To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2-4) and threads (1-5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique.

This article investigates how to satisfy network performance requirements that are crucial in achieving the service level objectives (SLOs) in clouds. Traditional techniques for network performance management have a limited ability to satisfy the network SLOs. Our in-depth analysis reveals that the fundamental reason comes from decoupling of the CPU scheduler and the network traffic controller as the current CPU scheduler is not aware of such network requirements but only provides a fair-share amount of CPU to all containers. Thus, the container cannot perform the amount of network processing as needed to satisfy its SLO when the CPU allocation is insufficient. In this article, we propose Autothrottle that dynamically adjusts the CPU allocation for the containers to satisfy their network SLOs. The key element of Autothrottle is a throttle algorithm that autonomously determines the amount of CPU for each container needed to satisfy the requirement. We implement Autothrottle in the Linux kernel and evaluate it with massive real-world workloads such as Apache Kafka. Our evaluation results show that Autothrottle successfully satisfies the given network SLO only with a 2% gap while the existing scheme achieves 20% less than the SLO. We further observe that Autothrottle also reduces the CPU overhead in network processing by 19%, improving the network throughput by 27% compared to the existing scheme.

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students' hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.

This paper introduces CodeDive, a web-based programming environment with real-time behavioral tracking designed to enhance student progress assessment and provide timely support for learners, while also addressing the academic integrity challenges posed by Large Language Models (LLMs). Visibility into the student’s learning process has become essential for effective pedagogical analysis and personalized feedback, especially in the era where LLMs can generate complete solutions, making it difficult to truly assess student learning and ensure academic integrity based solely on the final outcome. CodeDive provides this process-level transparency by capturing fine-grained events, such as code edits, executions, and pauses, enabling instructors to gain actionable insights for timely student support, analyze learning trajectories, and effectively uphold academic integrity. It operates on a scalable Kubernetes-based cloud architecture, ensuring security and user isolation via containerization and SSO authentication. As a browser-accessible platform, it requires no local installation, simplifying deployment. The system produces a rich data stream of all interaction events for pedagogical analysis. In a Spring 2025 deployment in an Operating Systems course with approximately 100 students, CodeDive captured nearly 25,000 code snapshots and over 4000 execution events with a low overhead. The collected data powered an interactive dashboard visualizing each learner’s coding timeline, offering actionable insights for timely student support and a deeper understanding of their problem-solving strategies. By shifting evaluation from the final artifact to the developmental process, CodeDive offers a practical solution for comprehensively assessing student progress and verifying authentic learning in the LLM era. The successful deployment confirms that CodeDive is a stable and valuable tool for maintaining pedagogical transparency and integrity in modern classrooms.

본 논문은 마이크로서비스 모니터링에 관련된 최근 주요 연구들을 대상으로, 전통적인 모니터링과는 다른 컨테이너 기반 마이크로서비스 배포 환경에서의 고려 사항과 이슈들을 깊이 있게 다룬다. 특히, 각 모니터링 기법의 목적, 성능, 오버헤드 및 주요 사용 기술을 소개하고, 실제 마이크로서비스 배포 환경에서 고려해야 할 사항을 논의한다. 또한 기존 연구들에서 사용된 다양한 마이크로서비스 벤치마크들과 모니터링 기법의 구체적인 대상들을 분석한다. 최종적으로 최근 마이크로서비스 모니터링 관련 연구들의 한계와 추후 다뤄져야 할 이슈들을 논의한다. 본 논문은 현대적이고 혁신적인 소프트웨어 개발 방식에 대한 이해와 더불어 복잡한 시스템을 대상으로 효율적인 모니터링 시스템을 구축하는데 필수적인 통찰을 제공할 수 있을 것이다.

Abstract This paper presents enCloud, a new aspect‐oriented trusted service migration with SGX‐enabled cloud VM. Addressing the challenge of reconciling end‐to‐end security with VM migration, enCloud incorporates two key aspects: (1) end‐to‐end security for enclave context migration, and (2) VM abstraction for conventional VM context migration. This paper provides a practical guideline with applicable APIs for trusted service migration. In a case study, enCloud demonstrates effective trusted DB service migration on a cloud VM, achieving end‐to‐end security with minimal trust boundaries. The framework supports pre‐copy live VM migration to minimize service downtime. This paper contributes a concise and practical solution in the form of the enCloud framework for secure service migration.

