• Journal of Computing Science and Engineering
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• v.3 no.2
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• pp.127-141
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• 2009

In this paper, we describe our vision of a "Just-in-time" initiative to solve the Data Interoperability Problem (a.k.a. INTEROP.) We provide an architectural overview of our initiative which draws upon existing technologies to develop an immersive and collaborative approach which aims at empowering data stakeholders (e.g., data producers and data consumers) with integrated tools to interact and collaborate with each other while directly manipulating visual representations of their data in an immersive environment (e.g., implemented via Second Life.) The semantics of these visual representations and the operations associated with the data are supported by ontologies defined using the Common Logic Framework (CL). Data operations gestured by the stakeholders, through their avatars, are translated to a variety of generated resources such as multi-language source code, visualizations, web pages, and web services. The generality of the approach is supported by a plug-in architecture which allows expert users to customize tasks such as data admission, data manipulation in the immersive world, and automatic generation of resources. This approach is designed with a mindset aimed at enabling stakeholders from diverse domains to exchange data and generate new knowledge.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

• Korean Journal of Computational Design and Engineering
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• v.10 no.2
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• pp.143-150
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• 2005

A design framework to employ the multidisciplinary design optimization technologies on a computer system has been developed and is named as the Extensible Multidisciplinary Design Integration and Optimization System (EMDIOS). The framework can not only effectively solve complex system design problems but also conveniently handle MDO problems. Since the EMDIOS exploits both state-of-the-art of computing capabilities and sophisticated optimization techniques, it can overcome many scalability and complexity problems. It can make users who are not even familiar with the optimization technology use EMDIOS easily to solve their design problems. The client of EMDIOS provides a front end for engineers to communicate the EMDIOS engine and the server controls and manages various resources luck as scheduler, analysis codes, and user interfaces. EMDIOS client supports data monitoring, design problem definition, request for analyses and other user tasks. Three main components of the EMDIOS are the Engineering Design Object Model which is a basic idea to construct EMDIOS, EMDIOS Language (EMDIO-L) which is a script language representing design problems, and visual modeling tools which can help engineers define design problems using graphical user interface. Several example problems are solved and EMDIOS has shown various capabilities such as ease of use, process integration, and optimization monitoring.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.33-41
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• 2018

In recent years, technology is rapidly changing to support new service consumption and distribution models in multimedia service systems and hybrid delivery of media services is a key factor for enabling next generation multimedia services. This phenomenon can lead to rapidly increasing network traffic and ultimately has a direct and aggravating effect on the user's quality of service (QOS). To address the issue, we propose a novel system architecture to provide smart hybrid media services efficiently. The architecture is designed to apply the software-defined networking (SDN) method, detect changes in traffic, and combine the data, including user data, service features, and computation node status, to provide a service schedule that is suitable for the current state. To this end, the proposed architecture is based on 2-level scheduling, where Level-1 scheduling is responsible for the best network path and a computation node for processing the user request, whereas Level-2 scheduling deals with individual service requests that arrived at the computation node. This paper describes the overall concept of the architecture, as well as the functions of each component. In addition, this paper describes potential scenarios that demonstrate how this architecture could provide services more efficiently than current media-service architectures.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.989-998
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• 2011

Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. One of the fundamental challenges in geographically distributed clouds is to provide efficient algorithms for supporting inter-cloud data management and dissemination. In this paper, we propose a group quorum system (GQS)-based dissemination for improving the interoperability of inter-cloud in time-critical event dissemination service, such as computing policy updating, message sharing, event notification and so forth. The proposed GQS-based method organizes these distributed clouds into a group quorum ring overlay to support a constant event dissemination latency. Our numerical results show that the GQS-based method improves the efficiency as compared with Chord-based and Plume methods.

• International Journal of Contents
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• v.2 no.1
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• pp.9-16
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• 2006

Recently many researches are investigating for ubiquitous computing and network. In the real world many sensor devices must be equipped to provide many services for users. To make computing environment easy and more user friendly, middleware system not only hides all complexities (network, system, services, etc), but also needs to have efficient context inference scheme and system reconfiguration capability. In this paper we suggest context-aware middleware design for sensor network which provides efficient computing environment for end-users. We also present XML based implementation of our system.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.71-85
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• 2009

Nowadays the research and development of various middleware for the ubiquitous computing environment has been demanded and many application services have been provided. The practical ubiquitous computing environment is the world where a variety of services based on RFID/USN devices are integrated with each other and make synergy effects all together. In the end user perspectives, there should be a middleware platform and business logic framework collaborating various services all together to satisfy the user requirements. In addition, the platform should provide the user to describe a scenario based on various event to maximize the collaboration. In this paper, we have proposed business logic framework which is supporting collaboration between heterogeneous service systems based on the user's scenario. We have also tried to show the efficiency and scalability of the proposed framework by providing the result of a couple of tests.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• Asia pacific journal of information systems
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• v.3 no.1
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• pp.54-87
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• 1993

This exploratory study addressed the issue of effective EUC management, based on the sociotechnical theory. Extensive literature review gave birth to a set of critical success factors in EUC. With those factors, a basic EUC model was developed. Then the model was tested in the domestic industry to find its validity. It was also attemped to develop a structural equation model in which critical factors for sucessful implementation of EUC are arranged by the result of empirical survey. In the final model, technical support, computer experience, user control, and top management support were included as exogenous factors. Thses contructs were found to influence directly or indirectly three endogenous factors, user satisfaction, computer use, and performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.998-1019
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• 2024

Mobile cloud computing is a very attractive service paradigm that outsources users' data computing and storage from mobile devices to cloud data centers. To protect data privacy, users often encrypt their data to ensure data sharing securely before data outsourcing. However, the bilinear and power operations involved in the encryption and decryption computation make it impossible for mobile devices with weak computational power and network transmission capability to correctly obtain decryption results. To this end, this paper proposes an outsourcing decryption algorithm of verifiable transformed ciphertext. First, the algorithm uses the key blinding technique to divide the user's private key into two parts, i.e., the authorization key and the decryption secret key. Then, the cloud data center performs the outsourcing decryption operation of the encrypted data to achieve partial decryption of the encrypted data after obtaining the authorization key and the user's outsourced decryption request. The verifiable random function is used to prevent the semi-trusted cloud data center from not performing the outsourcing decryption operation as required so that the verifiability of the outsourcing decryption is satisfied. Finally, the algorithm uses the authorization period to control the final decryption of the authorized user. Theoretical and experimental analyses show that the proposed algorithm reduces the computational overhead of ciphertext decryption while ensuring the verifiability of outsourcing decryption.