Honguk Woo

While scaling up to the enormous and growing Internet population with unpredictable usage patterns, E-commerce applications face severe challenges in cost and manageability, especially for database servers that are deployed as those applications' backends in a multi-tier configuration. Middle-tier database caching is one solution to this problem. In this paper, we present a simple extension to the existing federated features in DB2 UDB, which enables a regular DB2 instance to become a DBCache wi

Modern mobile devices are equipped with multiple network interfaces, including 3G/LTE and WiFi. Bandwidth aggregation over LTE and WiFi links offers an attractive opportunity of supporting bandwidth-intensive services, such as high-quality video streaming, on mobile devices. However, achieving effective bandwidth aggregation in mobile environments raises several challenges related to deployment, link heterogeneity, network fluctuation, and energy consumption. We present GreenBag, an energy-effic

Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process-especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neura

Advanced reinforcement learning (RL) technologies have recently increased the opportunity for automating several tasks in cluster management at scale by exploiting repetitive logs of cluster operation and building a learning model for resource allocation and job scheduling. Yet, this trend of adopting RL in the domain of cluster management has not fully addressed the diversity and heterogeneity of jobs and machines in modern cluster environments. In this paper, we present an RL-based scheduler f

Increasingly, business applications need to capture consumers' complex preferences interactively and monitor those preferences by translating them into event-condition-action (ECA) rules and syntactically correct processing specification. An expressive event model to specify primitive and composite events that may involve timing constraints among events is critical to such applications. Relying on the work done in active databases and real-time systems, this research proposes a new composite eve

Predicting the fix time of a bug is important for managing the resources and release milestones of a software development project. However, it is considered non-trivial to achieve high accuracy when predicting bug-fix times. We view that such difficulties come from the lack of continuous or posterior estimation based on subsequent developers' activities after a bug is initially reported. In this paper, we formulate the problem of bug-fix time prediction into a continual update of estimates with

Mission-critical cyber-physical systems must be resilient to all classes of failures, both hardware and software components. Failures affecting a systempsilas ability to accurately control its physical actions are of special concern, requiring a meta-level monitoring and reaction ability to enable high-performance nominal and safe post-failure operation. This paper addresses these challenges by unifying formal software engineering with a suite of feedback control laws and efficient resource moni

Data uncertainty is a common problem for the real-time monitoring of data streams. In this paper, we address the issue of efficiently monitoring the satisfaction/violation of user-defined constraints over data streams where the data uncertainty can be probabilistically characterized. We propose a monitoring architecture SPMON that can incorporate probabilistic models of uncertainty in constraint monitoring. We adapt the concept of data similarity in real-time databases to the processing of uncer

Parallelization of tasks and efficient utilization of processors are considered important and challenging in operating large-scale real-time systems. Recently, deep reinforcement learning (DRL) was found to provide effective solutions to various combinatorial optimization problems. In this paper, inspired by recent achievements in DRL, we employ DRL techniques for scheduling a directed acyclic graph (DAG) task in which a set of non-preemptive subtasks are specified by precedence conditions among

The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. I

Recently, AI software has been rapidly growing and is widely used in various industrial domains, such as finance, medicine, robotics, and autonomous driving. Unlike traditional software, in which developers need to define and implement specific functions and rules according to requirements, AI software learns these requirements by collecting and training relevant data. For this reason, if unintended biases exist in the training data, AI software can create fairness and safety issues. To address

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision "digital twin", in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and di

In this paper, we present an open platform of content delivery networks (CDNs), namely vCDN, by which a wide range of delivery strategies can be dynamically deployed via a centralized controller and a pool of geographically dispersed cloud resources. A control application can be written for representing a specific strategy w.r.t required scale, responsiveness, and security in content delivery, and then translated into a virtualized delivery network, a form of an overlay network on cloud storages

This paper provides a comprehensive approach to the problem of monitoring timing constraints over event streams for which the timestamp values are inherently uncertain. We first propose a generic framework for capturing the early detection of the violation of timing constraints, based on the notion of probabilistic violation time. In doing so, we provide a systemic approach for deriving a set of necessary constraints at compilation time. Our work is innovative in that the framework is formulated