• 대한임베디드공학회논문지
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• 제11권3호
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5694-5711
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• 2017

The technology of cloud computing is growing very quickly, thus it is required to manage the process of resource allocation. In this paper, load balancing algorithm based on honey bee behavior (LBA_HB) is proposed. Its main goal is distribute workload of multiple network links in the way that avoid underutilization and over utilization of the resources. This can be achieved by allocating the incoming task to a virtual machine (VM) which meets two conditions; number of tasks currently processing by this VM is less than number of tasks currently processing by other VMs and the deviation of this VM processing time from average processing time of all VMs is less than a threshold value. The proposed algorithm is compared with different scheduling algorithms; honey bee, ant colony, modified throttled and round robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of execution time, response time, makespan, standard deviation of load, and degree of imbalance.

• 한국정보과학회논문지:시스템및이론
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• 제35권5호
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• pp.187-198
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• 2008

본 논문은 그리드 서비스 환경에서 효율적인 자원 관리를 위한 프레임워크를 개발한다. 자원 관리는 그리드 서비스의 핵심이며, 자원의 가변적 특성에 적응적으로 대처하기 위한 프레임워크인 GridRMF(Grid Resource Management Framework)를 모델링하고 개발한다. GridRMF는 그리드 자원의 참여 의도에 따라 계층적으로 관리한다. 계층적 자원 관리는 가상 조직 관리를 위한 VMS(Virtual organization Management System)와 메타데이타 관리를 위한 RMS(Resource Management System)로 관리 도메인을 구분한다. VMS는 최적 가상 조직 선택 전략에 의해 자원을 중개하며, LRM(Local Resource Manager) 자동 회복 전략에 의해 가상 조직의 결함에 대처한다. RMS는 자원 상태 모니터링 정보를 적응적 성능 기반 작업 할당 알고리즘에 적용하여 부하균등화와 결함에 대처한다.

• ETRI Journal
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• 제43권1호
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• pp.53-63
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• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.1998-2014
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• 2016

In recent years, cloud computing services based on smart phones and other mobile terminals have been a rapid development. Cloud computing has the advantages of mass storage capacity and high-speed computing power, and it can meet the needs of different types of users, and under the background, mobile cloud computing (MCC) is now booming. In this paper, we have put forward a new classification-based virtual machine placement (CBVMP) algorithm for MCC, and it aims at improving the efficiency of virtual machine (VM) allocation and the disequilibrium utilization of underlying physical resources in large cloud data center. By simulation experiments based on CloudSim cloud platform, the experimental results show that the new algorithm can improve the efficiency of the VM placement and the utilization rate of underlying physical resources.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.171-174
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• 2013

Virtualization technology opened a new era in the field of Data intensive, Grid and Cloud Computing. Today's Data centers are smarter than ever leveraging the Virtualization technology. In response to that, Dynamic consolidations of Virtual Machines (VMs) allow efficient resource management by live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), leads to stipulation in dealing with energy-performance trade-off as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a Cloud Based CDN approach for allocation of VM that aims to maximize the client-level SLA. Our experiment result demonstrates significant enhancement of SLA at certain level.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제1권3호
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• pp.137-148
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• 2012

사용자들은 자신의 작업을 처리하기 위해 자신에게만 한정된 가상 컴퓨팅 자원을 클라우드 서비스 제공자로부터 할당 받아 타 사용자로부터 독립된 환경에서 작업을 처리하게 된다. 이를 자동화된 방법으로 최적화를 대신 수행해주기 위한 모델로 브로커 미들웨어가 제시되었고 마감시간을 만족하는 이내에서 자원 이용률을 높이는 접근법으로 필요 가상 머신의 숫자를 줄여 비용을 절약한다. 이를 다루는 많은 논문들에서 작업 스케줄링은 기존 사용자들간의 독립을 보장하여 하나의 가상 머신이 하나의 작업에 한정된 가상 머신에서 처리하는 방식으로 다루어지고 있다. 하지만 기존의 SRSV 방식에서는 높은 정도의 다중 프로그래밍 작업이 아닐 경우 시스템을 효율적으로 사용하지 못한다. 이에 본 논문에서는 해당 자원을 마감시간과 스래싱(thrashing), 문맥 전환(context switching)에 따른 성능 저하를 고려한 상태에서 다중 프로그래밍 정도를 높여 낭비되는 자원을 최소화하여 비용을 절약하려고 한다. 실험 결과를 통해 제안하는 방법이 제약조건 이내에서 기존의 방식에 비해 좀 더 좋은 가격 대비 성능을 가지는 것을 보인다.

• 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.