• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.239-240
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• 2012

When users access to use the computing resources in the cloud, they expect specific quality of service (QoS) which should be guaranteed by the service provider. Meanwhile, the service provider should adopt proper schemes to enhance the resource utilization. In this thesis, we propose the MapChem-Broker which aims to satisfy users' QoS requirements as well as enhance the resource utilization by controlling the provision of VM resources in the cloud. On the experimental cloud testbed, we compare the proposed scheme with an existing one for VM resource provisioning. Results show that the proposed scheme outperforms the existing one.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.132-144
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• 2020

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.115-122
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• 2021

In the recent past enormous enterprise applications have migrated into the cloud computing (CC). The researchers have contributed to this ever growing technology and as a result several innovations strengthened to offer the quality of service (QoS) as per the demand of the customer. It was treated that management of resources as the major challenge to offer the QoS while focusing on the trade-offs among the performance, availability, reliability and the cost. Apart from these regular key focuses to meet the QoS other key issues in CC are data integrity, privacy, transparency, security and legal aspects (DIPTSL). This paper aims to carry out the literature survey by reflecting on the prior art of the work with regard to QoS in CC and possible implementation of block chain to implement decentralised CC solutions governing DIPTSL as an integral part of QoS.

• 디지털콘텐츠학회 논문지
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• 제15권3호
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• pp.413-423
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• 2014

최근 클라우드 컴퓨팅은 대학의 정보시스템 구축에도 영향을 미치고 있다. 대학 학사정보시스템을 클라우드 컴퓨팅 환경으로 설계할 때 자원낭비와 서비스품질을 고려하여 적정한 처리용량을 산정하는 것은 매우 중요한 일이다. 사용자가 기대하는 서비스품질을 만족시키는 클라우드 컴퓨터의 처리용량은 사용자 트랜잭션의 발생 패턴과 자원요구 특성에 근거하여 예측해야 한다. 본 논문에서는 대학 학사업무에서 발생하는 실제 트래픽을 분석하고 특정한 평균 응답시간을 만족시키는 클라우드 컴퓨터의 처리용량을 산출하는 기법을 제시한다. 이를 위해 학사업무 클라우딩 서비스 모델을 정립하고 대학 학사업무의 실제 운용데이터로부터 도출한 트래픽 패턴과 자원요구 특성을 적정용량 분석 모델에 적용하여 현실적인 값들을 유도한다. 제시된 서비스 모델과 실제 운용 데이터를 바탕으로 한 트래픽과 적정용량의 분석 결과는 유사한 규모의 대학 정보시스템 진화에 충분히 활용될 수 있다.

• 대한조선학회논문집
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• 제58권5호
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

• 한국콘텐츠학회논문지
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• 제19권4호
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• pp.79-90
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• 2019

최근 들어 다수의 클라우드 서비스 제공자가 클라우드 컴퓨팅 서비스를 제공함으로써 각 제공자는 더 많은 사용자를 확보하기 위해 치열한 경쟁을 벌이고 있다. 서비스 제공자별 컴퓨팅 자원의 구성 및 서비스 제공 부하가 다르기 때문에 사용자는 다양한 수준의 서비스 품질을 경험할 수 있다. 따라서 클라우드 서비스 시장에서 더 많은 사용자를 확보하여 수익을 최대화하기 위해서는 서비스 품질에 대한 가장 합리적인 가격을 결정하는 것이 매우 중요하다. 본 연구에서는 두 명의 서비스 제공자가 존재하는 멀티미디어 클라우드 서비스 시장에서 두 제공자 간 서비스 가격 경쟁에 대해 다룬다. 두 명의 클라우드 서비스 제공자가 최적의 가격을 결정하여 상호 경쟁하고 자신의 이익을 최대화할 수 있는 가격 산정 방법을 비협력 게임 이론으로 설명한다. 이를 위해 멀티미디어 클라우드 서비스의 제공 프로세스를 대기행렬 시스템으로 모형화하고, 분석 결과를 바탕으로 복점 멀티미디어 클라우드 서비스 시장에서 가격 경쟁 문제를 제안한다.

• 한국통신학회논문지
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• 제38B권3호
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• pp.163-171
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• 2013

최근 HD급 동영상이나 3D 어플리케이션과 같은 이전보다 저사양, 모바일 단말에서는 구동하기 힘든 프로그램들에 대한 이용 요구가 확대되면서 처리해야 할 콘텐츠 데이터들이 고용량화 되고 있다. 클라우드 기반의 VDI(Virtual Desktop Infrastructure) 서비스는 이를 처리하기 위해 효율적인 데이터 처리 능력이 필요해졌으며 QoE(Quality of Experience) 보장을 위한 성능 개선 연구가 이슈가 되고 있다. 본 논문에서는 H/W 성능이 향상되어 CPU와 GPU를 탑재한 Thick Client기반의 3가지 Thick-Thin간 VDI 자원 공유 및 위임이 가능한 VDI 서비스에 대해 제안하며, VDI 서비스 성능의 개선을 위해 CPU와 GPU가 혼합된 Heterogeneous 멀티코어 환경에서 CPU와 GPU 병렬 처리 기법인 OpenMP와 CUDA를 활용하여 VDI 서비스 최적화 방안을 제안하고 기존의 VDI와 비교한 성능을 거론한다.

• IEIE Transactions on Smart Processing and Computing
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• 제2권6호
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• pp.367-372
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• 2013

Cloud computing technology has been developing at an increasing expansion rate. Today most of firms are using this technology, making improving the quality of service one of the most important issues. To achieve this, the system must operate efficiently with less idle time and without deteriorating the customer satisfaction. This paper focuses on enhancing the efficiency of a conventional Genetic Algorithm (GA) for task scheduling in cloud computing using Fuzzy Logic (FL). This study collected a group of task schedules and assessed the quality of each task schedule with the user expectation. The work iterates the best scheduling order genetic operations to make the optimal task schedule. General GA takes considerable time to find the correct scheduling order when all the fitness function parameters are the same. GA is an intuitive approach for solving problems because it covers all possible aspects of the problem. When this approach is combined with fuzzy logic (FL), it behaves like a human brain as a problem solver from an existing database (Memory). The present scheme compares GA with and without FL. Using FL, the proposed system at a 100, 400 and 1000 sample size*5 gave 70%, 57% and 47% better improvement in the task time compared to GA.