• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.165-167
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• 2011

Xen과 같은 가상 머신에서 각 게스트 운영체제가 필요로 하는 CPU 요구량을 정확하게 측정하기는 어렵다. SEDF 스케줄러는 사용자가 각 게스트 운영체제의 CPU 할당량을 직접 입력하고 있다. 따라서 가변적인 부하를 가지고 있는 상황에서 게스트 운영체제의 스케줄링이 어렵다. 본 논문에서는 작업량이 가변적으로 변화하는 시스템의 QoS를 고려하여 실시간 태스크가 필요로 하는 CPU 자원을 효율적으로 할당하는 방법을 제안하였다. 실험을 통하여 제안한 방식이 가변적인 작업량에 대해서 효율적으로 동작됨을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.250-252
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• 2015

다양한 분야에서 활발히 연구되는 빅 데이터와 최근 이슈가 되고 있는 딥러닝(Deep-learning) 등은 컴퓨터공학 분야뿐만 아니라 다양한 분야와 접목하여 이에 대한 관심이 증가하고 있다. 대규모 클러스터를 통하여 빅데이터와 딥러닝 같은 계산 집약적인(computational-intensive) 작업을 빠르게 처리할 수 있다. 하지만 대규모 클러스터의 잦은 유휴상태는 클러스터의 활용률은 매우 낮아지게 한다. 본 논문에서는 작업 실행 시간 개선과 클러스터 활용 효율성을 향상시키는 HTCondor 클러스터를 위한 동적 자원 스케줄링 기법을 제안한다. 동적으로 자원 할당을 위해 가상머신으로 HTCondor 클러스터 환경을 구성하였으며, 가상머신의 관리를 위해 OpenStack을 사용하였다. OpenStack기반 HTCondor 클러스터 환경에서 HTCondor Python API와 OpenStack Python API를 사용하여 우리가 제안하는 동적 자원 스케줄링 기법을 구현하였으며, 실험을 통해 제안하는 기법의 성능 및 실현 가능성을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.20-29
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• 2006

The Java language has been established as one of the most widely used language due to its object-oriented programming, safety and flexibility and the garbage collection of the virtual machine has relieved programmers of many difficulties related to the memory management. In the embedded environment, Java is also prevalent, the virtual machine and garbage collector that takes into account the embedded environment is required. In this paper we manage the heap memory area by dividing into young generation and old generation, and we propose a garbage collector in which appropriate techniques are applied to each generation to utilize the different characteristics of each generation. Also, we propose the write barrier technique and double filtering technique for efficient garbage recognition, and double check method for determining and reclaiming the garbage with cyclic structure. The proposed method satisfies the embedded environment's requirements of fast object allocation, real time property, recollection of all the garbage, elimination of fragmentations and high locality.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.63-66
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• 2018

컨테이너 기술은 운영체제 수준 가상화 기술 중 하나로 하드웨어 레벨 가상화 기술에 비해 인스턴스의 빠른 생성 및 종료시킬 수 있는 특성이 있다. 이러한 특성은 직업 부하에 따라 인스턴스의 빠른 생성 및 종료시킬 수 있는 특성이 있다. 이러한 특성은 작업 부하에 따라 인스턴스의 수량을 동적으로 조정하는 오토스케일링 상황에서 유리하게 작용할 수 있다. 본 논문에서는 다수의 노드를 기반으로 구성된 컨테이너 기반의 오토스케일링 환경과 가상머신 기반의 오토스케일링 환경을 성능 측면에서 비교하고 컨테이너 기반 환경에서 자원 할당의 변화가 성능에 주는 영향을 측정 및 분석한다.

• The Journal of Information Technology
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• v.5 no.2
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• pp.27-34
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• 2002

The KVM garbage collector implemented in CLDC was generally based on the simple mark-sweep algorithm, but it is difficult to handle objects of varying size without fragmentation of the available memory. In this paper, we have designed and implemented a memory allocator based on the mark-sweep algorithm that minimizes the fragmentation by the method that determines the allocation position of free-space list according to object size. The experimental result shows that our algorithm reduce the fragmentation and improve the execution time than the existing algorithm.

• Journal of Digital Contents Society
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• v.12 no.4
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• pp.551-557
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• 2011

In this paper, we propose a stack allocation technique of Android Java objects in order to reduce the number of garbage collection which is one of major reason on Android performance degradation when running applications. The proposed technique is to allocate Java objects into stack rather than heap memory. To do so, stacked objects could escape the garbage collection process. We experiment the proposed technique in the latest Android 2.3 version. For the simulation, we take advantage of the well known Java benchmark, Caffeinemark, and our own. The result shows the performance degradation of Dalvik Virtual Machine execution time caused by the stack allocation of Java objects is very slight and the proposed method considerably reduces the frequency of garbage collection. This will increase application performance and give better user interfaces to Android phone users.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.302-308
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• 2013

Internet is becoming more common with increasing cloud services have been spreading rapidly. A SLA is agreements between service providers and customers of service providers is how to ensure quality of service. SLA in cloud computing environments from the perspective of the IT service providers to customer satisfaction increase for service quality, it will need to differentiate between competing carriers QoS guarantees with SLA is a very important factor. However, the study of SLA in the cloud is staying in its infancy. In this paper, SLA indices for cloud services defining and use them SLA Management System for QoS guarantee has been implemented. The proposed system using monitoring-based migration policy an open source-based cloud computing platform which Cluster Nodes load distribution of virtual machines assigned to their availability, response time, throughput analysis of what affects and is compared with existing cloud.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.546-548
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• 2011

컴퓨터 실습실에서 사용되고 있는 씬 클라이언트 PC는 개인별로 할당된 것이 아니라 다수의 사용자들이 공유하기 때문에 씬 클라이언트 PC를 관리하기 위한 별도의 관리시스템(Management System)이 필요하다. 본 논문에서는 컴퓨터 실습실 환경에서 가상머신에 원격으로 접속된 씬 클라이언트 PC의 전원관리를 효과적으로 할 수 있는 방법을 제안한다. 사용자가 이용하는 씬 클라이언트 PC에 대해 일정시간 동안 키보드/마우스의 입력이 없으면 해당 씬 클라이언트 PC로 메시지를 전송하여 사용 여부를 확인하고 해당 사용자로부터 아무런 응답이 없을 경우 씬 클라이언트 PC를 Shutdown 한다. 이러한 관리 방법은 사용되지 않는 씬 클라이언트 PC에 대해 불필요하게 낭비되는 전력을 줄일 수 있어 효율적으로 컴퓨터 실습실을 운영할 수 있을 것으로 예상된다.