arXiv subjects

Eun-Sung Jung

Publications and source records attributed to Eun-Sung Jung.

6 recordsLinked to original sources

arXiv2025

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

After the advent of the Internet of Things and 5G networks, edge computing became the center of attraction. The tasks demanding high computation are generally offloaded to the cloud since the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, minimizing latency, and increasing performance. An abundance of efforts on scheduling has been done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, and we subcategorize algorithms in each class further based on extracted attributes of algorithms. We hope that this survey will be very thoughtful in the development of new scheduling techniques. Issues, challenges, and future directions have also been examined.

cs.DC↗

arXiv2024

Design and Implementation of an Automated Disaster-recovery System for a Kubernetes Cluster Using LSTM

With the increasing importance of data in the modern business environment, effective data man-agement and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management plat-forms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared with manual recovery processes, thereby enhancing data management and recovery ef-ficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

cs.DC↗

arXiv2021

Factorial Convolution Neural Networks

In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection. However, it experiences challenges of contaminated deep features when concatenating elements with different properties. Also, since GoogleNet is not an entirely lightweight CNN, it still has many execution overheads to apply to a resource-starved application domain. Therefore, a new CNNs, FactorNet, has been proposed to overcome these functional challenges. The FactorNet CNN is composed of multiple independent sub CNNs to encode different aspects of the deep visual features and has far fewer execution overheads in terms of weight parameters and floating-point operations. Incorporating FactorNet into the Faster-RCNN framework proved that FactorNet gives \ignore{a 5\%} better accuracy at a minimum and produces additional speedup over GoolgleNet throughout the KITTI object detection benchmark data set in a real-time object detection system.

cs.CV↗

arXiv2021

Towards Accommodating Real-time Jobs on HPC Platforms

Increasing data volumes in scientific experiments necessitate the use of high-performance computing (HPC) resources for data analysis. In many scientific fields, the data generated from scientific instruments and supercomputer simulations must be analyzed rapidly. In fact, the requirement for quasi-instant feedback is growing. Scientists want to use results from one experiment to guide the selection of the next or even to improve the course of a single experiment. Current HPC systems are typically batch-scheduled under policies in which an arriving job is run immediately only if enough resources are available; otherwise, it is queued. It is hard for these systems to support real-time jobs. Real-time jobs, in order to meet their requirements, should sometimes have to preempt batch jobs and/or be scheduled ahead of batch jobs that were submitted earlier. Accommodating real-time jobs may negatively impact system utilization also, especially when preemption/restart of batch jobs is involved. We first explore several existing scheduling strategies to make real-time jobs more likely to be scheduled in due time. We then rigorously formulate the problem as a mixed-integer linear programming for offline scheduling and develop novel scheduling heuristics for online scheduling. We perform simulation studies using trace logs of Mira, the IBM BG/Q system at Argonne National Laboratory, to quantify the impact of real-time jobs on batch job performance for various percentages of real-time jobs in the workload. We present new insights gained from grouping jobs into different categories based on runtime and the number of nodes used and studying the performance of each category. Our results show that with 10% real-time job percentages, just-in-time checkpointing combined with our heuristic can improve the slowdowns of real-time jobs by 35% while limiting the increase of the slowdowns of batch jobs to 10%.

cs.DC↗

arXiv2020

Flexible IoT Datapath Programming using P4

The progress of the network and device technologies enables any device to be connected to the Internet thereby forming an Internet of Things (IoT) architecture. Internet-related activities based on the IoT services can inevitably generate a lot of real-time data which leads a big data phenomenon. To efficiently handle such big IoT data, software-defined networking (SDN) techniques could come into play. Among SDN techniques, P4 is a highlevel language for programming protocol-independent packet processors. This paper presents our preliminary work on how we can use P4 to achieve flexible datapath programming for large scale IoT streaming data streaming. As a case study, we describe the implementation of dynamic MST-based datapath programing for IoT data collection in an arbitrary network topology.

cs.NI↗

arXiv2019

FLIP:FLexible IoT Path Programming Framework for Large-scale IoT

With the rapid increase in smart objects forming IoT fabric, it is inevitable to see billions of devices connected together, forming large-scale IoT networks. This expeditious increase in IoT devices is giving rise to increased user requirements and network complexity. Collecting big data from these IoT devices with optimal network utilization and simplicity is becoming more and more challenging. This paper proposes FLIP- FLexible IoT Path Programming Framework for Large-scale IoT. The distinctive feature of FLIP is that it focuses on the IoT fabric from the perspective of user requirements and uses SDN techniques along with DPI technology to efficiently fulfill the user requirements and establish datapath in the network in an automated and distributed manner. FLIP utilizes SDN structure to optimize network utilization through in-network computing and automated datapath establishment, also hiding network complexity along the way. We evaluated our framework through experiments, and results indicate that FLIP has the potential to fulfill user requirements in an automated fashion and optimize network utilization.

cs.NI↗