• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• Journal of KIISE
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• v.44 no.5
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• pp.439-448
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• 2017

The workloads of large high-performance computing (HPC) scientific applications are steadily becoming "bursty" due to variable resource demands throughout their execution life-cycles. However, the over-provisioning of virtual resources for optimal performance during execution remains a key challenge in the scheduling of scientific HPC applications. While over-provisioning of virtual resources guarantees peak performance of scientific application in virtualized environments, it results in increased amounts of idle resources that are unavailable for use by other applications. Herein, we proposed a memory resource reconfiguration approach that allows the quick release of idle memory resources for new applications in OS-level virtualized systems, based on the applications resource-usage pattern profile data. We deployed a scientific workflow application in Docker, a light-weight OS-level virtualized system. In the proposed approach, memory allocation is fine-tuned to containers at each stage of the workflows execution life-cycle. Thus, overall memory resource utilization is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4177-4190
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• 2015

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5701-5722
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• 2018

The continuous increase in the cost of energy production and concerns for environmental sustainability are leading research communities, governments and industries to amass efforts to reduce energy consumption and global $CO_2$ footprint. Players in the information and communication industry are keen on reducing the operational expenditures (OpEx) and maintaining the profitability of cellular networks. Meanwhile, network virtualization has been proposed in this regard as the main enabler for 5G mobile cellular networks. In this paper, we propose a generic framework of slice resource provisioning and customized physical resource allocation for energy-efficiency and quality of service optimization. In resource slicing, we consider user demand and population resources provisioning scheme aiming to satisfy quality of service (QoS). In customized physical resource allocation, we formulate this problem with an integer non-linear programming model, which is solved by a heuristic algorithm based on minimum vertex coverage. The proposed algorithm is compared with the existing approaches, without consideration of slice resource constraints via system-level simulations. From the perspective of infrastructure providers, traffic is scheduled over a limited number of active small-cell base stations (sc-BSs) that significantly reduce the system energy consumption and improve the system's spectral efficiency. From the perspective of virtual network operators and mobile users, the proposed approach can guarantee QoS for mobile users and improve user satisfaction.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.984-998
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• 1999

최근 개인 휴대 통신에 대한 관심도가 증가하면서 B-ISDN (Broadband Integrated Services Digital Network)과 같은 기존의 유선 망에서 제공하던 다양한 멀티미디어 응용 지원을 무선 망으로 확장시키기 위한 연구가 활발히 진행되고 있다. 그러나 기존의 유선 망에서는 멀티미디어 응용 지원을 위해 QoS (Quality of Service) Provisioning에 관한 많은 연구가 되어 있으나 무선 망에서는 이동성과 무선 전파의 열악한 전송으로 인해 새로운 QoS Provisioning 방법에 관한 연구가 필수적이다. 본 논문에서는 이러한 무선 망의 특수성으로 인해 발생할 수 있는 서비스의 질 저하와 강제 종료를 줄임으로써 지속적인 QoS를 보장해 주고 한정된 무선 자원을 효율적으로 사용하며 처리에 의한 오버헤드를 줄이기 위하여 다음과 같은 세 가지 방법을 제안하였다. 첫째, 핸드오프 강제 종료율을 줄이기 위하여 대역폭 예약 방법을 사용하되 특정 셀의 트래픽 특성에 맞게 또한 시간대에 따른 트래픽 특성에 따라 예약 대역폭의 양을 조절하는 적응적 대역폭 예약 방법이다. 둘째, 많은 경우 각 셀의 트랙픽 변화는 일정한 주기로 변화한다는 특성에 따라 과거의 트래픽 정보를 이용하는 예측 기반의 대역폭 예약 방법이다. 마지막으로 호의 종류, 트래픽 특성, 단말기의 이동 속도에 따라 다른 우선 순위에 의해 호 수락 제어를 수행하는 우선 순위 기반의 호 수락 제어를 제안하였다. 시뮬레이션을 통하여 기존에 제안된 방법과 성능 비교하여, 요구되는 수준의 QoS 보장과 효율적인 자원의 사용, 요구되는 처리비용의 최소화를 통해 전체 시스템의 성능 향상을 입증하였다.Abstract As interest in wireless hand-held terminals and in personal communications services increases recently, there have been broad studies on the ways to support multimedia applications provided in wired networks such as B-ISDN (Broadband Integrated Services Digital Network) in wireless networks. However, since many studies have focused on Quality of Service (QoS) Provisioning in wired networks to provide multimedia applications, new methods of QoS Provisioning are needed in wireless networks to resolve the problem of wireless channel fading and the difficulty of mobility occurred in wireless networks. This paper proposes three schemes of QoS Provisioning in wireless networks which will make continuous QoS guarantee and efficient use of limited wireless resources possible. The first scheme reserves bandwidth in proportion to the amount of real-time traffic in the neighbor cells to decrease the handoff dropping rate of delay sensitive real-time connections, adapting reserved bandwidth for efficient resource utilization. The second scheme is predictive bandwidth reservation scheme that utilizes the past handoff information. It can decrease overheads required to adapt bandwidth reservation. The last scheme is priority-based call admission control prioritizing traffic type (real-time traffic/ non-real-time traffic), connection type (new connection /handoff connection), and mobile terminal speed (fast mobile/slow mobile). Simulation results show that the proposed QoS Provisioning schemes improve the total system performance by achieving three goals - required QoS guarantee, higher bandwidth utilization and less overhead.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.12-15
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• 2012

Energy costs for operating and cooling computing resources in Cloud infrastructure have increased significantly up to the point where they would surpass the hardware purchasing costs. Thus, reducing the energy consumption can save a significant amount of management cost. One of major approach is removing hardware over-provisioning. In this paper, we propose a technique that facilitates power saving through reducing resource over provisioning based on virtualization technology. To this end, we use dynamic workload shaping to reschedule and redistribute job requests considering overall power consumption. In this paper, we present our approach to shape workloads dynamically and distribute them on virtual machines and physical machines through virtualization technology. We generated synthetic workload data and evaluated it in simulating and real implementation. Our simulated results demonstrate our approach outperforms to when not using no workload shaping methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2840-2853
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• 2015

A major challenge in network service providers is to provide adequate resources in service level agreements based on forecasts of future demands. In this paper, we address the problem of capacity provisioning in a network subject to demand uncertainty such that a network coded multicast is applied as the data delivery mechanism with limited budget to purchase extra capacity. We address some particular type of uncertainty sets that obtain a tractable constrained capacity provisioning problem. For this reason, we first formulate a mathematical model for the problem under uncertain demand. Then, a robust optimization model is proposed for the problem to optimize the worst-case system performance. The robustness and effectiveness of the developed model are demonstrated by numerical results. The robust solution achieves more than 10% reduction and is better than the deterministic solution in the worst case.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.95-100
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• 2022

Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.133-135
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• 2018

In these days, providing flexible and personalized network services subject to customers' requirements becomes an interesting issue for network service providers. Moreover, because each network service provider own finite network resources and infrastructure, dynamic network provisioning is essential to leverage the limited network resources efficiently and effectively for supporting personalized network services. Recently, as the population of mobile devices increases, the location-awareness becomes as important as the QoS-awareness to provision a network service dynamically. In this paper, we propose a framework for providing location-aware dynamic network services. This framework includes the web user interface for obtaining customers' requirements such as locations and QoS, the network generator for mapping the requested locations and network infrastructure, the network path calculator for selecting routes to meet the requested QoS and the network controller for deploying a prepared network services into SDN(Software-Defined Networking) enabled network infrastructure.