Woochul Kang

RDDS: A Real-Time Data Distribution Service for Cyber-Physical Systems

One of the primary requirements in many cyber-physical systems (CPS) is that the sensor data derived from the physical world should be disseminated in a timely and reliable manner to all interested collaborative entities. However, providing reliable and timely data dissemination services is especially challenging for CPS since they often operate in highly unpredictable environments. Existing network middleware has limitations in providing such services. In this paper, we present a novel publish/subscribe-based middleware architecture called Real-time Data Distribution Service (RDDS). In particular, we focus on two mechanisms of RDDS that enable timely and reliable sensor data dissemination under highly unpredictable CPS environments. First, we discuss the semantics-aware communication mechanism of RDDS that not only reduces the computation and communication overhead, but also enables the subscribers to access data in a timely and reliable manner when the network is slow or unstable. Further, we extend the semantics-aware communication mechanism to achieve robustness against unpredictable workloads by integrating a control-theoretic feedback controller at the publishers and a queueing-theoretic predictor at the subscribers. This integrated control loop provides Quality-of-Service (QoS) guarantees by dynamically adjusting the accuracy of the sensor models. We demonstrate the viability of the proposed approach by implementing a prototype of RDDS. The evaluation results show that, compared to baseline approaches, RDDS achieves highly efficient and reliable sensor data dissemination as well as robustness against unpredictable workloads.

I/O-Aware Deadline Miss Ratio Management in Real-Time Embedded Databases

Recently, cheap and large capacity non-volatile memory such as flash memory is rapidly replacing disks in embedded systems. While the access time of flash memory is highly predictable, deadline misses may occur if data objects in flash memory are not properly managed in real-time embedded databases. Buffer cache can be used to mitigate this problem. However, since the workload of a real-time database cannot be precisely predicted, it may not be feasible to provide enough buffer space to satisfy all timing constraints. Several deadline miss ratio management schemes have been proposed, but they do not consider I/O activities. In this paper, we present an I/O-aware deadline miss ratio management scheme in real-time embedded databases whose secondary storage is flash memory. We propose an adaptive I/O deadline assignment scheme, in which I/O deadlines are derived from up-to-date system status. We also present a deadline miss ratio management architecture where a control theory-based feedback control loop prevents resource overload both in I/O and CPU. A simulation study shows that our approach can effectively cope with both I/O and CPU overload to achieve the desired deadline miss ratio.

Design, Implementation, and Evaluation of a QoS-Aware Real-Time Embedded Database

Quality-aware realtime Embedded DataBase (QeDB) is a database for data-intensive real-time applications running on embedded devices. Currently, databases for embedded systems are best effort, providing no guarantees on their timeliness and data freshness. Existing real-time database (RTDB) technology cannot be applied to these embedded databases since it hypothesizes that the main memory of a system is large enough to hold the entire database. This, however, might not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory. In particular, our approach is based on simultaneous control of both I/O and CPU resources to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we implement and evaluate the proposed scheme on a modern embedded device. The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches.

Failure Prediction in Computational Grids

Accurate failure prediction in grids is critical for reasoning about QoS guarantees such as job completion time and availability. Statistical methods can be used but they suffer from the fact that they are based on assumptions, such as time-homogeneity, that are often not true. In particular, periodic failures are not modeled well by statistical methods. In this paper, we present an alternative mechanism for failure prediction in which periodic failures are first determined and then filtered from the failure list. The remaining failures are then used in a traditional statistical method. We show that the use of pre-filtering leads to an order of magnitude better predictions

QeDB: A Quality-Aware Embedded Real-Time Database

QeDB is a database for data-intensive real-time applications running on flash memory-based embedded systems.Currently, databases for embedded systems are best effort,providing no guarantees on its timeliness and data freshness. Moreover, the existing real-time database (RTDB) technology can not be applied to these embedded databases since they hypothesize that the main memory of a system is large enough to hold all database, which can not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory.In particular, our approach is based on simultaneous control of both I/O and CPU resource to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we actually implement and evaluate the proposed scheme in a modern embedded system.The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches.

A low complexity coordination architecture for networked supervisory medical systems

Cooperating medical devices, envisioned by Integrated Clinical Environment (ICE) of Medical Device Plug-and-Play (MDPnP), is expected to improve the safety and the quality of patient care. To ensure safety, the cooperating medical devices must be thoroughly verified and tested. However, concurrent control of devices without proper coordination poses a significant challenge for the verification of the safety, since complex interaction patterns between devices might cause the explosion of the verification state space. In this paper, we propose a low-complexity coordination architecture and protocol for networked supervisory medical systems. The proposed architecture organizes the systems in a hierarchical and organ-based manner in accordance to human physiology and home-ostasis. Further, the proposed protocol avoids potential conflicts and unsafe controls, while allowing efficient concurrent operations of medical devices. The evaluation results show that our approach reduce the complexity by several orders of magnitude.

Applying Formal Methods to Modeling and Analysis of Real-time Data Streams

Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent realtime requirements. In this paper we showed how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We used MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the quality-of-service management mechanisms of RT-STREAM, a prototype system for data stream management. MEDAL’s ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the application’s performance and quality of service.

PRIDE: A Data Abstraction Layer for Large-Scale 2-tier Sensor Networks

It is a challenging task to provide timely access to global data from sensors in large-scale sensor network applications. Current data storage architectures for sensor networks have to make trade-offs between timeliness and scalability. PRIDE is a data abstraction layer for 2-tier sensor networks, which enables timely access to global data from the sensor tier to all participating nodes in the upper storage tier. The design of PRIDE is heavily influenced by collaborative real-time applications such as search-and-rescue tasks for high-rise building fires, in which multiple devices have to collect and manage data streams from massive sensors in cooperation. PRIDE achieves scalability, timeliness, and flexibility simultaneously for such applications by combining a model-driven full replication scheme and adaptive data quality control mechanism in the storage-tier. We show the viability of the proposed solution by implementing and evaluating it on a large-scale 2-tier sensor network testbed. The experiment results show that the model-driven replication provides the benefit of full replication in a scalable and controlled manner.

The design of an open data service architecture for cyber-physical systems

In this paper, we discuss the data management issues in cyber-physical systems, and propose an open data service architecture to address the problem.

Middleware design for physically-asynchronous logically-synchronous (PALS) systems

The Physically-Asynchronous Logically-Synchronous (PALS) system is a recently proposed architectural pattern for cyber-physical systems. It guarantees a logically synchronous design abstraction for real-time distributed computations. In this work, we develop a new middleware, called PALSware, to support an efficient and robust implementation of the PALS system and its extensions. PALSware guarantees consistency in distributed applications by eliminating any asynchronous interactions resulting from distributed clocks and node failures. We present a layered design for this middle-ware that is both reusable in different system architectures and can be extended with architecture-specific solutions for fault management. We demonstrate the middleware for an academic control testbed and show the consistency in a fault injection framework designed for this middleware.