Earticle

최근 의료 서비스 분야는 서비스 품질 및 시간 단축을 위해서 전자 건강 기록을 로컬 환경에서 클라우드 환경 으로 변화하고 있다. 그러나, 의료 현장에서 클라우드 기반 EHR 소프트웨어는 일부 위험이 내포되고 있어 이러한 위 협들을 대응할 수 있는 추가 방안이 필요하다. 본 논문에서는 분산형 클라우드 환경에서 블록체인 기반의 전자 건강 기록 관리 모델을 제안한다. 제안 모델은 분산 클라우드 환경에서 환자의 전자 건강 기록 정보를 안전하게 보관·처리 하기 위한 방법으로 전자 건강 기록들을 블록체인으로 묶어 의료 서비스 품질을 높이고 있다. 또한, 제안 모델은 서로 다른 의료진이 건강 기록들을 공유할 수 있도록 클라우드 환경에서 전자 건강 기록 정보를 루트 해시로 판별할 수 있 도록 블록에 저장된 URL '경로'의 위치를 변경하여 손상된 전자 건강 기록 정보를 복구하여 전자 건강 기록 정보의 무결성을 보장한다.

Recently, the medical service sector is changing electronic health records from local environments to cloud environments for service quality and time reduction. However, cloud-based EHR software poses some risks in the medical field, so additional measures are needed to cope with these threats. In this paper, we propose a blockchain-based electronic health record management model in a distributed cloud environment. The proposed model improves the quality of medical services by grouping electronic health records into blockchains as a way to safely store and process patient electronic health record information in a distributed cloud environment. In addition, the proposed model guarantees the integrity of electronic health record information by restoring the damaged electronic health record information by changing the location of the URL 'path' stored in the block so that different medical staff can determine electronic health record information as a root hash in a cloud environment so that health records can be shared.

With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

Recently, with the development of IoT technology, data has increased, and problems with the centralized cloud computing method are appearing. As an alternative to this, Edge Computing, a distributed cloud method that processes data close to the edge of the network where data is generated, is utilized. On the other hand, as the number of containers on one host increases, container management becomes difficult, and accordingly, container orchestration technology capable of configuring and managing a large number of containers is required. In this paper, we measure, compare, and analyze the time to transmit sensor data to the DB server of each Kubernetes cluster by building a multicluster infrastructure using Kubernetes, which is the most used container orchestration tool.

최근 클라우드 기반의 자원들은 다양한 장치를 통해 클라우드 서버로 저장하여 사용되고 있지만, 클라우드 서버로 전송되는 자원들은 클라우드에서 작업량을 최적화하기 위해 특정한 기능을 연결해주는 유연성 및 이동성에 제약이 있다. 본 논문에서는 속도, 성능, 안정성과 같은 요소를 기준으로 클라우드에서 작업량을 최적화할 수 있는 멀티 클라우드 기반 시스템 장애 최소화를 위한 분산 클라우드 자원 할당 기법을 제안한다. 제안 기법은 클라우드 자원의 특징을 계층별로 추출하여 특징들의 패턴을 합성 과정을 통해 클라우드 자원 할당을 동적으로 연계하여 최적화한다. 특히, 제안 기법은 멀티 클라우드 환경에서 클라우드 자원 할당을 최소화하기 위해서 동일 패턴의 클 라우드 자원을 블록체인으로 묶고, 합성 과정을 통해 클라우드 자원 크기를 줄임으로써 클라우드 자원을 일관성 있게 유지한다. 성능평가 결과, 제안 기법은 클라우드 자원 간 확률값에 따라 서로 연계하기 때문에 클라우드 자원 검증 시간이 평균 7.42% 향상되었고, 블록체인의 크기가 증가할수록 검증 지연 시간은 평균 8.81% 낮게 나타났다. 또한, 블록체인 그룹 크기에 따른 클라우드 자원의 연계 장애율은 평균 11.12% 낮은 결과를 얻었다.

Recently, cloud-based resources are stored and used as cloud servers through various devices, but resources transmitted to cloud servers have limitations in flexibility and mobility that connect specific functions to optimize workloads in the cloud. In this paper, we propose a distributed cloud resource allocation technique for minimizing failures in a multi-cloud system that can optimize workloads in the cloud based on factors such as speed, performance, and stability. The proposed technique extracts features of cloud resources by layer and dynamically links and optimizes cloud resource allocation through the convolution process of the patterns of features. In particular, the proposed technique keeps cloud resources consistent by grouping the same pattern of cloud resources into a blockchain to minimize cloud resource allocation in a multi-cloud environment and reducing the size of cloud resources through the convolution process. As a result of the performance evaluation, because the proposed technique is linked to each other according to the probability value between cloud resources, the average cloud resource verification time improved by 7.42%, and the verification delay time decreased by 8.81%. In addition, the average linkage failure rate of cloud resources according to the blockchain group size was 11.12% lower.

