• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.577-582
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• 2011

EMR(Electronic Medical Record) is an emerging technology that is highly-blended between non-IT and IT area. One of methodology to link non-IT and IT area is to construct databases. Nowadays, it supports before and after-treatment for patients and should satisfy all stakeholders such as practitioners, nurses, researchers, administrators and financial department and so on. In accordance with the database maintenance, DAS (Data as Service) model is one solution for outsourcing. However, there are some scalability and strategy issues when we need to plan to use DAS model properly. We constructed three kinds of databases such as plain-text, MS built-in encryption which is in-house model and custom AES (Advanced Encryption Standard) - DAS model scaling from 5K to 2560K records. To perform custom AES-DAS better, we also devised Bucket Index using Bloom Filter. The simulation showed the response times arithmetically increased in the beginning but after a certain threshold, exponentially increased in the end. In conclusion, if the database model is close to in-house model, then vendor technology is a good way to perform and get query response times in a consistent manner. If the model is DAS model, it is easy to outsource the database, however, some technique like Bucket Index enhances its utilization. To get faster query response times, designing database such as consideration of the field type is also important. This study suggests cloud computing would be a next DAS model to satisfy the scalability and the security issues.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.315-318
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• 2017

Nowadays, Distributed Denial of Service (DDoS) attacks have gained increasing popularity and have been a major factor in a number of massive cyber-attacks. It could easily exhaust the computing and communicating resources of a victim within a short period of time. Therefore, we have to find the method to detect and prevent the DDoS attack. Recently, there have been some researches that provide the methods to resolve above problem, but it still gets some limitations such as low performance of detecting and preventing, scope of method, most of them just use on cloud server instead of network, and the reliability in the network. In this paper, we propose solutions for (1) handling multiple DDoS attacks from multiple IP address and (2) handling the suspicious attacks in the network. For the first solution, we assume that there are multiple attacks from many sources at a times, it should be handled to avoid the conflict when we setup the preventing rule to switches. In the other, there are many attacks traffic with the low volume and same destination address. Although the traffic at each node is not much, the traffic at the destination is much more. So it is hard to detect that suspicious traffic with the sampling based method at each node, our method reroute the traffic to another server and make the analysis to check it deeply.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• Convergence Security Journal
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• v.16 no.7
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• pp.21-29
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• 2016

Our society is currently exposed to environment of various information that is exchanged real time through networks. Especially regarding medical policy, the government rushes to practice remote medical treatment to improve the quality of medical services for citizens. The remote medical practice requires establishment of medical information based on big data for customized treatment regardless of where patients are. This study suggests establishment of regional medical cluster along with defense and protection cooperation models that in case service availability is harmed, and attacks occur, the attacks can be detected, and proper measures can be taken. For this, the study suggested forming networks with nationwide local government hospitals as regional virtual medical cluster bases by the same medical information system. The study also designed a mutual cooperation security model that can real time cope with IP Spoofing attack that can occur in the medical cluster and DDoS attacks accordingly, so that the limit that sole system and sole security policy have can be overcome.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.101-109
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• 2014

Medical technology development and increase the income level of a "Long and healthy Life=Wellness," with the growing interest in actively promoting and maintaining health and wellness has become enlarged. In addition, the demand for personalized health care services is growing and extensive medical moves of big data, disease prevention, too. In this paper, the main interest in the market, highlighting wellness in order to support big data-driven healthcare quality through patient-centered medical services purposes. Patients with drug dependence treatment is not to diet but to improve disease prevention and treatment based on analysis of big data. Analysing your Tweets-daily information and wellness disease prevention and treatment, based on the purpose of the dictionary. Efficient big data analysis for node while increasing processing time experiment. Test result case of total access time efficient 26% of one node to three nodes and case of data storage is 63%, case of data aggregate is 18% efficient of one node to three nodes.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.1-8
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• 2017

Lightweight Encryption Algorithm(LEA) was developed by National Security Research Institute(NSRI) in 2013 and targeted to be suitable for environments for big data processing, cloud service, and mobile. LEA specifies the 128-bit message block size and 128-, 192-, and 256-bit key sizes. In this paper, block cipher LEA algorithm which can encrypt and decrypt 128-bit messages is designed using Verilog-HDL. The designed IP for encryption and decryption has a maximum throughput of 874Mbps in 128-bit key mode and that of 749Mbps in 192 and 656Mbps in 256-bit key modes on Xilinx Vertex5. The cryptographic IP of this paper is applicable as security module of the mobile areas such as smart card, internet banking, e-commerce and IoT.

• Spatial Information Research
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• v.20 no.3
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• pp.67-81
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• 2012

Due to the recent advancement of cloud computing, the research on database outsourcing has been actively done. Moreover, the number of users who utilize Location-based Services(LBS) has been increasing with the development in w ireless communication technology and mobile devices. Therefore, LBS providers attempt to outsource their spatial database to service provider, in order to reduce costs for data storage and management. However, because unauthorized access to sensitive data is possible in spatial database outsourcing, it is necessary to study on the preservation of a user's privacy. Thus, we, in this paper, propose a spatial data encryption scheme to produce outsourced database from an original database. We also propose a k-Nearest Neighbor(k-NN) query processing algorithm that efficiently performs k-NN by using the outsourced database. Finally, we show from performance analysis that our algorithm outperforms the existing one.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.540-542
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• 2014

The Universal Plug and Play (UPnP) specification permits networked devices to discover each other and to provide diverse services in the home network environment. Recently, new paradigms such as mobile connected computing, cloud-based service delivery, smart device content sharing, and Internet of Things (IoT) have emerged, but the home network based UPnP shows functional limitations in supporting such paradigms. To support them, the UPnP Forum has recently extended the capabilities of the existing UPnP, calling it UPnP+. In this paper, the UPnP Device Architecture V2.0 (UDA 2.0), which forms the basis of UPnP+, is presented. We present how UDA 2.0 enables the expansion of the home network to wide-area networks and non-IP device domains.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.241-247
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• 2021

As a way to solve the side effects of urban development, a smart city with information and communication technology converges in the city is being built. For this, a smart city platform should support the development and integration of smart city services. Therefore, the underlying technology and the functional and non-functional requirements that the smart platform must support were analyzed. As a result of this, we classified the Internet of Things, cloud computing, big data and cyber-physical systems into four categories as the underlying technologies supported by the smart city platform, and derived the functional and non-functional requirements that can be implemented and the reference model of the smart city platform. The reference model of the smart city platform is used for decision-making on investment in infrastructure technology and the development scope of services according to functional or non-functional requirements to solve specific city problems for city managers. It provides platform developers with guidelines to identify and determine the functional and non-functional requirements and implementation technologies of software platforms for building smart cities.