• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.484-503
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• 2016

Mobile cloud computing has recently become a new paradigm for the utilization of a variety of shared mobile resources via wireless network environments. However, due to the inherent characteristics of mobile devices, a limited battery life, and a network access requirement, it is necessary for mobile servers to provide a dynamic approach for managing mobile resources efficiently in mobile cloud computing environments. Since on-demand job requests occur frequently and the number of mobile devices is drastically increased in mobile cloud computing environments, a different mobile resource management method is required to maximize the computational power. In this paper, we therefore propose a cooperative, mobile resource sharing method that considers both the inherent properties and the number of mobile devices in mobile cloud environments. The proposed method is composed of four main components: mobile resource monitor, job handler, resource handler, and results consolidator. In contrast with conventional mobile cloud computing, each mobile device under the proposed method can be either a service consumer or a service provider in the cloud. Even though each device is resource-poor when a job is processed independently, the computational power is dramatically increased under the proposed method, as the devices cooperate simultaneously for a job. Therefore, the mobile computing power throughput is dynamically increased, while the computation time for a given job is reduced. We conduct case-based experiments to validate the proposed method, whereby the feasibility of the method for the purpose of cooperative computation is shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1777-1788
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• 1999

The portability of Java language makes it possible to use heterogeneous computers without re-compiling of application programs. Java applet can also be transported to other computers via Web browser. In this research, a Cooperative Web Computing Environment(CWCE) that uses idle computers on the Intranet for cooperative parallel computing work is suggested. The CWCE allows to use more than a manager computer that sends applets and manages communication between other computers. The number of manager computers can be determined according to the characteristics of computing environment and any chosen application program. It can reduce the amount of communication overhead for the application programs especially with synchronized communication. For the CWCE, a decision function to determine the managing level is provided. The CWCE turns out to be useful computing environment for the applications with less computation request ratio and multi-managing can help to reduce the communication overhead especially for the applications with a high ratio of synchronization purpose communications.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.61-72
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• 2006

Ubiquitous computing technologies become mature enough to be applied in acceptable ubiquitous services. In particular, in u-shopping area, personalized recommender systems which automatically collect the nomadic user-related context data and then provide them with products or shops in a flexible manner. However, legacy cooperative queries and context-aware queries so far do not come up with dynamically changing situations and ambiguous query commands, respectively. Hence, The purpose of this paper is to propose a personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among node instances, while considering the user's context data. To show the feasibility of the methodology proposed in this paper, we have implemented a prototype system, CACO, in the area of site search in a large-scale shopping mall.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.35-42
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• 2011

Cooperative relay communication for wireless networks has been extensively studied due to its ability to mitigate fading effectively via spatial diversity. In this paper, we propose a balanced transmit scheme in cooperative relay communication with decode-and-forward DF) scheme. The proposed scheme selects the feedback bits to obtain the maximum cooperative diversity gain. The simulation results show that the proposed scheme improves the bit error rate BER) performance as compare with a conventional scheme.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.287-294
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• 2014

Cloud computing is a new method of computing and managing organizational information technology resources strategically. From the user perspective, it involves computing environment for accessing applications remotely, storing data, and supporting cooperative works. For the cloud computing to be effective in an organization, it should be accepted by individual users. In this paper we propose a research model, extending and modifying UTAUT model. We also test the validity of the model using the questionnaire from a sample of cloud computing services users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.30-35
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• 2022

Rendering is widely used for visual effects in animations and movies. Although rendering is computing-intensive, we observe that it accompanies heavy I/O because of large input data. This becomes technical hurdles for multi-node rendering performed on public cloud nodes. To reduce the overhead of data transmission in multi-node rendering, this paper analyzes the characteristics of rendering workloads, and presents the cooperative caching scheme for multi-node rendering. Our caching scheme has the function of synchronization between original data in local storage and cached data in rendering nodes, and the cached data are shared between multiple rendering nodes. We perform measurement experiments in real system environments and show that the proposed cooperative caching scheme improves the conventional caching scheme used in the network file system by 27% on average.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1551-1558
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• 2022

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.