• 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:Information Networking
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• v.33 no.3
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• pp.247-256
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• 2006

Among the resource provisioning mechanisms for the hose based Virtual Private Network (VPN) Quality of Service (QoS ), VPN-specific state provisioning allows the service provider to obtain highest resource multiplexing gains. However, dynamic and automatic resource reservation for the VPN-specific state provisioning is difficult due to the lack of appropriate resource reservation protocol. Furthermore, users of a VPN may experience unfair usage of resources among themselves since the reserved resources of a VPN are shared by the VPN users in a similar way that the traditional LAN bandwidth is shared by the attached hosts. In this paper, we propose a resource reservation protocol and a traffic service mechanism, which not only enable dynamic and automatic resource reservation according to the VPN-specific state provisioning algorithm, but also enforce the fair usage of reserved resources among the users of a VPN in case of congestion.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.310-328
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• 2022

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.1-10
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• 2014

Cloud computing provides flexible and efficient mass data analysis platform. In this paper, we define a new resource provisioning architecture in the public cloud, named PCIA. In addition, we provide a service model of PCIA and its new naming scheme. Our model selects the proper number of physical or virtual resources based on the requirements of clients. By the analysis of performance variation in the PCIA, we evaluate the relationship between performance variation and resource provisioning, and we present key standards for cloud system constructions, which can be an important resource provisioning criteria for both cloud service providers and clients.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.77-86
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• 2013

Cloud computing is one of the distributed computing environments and utilizes several computing resources. Cloud environment uses a virtual machine to process a requested job. To balance a workload and process a job rapidly, cloud environment uses a provisioning technique and assigns a task with a status of virtual machine. However, a scheduling method for cloud computing requires a definition of virtual machine availabilities, which have an obscure meaning. In this paper, we propose Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation(FVPRE). FVPRE analyzes a state of every virtual machine and actualizes a value of resource availability. Thus FVPRE provides an efficient provisioning scheduling with a precise evaluation of resource availability. FVPRE shows a high throughput and utilization for job processing on cloud 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 Internet of Things and Convergence
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• v.9 no.4
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• pp.9-14
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• 2023

Recently, resource optimization research has been actively conducted in enterprises and data centers to manage the rapid growth of big data. In particular, thin provisioning, which allocates a large number of resources compared to fixedly allocated storage resources, has the effect of reducing initial costs, but as the number of resources actually used increases, the cost effectiveness decreases and the management cost for allocating resources increases. In this paper, we propose a technique that divides the physical blocks of flash memory into single-bit cells and multi-bit cells, formats them with a hybrid technique, and manages them by dividing frequently used hot data and infrequently used cold data. The proposed technique has the advantage that the physical and allocated resources are the same, such as thick provisioning, and can be used without additional cost increase, and the underutilized resources can be managed in multi-bit cell blocks, such as thin provisioning, which can allocate more resources than typical storage devices. Finally, we estimated the resource optimization effectiveness of the proposed technique through experiments based on simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5404-5424
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• 2018

This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.238-256
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• 2016

Modern web services have been broadly deployed on the Internet. Most of these services use multi-tier architecture for flexible scaling and software reusability. However, managing the performance of multi-tier web services under dynamic and unpredictable workload, and different resource demands in each tier is a critical problem for a service provider. When offering quality of service assurance with least resource usage costs, web service providers should adopt self-adaptive resource provisioning in each tier. Recently, a number of rule- and model-based approaches have been designed for dynamic resource management in virtualized data centers. This survey investigates the challenges of resource provisioning and provides a competing assessment on the existing approaches. After the evaluation of their benefits and drawbacks, the new research direction to improve the efficiency of resource management and recommendations are introduced.