulability of independent and preemptive tasks. The minimal ratio of processor frequency can be used to plan the energy supply of real-time components. Our model-based technique is extensible by refining our model with additional features so that energy management techniques and their energy efficiency can be evaluated by model checking techniques. We exploit the compositional framework for hierarchical scheduling systems and provide a new resource model for the frequency computation. As results, our use-case for avionics software components shows that our new method outperforms the classical real-time calculus (RTC) method, requiring 36.21% less frequency ratio on average for scheduling units under RM than the RTC method.
  • Publication
    Regular Specifications of Resource Requirements for Embedded Control Software
    (2008-04-01) Alur, Rajeev; Weiss, Gera
    For embedded control systems, a schedule for the allocation of resources to a software component can be described by an infinite word whose ith symbol models the resources used at the ith sampling interval. Dependency of performance on schedules can be formally modeled by an automaton (w-regular language) which captures all the schedules that keep the system within performance requirements. We show how such an automaton is constructed for linear control designs and exponential stability or settling time performance requirements. Then, we explore the use of the automaton for online scheduling and for schedulability analysis. As a case study, we examine how this approach can be applied for the LQG control design. We demonstrate, by examples, that online schedulers can be used to guarantee performance in worst-case condition together with good performance in normal conditions. We also provide examples of schedulability analysis.
  • Publication
    A Safety-Assured Development Approach for Real-Time Software
    (2010-08-23) Jee, Eunkyoung; Wang, Shaohui; Kim, Jeong Ki; Lee, Jaewoo; Sokolsky, Oleg; Lee, Insup
    Guaranteeing timing properties is an important issue as we develop safety-critical real-time systems such as cardiac pacemakers. We present a safety assured development approach of real-time software using a pacemaker as our case study. Following the model-driven development techniques, measurement-based timing analysis is used to guarantee timing properties in implementation as well as in the formal model. Formal specification with timed automata is checked with respect to timing properties by model checking technique and is transformed into implementation systematically. When timing properties may be violated in the implementation due to timing delay, it is suggested to measure the time deviation and reflect it to the code explicitly by modifying guards. The model is altered according to the modifications in the code. These changes of the code and the model are considered safe if all the properties are still satisfied by the modified model in re-performed model hecking. We demonstrate how the suggested approach can be applied to single-threaded and multi-threaded versions of implementation. This approach can provide developers with a useful time-guaranteeing technique applicable to several code generation schemes without imposing many restrictions.
  • Publication
    An Empirical Analysis of Scheduling Techniques for Real-Time Cloud-Based Data Processing
    (2011-12-01) Phan, Linh T.X.; Loo, Boon Thau; Zhang, Zhuoyao; Lee, Insup; Zheng, Qi
    In this paper, we explore the challenges and needs of current cloud infrastructures, to better support cloud-based data-intensive applications that are not only latency-sensitive but also require strong timing guarantees. These applications have strict deadlines (e.g., to perform time-dependent mission critical tasks or to complete real-time control decisions using a human-in-the-loop), and deadline misses are undesirable. To highlight the challenges in this space, we provide a case study of the online scheduling of MapReduce jobs executed by Hadoop. Our evaluations on Amazon EC2 show that the existing Hadoop scheduler is ill-equipped to handle jobs with deadlines. However, by adapting existing multiprocessor scheduling techniques for the cloud environment, we observe significant performance improvements in minimizing missed deadlines and tardiness. Based on our case study, we discuss a range of challenges in this domain posed by virtualization and scale, and propose our research agenda centered around the application of advanced real-time scheduling techniques in the cloud environment.
  • Publication
    Nondeterministic Streaming String Transducers
    (2011-07-01) Alur, Rajeev; Deshmukh, Jyotirmoy
    We introduce nondeterministic streaming string transducers (NSSTs) { a new computational model that can implement MSO-definable relations between strings. An NSST makes a single left-to-right pass on the input string and uses a finite set of string variables to compute the output. In each step, it reads one input symbol, and updates its string variables in parallel with a copyless assignment.We show that the expressive power of NSST coincides with that of nondeterministic MSO-definable transductions. Further, we identify the class of functional NSST; these allow nondeterministic transitions, but for every successful run on a given input generates the same output string. We show that deciding functionality of an arbitrary NSST is decidable with PSPACE complexity, while the equivalence problem for functional NSST is PSPACE-complete. We also show that checking if the set of outputs of an NSST is contained within the set of outputs of a finite number of DSSTs is decidable in PSPACE.
  • Publication
    Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
    (2022-11-17) Dutta, Souradeep; Sridhar, Kaustubh; Bastani, Osbert; Dobriban, Edgar; Weimer, James; Parish-Morris, Julia
    Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that they often receive expert intervention that enables them to understand the high-level structure of the task before mastering low-level control actions. We propose a framework for leveraging expert intervention to solve long-horizon reinforcement learning tasks. We consider option templates, which are specifications encoding a potential option that can be trained using reinforcement learning. We formulate expert intervention as allowing the agent to execute option templates before learning an implementation. This enables them to use an option, before committing costly resources to learning it. We evaluate our approach on three challenging reinforcement learning problems, showing that it outperforms state-of-the-art approaches by two orders of magnitude.
  • Publication
    Scalable Verification of Linear Controller Software
    (2016-04-01) Park, Junkil; Lee, Insup; Sokolsky, Oleg; Pajic, Miroslav
    We consider the problem of verifying software implementations of linear time-invariant controllers against mathematical specifications. Given a controller specification, multiple correct implementations may exist, each of which uses a different representation of controller state (e.g., due to optimizations in a third-party code generator). To accommodate this variation, we first extract a controller's mathematical model from the implementation via symbolic execution, and then check input-output equivalence between the extracted model and the specification by similarity checking. We show how to automatically verify the correctness of C code controller implementation using the combination of techniques such as symbolic execution, satisfiability solving and convex optimization. Through evaluation using randomly generated controller specifications of realistic size, we demonstrate that the scalability of this approach has significantly improved compared to our own earlier work based on the invariant checking method.
  • Publication
    Estimation of Blood Oxygen Content Using Context-Aware Filtering
    (2016-04-01) Ivanov, Radoslav; Atanasov, Nikolay; Weimer, James; Simpao, Allan F; Rehman, Mohamed A; Pappas, George; Lee, Insup; Pajic, Miroslav
    In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children’s Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables.
  • Publication
    Bandwidth Optimal Data/Service Delivery for Connected Vehicles via Edges
    (2018-07-01) Gangadharan, Deepak; Sokolsky, Oleg; Lee, Insup; Kim, BaekGyu; Lin, Chung-Wei; Shiraishi, Shinichi
    The paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.