현재의 ICT 인프라(인터넷과 서버/Client 연동)는 다양한 장치, 서비스, 비즈니스 및 기술 진화에 따른 신속한 대응 에 어려움을 겪고 있다. 클라우드 컴퓨팅(Cloud Computing)은 구름 같은 네트워크 환경에서 원하는 작업을 요청하여 실행한다는 데서 기원하였으며, 인터넷 기술을 활용하여 IT 자원을 서비스로 제공하는 컴퓨팅을 뜻하고 오늘날 IT 트 렌드의 하나로 가장 주목 받고 있다. 이러한 분산클라우드 환경에서는 네트워크 및 컴퓨팅 자원에 대한 통합 관리 체계 를 통하여 관리 비용 증가 문제를 원천적으로 해결하고 분산된 마이크로 데이터센터(Micro DC(Data Center))를 통하여 코어 네트워크 트래픽 폭증 문제를 해결하여 비용 절감 효과를 높일 수 있다. 그러나 기존의 Flooding 방식은 인접한 모든 DC들에게 전송하기 때문에 많은 트래픽을 유발 할 수 있다. 이를 위해 Restricted Path Flooding 알고리즘이 제안 되었으나 대규모 네트워크에서는 여전히 트래픽을 발생할 수 있는 단점이 있어서 본 논문에서는 홉수 제한을 통하여 이를 개선한 Lightweight Path Flooding 알고리즘을 제안하였다.

Current ICT(Information & Communication Technology) infrastructures (Internet and server/client communicatio n) are struggling for a wide variety of devices, services, and business and technology evolution. Cloud computing or iginated simply to request and execute the desired operation from the network of clouds. It means that an IT resour ce that provides a service using the Internet technology. It is getting the most attention in today's IT trends. In the distributed cloud environments, management costs for the network and computing resources are solved fundamentall y through the integrated management system. It can increase the cost savings to solve the traffic explosion problem of core network via a distributed Micro DC. However, traditional flooding methods may cause a lot of traffic due to transfer to all the neighbor DCs. Restricted Path Flooding algorithms have been proposed for this purpose. In large networks, there is still the disadvantage that may occur traffic. In this paper, we developed Lightweight Path Floodi ng algorithm to improve existing flooding algorithm using hop count restriction..

Recently, cloud computing technology is rapidly growing at a faster rate offering cloud-based driver profiling applications with lower latency. In this study, we have proposed a computational efficient cloud-based architecture for the deployment of driver profiling deep learning algorithms. In order to validate the efficacy of the proposed architecture, we have evaluated the performance of the proposed deep learning architecture for the recent driver behavior identification using time series sensor data. We have utilized an Amazon web service-based cloud computing solution for the deployment of the proposed architecture. The experimental results show that the proposed architecture improves end-to-end latency by 3.1 times compared to the traditional method.

위치기반 서비스란 휴대폰과 같은 모바일 단말기 속에 위성 항법장치(GPS)와 연결되는 칩을 부착하여 위치추적 서비스, 공공안전 서비스, 위치기반 정보 서비스 등 위치와 관련된 각종 정보를 제공하는 서비스를 일컫는다. 그리고 클라우드 컴퓨팅이란 개인용 컴퓨터 또는 기업의 서버에 개별적으로 저장해 두었던 자료와 소프트웨어들을 클라우드 클러스터로 구축하여 필요할 때 PC나 휴대폰 같은 각종 단말기를 이용하여 원격 작업을 수행할 수 있는 환경을 의미한다. 본 논문에서는 구글의 연구로부터 시작된 클라우드 컴퓨팅 기술에 주목하고 클라우드 컴퓨팅 환경을 위한 대용량 이동 객체의 정보를 저장하고 질의하는 시스템에 대하여 연구하였다. 그리고 위치 기반 서비스를 위한 대용량 이동객체에 대하여 효율적인 질의를 수행할 수 있는 저장 구조를 제안하였다. 본 논문에서 제안한 방법을 적용한 시스템의 효율성을 증명하기 위해 저장 및 질의 성능을 다양한 측면에서 실험하고, 실험 결과를 다른 분산처리 시스템과의 비교하여 성능을 입증하였다.

The location-based service is referred to as the service for providing various types of information regarding such as the location-tracing service, the public-safety service and the location-based information service by attaching chips connected with the GPS to the mobile phone. In addition, cloud computing means the environment which makes it possible to carry out remote operations by establishing cloud clusters with the data and software which have been individually stored in personal computer or corporate servers and using devices such as personal computers and cellular phones whenever it is necessary. Throughout this paper, the cloud computing technology which originates from the research by Google the system has been focused on and the system for storing a large volume of information for moving object and making query for cloud computing environment has been studied. Besides, the storing structure for executing efficient query regarding the high-capacity mobile devices for the location-based service has been suggested. In order to prove the efficiency of the system with the application of the method suggested in this paper, the storage and qualitative functions have been experimented in various aspects and compared with other distributed processing system's performance.