다수의 컴퓨터를 필요로 하는 SW 교육환경에서는 한 실습실을 여러 수업에서 공유하여 활용하는 것이 일반적이다. 하지만 기구축된 환경이 여러 수업의 다양한 하드웨어 및 소프트웨어 요구사항을 동시에 충족하기는 어렵기 때문에, 각각의 수업에서 요구하는 환경을 신속하게 준비할 수 있는 클라우드 컴퓨팅 환경을 적용할 수 있다. 본 논문에서는 오픈스택(OpenStack)을 활용한 교육용 사설 클라우드 시스템의 설계 및 구축 방안을 소개한다. 특히 현재의 교육용 클라우드 시스템에서 극복해야 할 세가지 문제점이 있다. 첫째, 공인 IP의 수가 제한적인 상황에서 학생들이 본인의 실습환경에 인터넷을 통해 접속할 수 있는 기능을 제공해야 하고, 둘째, 타 학생의 실습 환경에 접근하지 못하게 하는 권한 관리가 필요하며, 셋째, 타 학생의 자료나 구성 환경을 무단으로 수정할 수 없도록 제한하여야 한다. 본 논문에서는 오픈스택 기반 교육용 사설 클라우드의 구축 방안과 함께 위 세 가지 문제에 대한 해결 방안들을 제안하며, 실제 교육 현장에서 해결 방안들을 적용한 클라우드 시스템을 사용한 결과 사용자의 84.6%가 편리하다고 대답하였다.

Containers enable high performance and easy deployment due to their lightweight architecture, thus facilitating resource utilization in edge computing nodes. Secure container runtimes have attracted significant attention because of the necessity for overcoming the security vulnerabilities of containers. As the runtimes adopt an additional layer such as virtual machines and user-space kernels to enforce isolation, the container performance can be degraded. Even though previous studies presented experimental results on performance evaluations of secure container runtimes, they lack a detailed analysis of the root causes that affect the performance of the runtimes. This paper explores the architecture of three secure container runtimes in detail: Kata containers, gVisor, and Firecracker. We focus on file I/O, which is one of the key aspects of container performance. In addition, we present the results of the user- and kernel-level profiling and reveal the major factors that impact the file I/O performance of the runtimes. As a result, we observe three key findings: (1) Firecracker shows the highest file I/O performance as it allows for utilizing the page cache inside VMs, and (2) Kata containers offer the lowest file I/O performance by consuming the largest amount of CPU resources. Also, we observe that gVisor scales well as the block size increases because the file I/O requests are mainly handled by the host OS similar to native applications.

To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2-4) and threads (1-5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique.

This article investigates how to satisfy network performance requirements that are crucial in achieving the service level objectives (SLOs) in clouds. Traditional techniques for network performance management have a limited ability to satisfy the network SLOs. Our in-depth analysis reveals that the fundamental reason comes from decoupling of the CPU scheduler and the network traffic controller as the current CPU scheduler is not aware of such network requirements but only provides a fair-share amount of CPU to all containers. Thus, the container cannot perform the amount of network processing as needed to satisfy its SLO when the CPU allocation is insufficient. In this article, we propose Autothrottle that dynamically adjusts the CPU allocation for the containers to satisfy their network SLOs. The key element of Autothrottle is a throttle algorithm that autonomously determines the amount of CPU for each container needed to satisfy the requirement. We implement Autothrottle in the Linux kernel and evaluate it with massive real-world workloads such as Apache Kafka. Our evaluation results show that Autothrottle successfully satisfies the given network SLO only with a 2% gap while the existing scheme achieves 20% less than the SLO. We further observe that Autothrottle also reduces the CPU overhead in network processing by 19%, improving the network throughput by 27% compared to the existing scheme.