• The Journal of Korean Association of Computer Education
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• v.11 no.5
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• pp.67-76
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• 2008

Grid computing has a large scale virtual computing environment that enables a collaborative processing through sharing resources of geographically distributed organizations. In recent year, the development of wireless networks and mobile devices enables mobile devices to consider as a resource of the grids. However, there are some problems such as low performance of processors, small capacity of storages, limited capacity of battery, and low bandwidth. In this paper, to overcome these limitations occurred in mobile grid environments, we proposed a proxy-based mobile grid system. Our proposed system enables mobile devices to play roles as a resource consumer and a resource provider and to collaborate with wired grids through a mobile router. Also, we propose an adaptive job scheduling scheme to cope with context changes of mobile devices and compare our scheduling scheme with other scheduling schemes using a simulation tool, SimGrid, to verify the validity of our job scheduling scheme.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.6
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• pp.608-618
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• 2000

We consider the problem of placing resources in a distributed computing system so that certain performance requirements may be met while minimizing the number of required resource copies, irrespective of node or link failures. To meet the requirements for high performance and high availability, minimum number of resource copies should be placed in such a way that each node has at least two copies on the node or its neighbor nodes. This is called the fault-tolerant resource placement problem in this paper. The structure of a parallel or a distributed computing system is represented by a graph. The fault-tolerant placement problem is first transformed into the problem of finding the smallest fault-tolerant dominating set in a graph. The dominating set problem is known to be NP-complete. In this paper, searching for the smallest fault-tolerant dominating set is formulated as a state-space search problem, which is then solved optimally with the well-known A* algorithm. To speed up the search, we derive heuristic information by analyzing the properties of fault-tolerant dominating sets. Some experimental results on various regular and random graphs show that the search time can be reduced dramatically using the heuristic information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1567-1587
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• 2014

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.253-273
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• 2015

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Categorized as one kind of cloud computing, SaaS is computing resource and software sharing model with can be accessed via the internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level and performance in company. Therefore this research limited cloud services to SaaS especially focused on collaborative application service, and attempts to identify the factors which impact the performance of collaboration and intention to use. This study adopts technological factors of cloud SaaS services and factors of task characteristics to explore the determinants of collaborative performance and intention to use. An experimental study using student subjects with Google Apps provided empirical validation for our proposed model. Based on 337 data collected from respondents, the major findings are following. First, the characteristics of cloud computing services such as collaboration support, service reliability, and ease of use have positive effects on perceived usefulness of collaborative application while accessability, service reliability, and ease to use have positive effects on intention to use. Second, task interdependence has a positive effects on collaborative performance while task ambiguity factor has not. Third, perceived usefulness of collaborative application have positive effects on intention to use.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.9-17
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• 2018

Heterogeneous high performance computing systems are gaining acceptance as the environments for computational scientific simulations of various application fields. Those computing systems, however, have been mostly used with the legacy consoles, resulting in the severe decrement of accessibility and usability of heterogeneous computing assets. To solve this problem, this paper presents the design and implementation of web-based computational science simulation management program. The proposed program provides fundamental primitives including user authentication, data management, physical/virtual computing resource management, job management, etc. that can be used to manage different kinds of simulations efficiently, and also offers highly extensible feature through a modular plug-in architecture. We also present the best practical examples of applications (e.g., scientific simulation education and bio-medical) to confirm our program's effectiveness.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.440-448
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• 2019

The Internet of Things (IoT) is one of the main enablers for situation awareness needed in accomplishing smart cities. IoT devices, especially for monitoring purposes, have stringent timing requirements which may not be met by cloud computing. This deficiency of cloud computing can be overcome by fog computing for which fog nodes are placed close to IoT devices. Because of low capabilities of fog nodes compared to cloud data centers, fog nodes may not be deployed with all the services required by IoT devices. Thus, in this article, we focus on the issue of fog service placement and present the recent research trends in this issue. Most of the literature on fog service placement deals with determining an appropriate fog node satisfying the various requirements like delay from the perspective of one or more service requests. In this article, we aim to effectively place fog services in accordance with the pre-obtained service demands, which may have been collected during the prior time interval, instead of on-demand service placement for one or more service requests. The concept of the logical fog network is newly presented for the sake of the scalability of fog service placement in a large-scale smart city. The logical fog network is formed in a tree topology rooted at the cloud data center. Based on the logical fog network, a service placement approach is proposed so that services can be placed on fog nodes in a resource-effective way.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Asia pacific journal of information systems
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• v.27 no.4
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• pp.191-215
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• 2017

This study investigated the antecedents and the performance impact of two types of Smartphone functions (communication vs. computing functions) in organizational environment and the moderating impact of Smartphone users' organizational roles. More specifically, identifying two distinct types of Smartphone functions such as communication functions and computing functions (including informational, social network, and resource management functions), we investigated the impact of three antecedents (Smartphone dependency, task mobility, and perceived critical mass) on the use of the two Smartphone functions and how organizational workers' perceived performance gains differ by using these two different Smartphone functions for their workplace activities. We tested our hypotheses with survey data collected from 176 organizational workers. Our findings suggest that Smartphone dependency, task mobility and perceived critical mass of Smartphone use are significantly associated with the use of the two different functions, and that the use of computing functions is more strongly associated with perceived performance gain than the use of communication functions. We also found that managerial roles played by individual workers differently moderate the impact of Smartphone use on perceived performance gain. The present findings enable researchers and practitioners to better understand the impact of Smartphone use in workplaces.