• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.131-134
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• 2013

최근 클라우드 컴퓨팅의 발전과 상업적인 성공과 함께 클라우드 자원의 이용률을 최대로 유지하면서 에너지를 효율적으로 사용하기 위한 연구에 대한 관심이 커지고 있다. 자원의 사용률이 최대로 높아지게 되면 에너지 소비량이 급격하게 증가하여 많은 에너지를 사용하게 되므로 자원의 사용율과 에너지 사용은 트레이드오프 관계를 가지게 된다. 따라서 본 논문에서는 자원의 최대 사용 및 효율적인 에너지 사용을 위해 에너지 소비가 최적이 되는 자원 이용률의 임계값을 찾기 위한 연구를 수행하였다. 실험을 위해 자원 중 가장 많은 에너지를 소비하는 CPU를 이용하였고, 전력 측정을 위해 KEM2500 전력계와 ThrottleStop_500 프로그램을 사용하였다. 실험 결과 CPU 사용률이 약 90%일 때 에너지 사용량이 급격하게 증가하였으며, 기존의 평균 자원 이용률과 비교했을 때 12.3% 정도의 전기량이 더 소모됨을 확인하였다. 따라서 클라우드 컴퓨팅에서 CPU 자원의 이용률이 90%일 때 에너지가 최적이라고 할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.564-566
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• 2011

최근 에너지 소비의 지속적 증가 및 에너지 가격의 급격한 상승으로 그린 IT 도입 운영이 필수적인 요소로 인식됨에 따라 서버 발열 및 데이터센터 에너지 절감을 위해 IT 인프라가 클라우드 컴퓨팅 플랫폼으로 대체 되어가고 있다. 본 연구에서는 오픈 소스 기반 클라우드 플랫폼을 구축하고 클라우드 컴퓨팅 노드의 다양한 자원 값을 이용한 전력제어 스케줄링 알고리즘을 제안하였다. 이 알고리즘은 클라우드 노드의 자원 효율을 높이면서 전력 소모를 절감하여 그린 IT 실현에 기여할 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.360-363
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• 2012

Recently green IT is considered as an essential element due to continuous consumption of energy and abrupt oil price. Thus, IT infrastructure is being replaced with cloud computing platform in oder to reduce server heat and energy consumption of data centers. In this paper, we implement an open source-based cloud platform and propose a resource scheduling algorithm for cloud VDI service using node clustering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.793-796
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• 2012

Recently adoption of the Green IT is regarded as an essential element in order to decrease server heat and save energy in data center because of continuous increase of energy consumption and energy price. Consequently the conventional IT infrastructure is replaced with cloud computing platform. In this paper, we have implemented a Linux virtual server load balancing in open source-based cloud platform and the performance of the LVS load balancing is analyzed.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.1-9
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• 2017

Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

• Journal of KIISE
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• v.42 no.6
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• pp.707-712
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• 2015

Mobile cloud computing is a technology that uses cloud services to overcome resource constrains of a mobile device, and it applies the computation offloading scheme to transfer a portion of a task which should be executed from a mobile device to the cloud. If the communication cost of the computation offloading is less than the computation cost of a mobile device, the mobile device commits a certain task to the cloud. The previous cost analysis models, which were used for separating functions running on a mobile device and functions transferring to the cloud, only considered the amount of data transfer and response time as the offloading cost. In this paper, we proposed a new task partitioning scheme that considers the frequency of function calls and data synchronization, during the cost estimation of the computation offloading. We also verified the energy efficiency of the proposed scheme by using experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.770-773
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• 2010

최근 큰 관심을 모으고 있는 스마트 그리드는 그린 에너지 환경 구현을 위한 기반 기술로 에너지 효율을 최적화하고자 하는 차세대 전략망이다. 스마트 그리드의 다양한 활용 가능성에도 불구하고 구조적 특징과 상호 운용성 표준의 부재로 인해 신뢰적인 인증을 보장하지 못한다. 이로 인해 네트워크의 신뢰성을 약화시키는 요인으로 작용하며, 많은 보안상의 문제를 야기한다. 따라서, 신뢰적인 클라우드 기반 스마트 그리드 환경을 구현하기 위하여 표준 및 정책 제정과 안전한 데이터 통신을 위한 보안 메커니즘 개발 및 인증 기술 개발이 필요하다. 본 논문에서는 클라우드 기반 스마트 그리드와 표준화 동향 및 클라우드 기반 스마트 그리드 환경에서의 보안기술을 살펴보고 이를 해결하기 위한 대안을 제시함을 목표로 한다.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.39-48
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• 2013

A cloud system has to deal with highly variable workloads resulting from dynamic usage patterns in order to keep the QoS within the predefined SLA. Aside from the aspects regarding services, another emerging concern is to keep the energy consumption at a minimum. This requires the cloud providers to consider energy and performance trade-off when allocating virtualized resources in cloud data centers. In this paper, we propose a resource provisioning approach based on dynamic thresholds to detect the workload level of the host machines. The VM selection policy uses utilization data to choose a VM for migration, while the VM allocation policy designates VMs to a host based on its service reputation. We evaluated our work through simulations and results show that our work outperforms non-power aware methods that don't support migration as well as those based on static thresholds and random selection policy.