Recently, cloud computing technology has been offering cloud-based plate number classification applications with lower latency. In this paper, we design and implement a new distributed plate number classification system (DPNC). The proposed DPNC system absorbs a more significant number of input sensor data from autonomous cars with a lightweight model that provides high accuracy. In addition, our model has employed the entire convolution network – Long Short-term Memory (FCN-LSTM) to predict a total of 3 classes such as image plate, boundary, and number detection. We evaluate the proposed system using an existing Iranian plate dataset containing a collection of plate images using an autonomous car. We used various Amazon cloud services for deploying the proposed DPNC architecture. The experimental results show that the proposed architecture improves end-to-end latency by 2.1 times compared to the traditional architecture.

Recently, along with digital transformation, technologies such as cloud computing, big data, and artificial intelligence have been actively introduced. In a situation where these technological changes are progressing rapidly, it is often difficult to manage processes efficiently using existing simple workflow management methods. Companies providing current cloud services are adopting virtualization technologies, including virtual machines (VMs) and containers, in their distributed system infrastructure for automated application deployment. Accordingly, this paper proposes a process-based orchestration system for integrated execution of corporate process-oriented workloads by integrating the potential of big data and machine learning technologies. This system consists of four layers as components for performing workload processes. Additionally, a common information model is applied to the data to efficiently integrate and manage the various formats and uses of data generated during the process creation stage. Moreover, a standard metadata protocol is introduced to ensure smooth exchange between data. This proposed system utilizes various types of data storage to store process data, metadata, and analysis models. This enables flexible management and efficient processing of data.

This paper proposes a Holochain-based system to enhance the security and management of healthcare data in a distributed cloud environment, addressing the limitations of blockchain technology. While blockchain ensures data integrity, it faces performance degradation and resource consumption challenges when processing large volumes of transactions. To overcome these issues, the study introduces Holochain, a technology that enables data recovery in case of loss and validates data through local consensus without requiring global agreement. The proposed system is composed of a HoloLink module and a transformation module, with the user interface fully implemented. Performance evaluation experiments demonstrate that the Holochain-based system significantly reduces validation time as the number of nodes and transactions increases, compared to traditional blockchain systems.

We propose to design a Holochain-based security and privacy protection system for resource-constrained IoT healthcare systems. Through analysis and performance evaluation, the proposed system confirmed that these characteristics operate effectively in the IoT healthcare environment. The system proposed in this paper consists of four main layers aimed at secure collection, transmission, storage, and processing of important medical data in IoT healthcare environments. The first PERCEPTION layer consists of various IoT devices, such as wearable devices, sensors, and other medical devices. These devices collect patient health data and pass it on to the network layer. The second network connectivity layer assigns an IP address to the collected data and ensures that the data is transmitted reliably over the network. Transmission takes place via standardized protocols, which ensures data reliability and availability. The third distributed cloud layer is a distributed data storage based on Holochain that stores important medical information collected from resource-limited IoT devices. This layer manages data integrity and access control, and allows users to share data securely. Finally, the fourth application layer provides useful information and services to end users, patients and healthcare professionals. The structuring and presentation of data and interaction between applications are managed at this layer. This structure aims to provide security, privacy, and resource efficiency suitable for IoT healthcare systems, in contrast to traditional centralized or blockchain-based systems. We design and propose a Holochain-based security and privacy protection system through a better IoT healthcare system.

Intrusion prospects in cloud paradigm are many and with high gains, may it be a bad user or a competitor of cloud client. Distributed model makes it vulnerable and prone to sophisticated distributed intrusion attacks like Distributed Denial of Service (DDOS) and Cross Site Scripting (XSS). Confronting new implementation situations, traditional IDSs are not well suited for cloud environment. To handle large scale network access traffic and administrative control of data and application in cloud, a new multi-threaded distributed cloud IDS model has been proposed. Our proposed cloud IDS handles large flow of data packets, analyze them and generate reports efficiently. Transparent reports are instantly send for information of cloud user and expert advice for cloud service provider’s network mis-configurations through a third party IDS monitoring and advisory service.

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

As the demand on online services and cloud computing has kept increasing in recent years, the power usage and cost associated with cloud data centers’ operation have been uprising significantly. Most existing research focuses on reducing power consumption of data centers. However, the ultimate goal of cloud service operators is to reduce the total operating cost of data centers while guaranteeing the quality of service such as service delay to the end users. This paper exploits both the workload dispatching and the service provisioning to address the total electricity cost minimization problem. This problem is formulated as a hierarchical capacitated median model based on mixed integer linear programming (MILP) technique. Extensive evaluations based on real-life electricity price data for multiple data centers show the efficiency and efficacy of our approach.

As the size of big data models grows, distributed training is emerging as an essential element for largescale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.