• Journal of Digital Convergence
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• v.10 no.1
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• pp.49-57
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• 2012

The Internet succeeded because of the end-to-end principle which allowed anyone to add functionality to the network. However, as the internet is increasingly becoming the platform for smart IT applications such as VoIP, IPTV, Cloud Computing and Smart Phone, networks are now under increasing strain of traffic congestion and the absence of quality of service insurances. To date, the debate over internet rules has focused on network neutrality rather than network efficiency. This article emphasizes the well-functioning role of market mechanism for the efficient use and further development of the network. To maximize the value of the network, this article proposes a differential treatment to packets based on customer types, and a two-part tariff pricing rule to secure funding to expand and upgrade networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.427-432
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• 2023

In this study, MEC (: Multi-access Edge Computing) proposes a cloud service network configuration for various tests of autonomous vehicles to which V2X (: Vehicle to Everything) is applied in Wave, LTE, and 5G networks and MEC App (: Application) applied V2X service function test verification of two domains (operator (KT, SKT, LG U+), network type (Wave, LTE (including 3G), 5G)) in a specific region. In 4G networks of domestic operators (SKT, KT, LG U+ and Wave), MEC summarized the improvement effects through V2X function blocks and traffic offloading for the purpose of bringing independent network functions. And with a high level of QoS value in the V2X VNF of the 5G network, the traffic steering function scenario was demonstrated on the destination-specific traffic path.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.145-158
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• 2011

Recently, the rapidly increased use of smartphone devices provides both opportunity and problems at the same time to the telecom companies in domestic and world wide. It provides a great opportunity to overcome the market stagnation of the existing voice- centric communications, and also problems of building a highly efficient new wireless access networks to accommodate huge volume of data traffic among smart devices. Facing with the age of keen competition, telecom companies have two kinds of strategies for network transition; a strategy of building uniform LTE -based 4G networks as fast as possible, and a hybrid network enhancement strategy compounding 3G and 4G networks using HSP A+, WiFi, cloud computing, etc. It is difficult to predict which strategy will have more successful outcome in the near future. This paper studies the trends and issues of network enhancement policies and transition strategies to the 4G network era, and tries to draw appropriate and efficient ways for network enhancement and operation.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.552-566
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• 2011

The Internet is a very successful modern technology and is considered to be one of the most important means of communication. Despite that success, fundamental architectural and business limitations exist in the Internet's design. Among these limitations, we focus on a specific issue, the lack of manageability, in this paper. Although it is generally understood that management is a significant and important part of network and service design, it has not been considered as an integral part in their design phase. We address this problem with our future Internet management architecture called highly manageable network and service architecture for new generation (HiMang), which is a novel architecture that aims at integrating management capabilities into network and service design. HiMang is highly manageable in the sense that it is autonomous, scalable, robust, and evolutionary while reducing the complexity of network management. Unlike any other management framework, HiMang provides management support for the revolutionary networks of the future while maintaining backward compatibility for existing networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.281-298
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• 2020

Fog computing has become a popular concept in the application of internet of things (IoT). With the superiority in better service providing, the edge cloud has become an attractive solution to IoT networks. The data outsourcing scheme of IoT devices demands privacy protection as well as computation verification since the lightweight devices not only outsource their data but also their computation. Existing solutions mainly deal with the operations over encrypted data, but cannot support the computation verification in the same time. In this paper, we propose a data outsourcing scheme based on an encrypted database system with linear computation as well as efficient query ability, and enhance the interlayer program in the original system with homomorphic message authenticators so that the system could perform computational verifying. The tools we use to construct our scheme have been proven secure and valid. With our scheme, the system could check if the cloud provides the correct service as the system asks. The experiment also shows that our scheme could be as effective as the original version, and the extra load in time is neglectable.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.725-734
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• 2016

For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.592-601
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• 2015

Multitenancy has gained growing importance with the development and evolution of cloud computing technology. In a multitenant environment, multiple tenants with different demands can share a variety of computing resources (e.g., CPU, memory, storage, network, and data) within a single system, while each tenant remains logically isolated. This useful multitenancy concept offers highly efficient, and cost-effective systems without wasting computing resources to enterprises requiring similar environments for data processing and management. In this paper, we propose a novel approach supporting multitenancy features for Apache Hadoop, a large scale distributed system commonly used for processing big data. We first analyze the Hadoop framework focusing on "yet another resource negotiator (YARN)", which is responsible for managing resources, application runtime, and access control in the latest version of Hadoop. We then define the problems for supporting multitenancy and formally derive the requirements to solve these problems. Based on these requirements, we design the details of multitenant Hadoop. We also present experimental results to validate the data access control and to evaluate the performance enhancement of multitenant Hadoop.

• ETRI Journal
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• v.37 no.4
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• pp.743-751
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• 2015

Cloud gaming services are heavily dependent on the efficiency of real-time video streaming technology owing to the limited bandwidths of wire or wireless networks through which consecutive frame images are delivered to gamers. Video compression algorithms typically take advantage of similarities among video frame images or in a single video frame image. This paper presents a method for computing and extracting both graphics information and an object's boundary from consecutive frame images of a game application. The method will allow video compression algorithms to determine the positions and sizes of similar image blocks, which in turn, will help achieve better video compression ratios. The proposed method can be easily implemented using function call interception, a programmable graphics pipeline, and off-screen rendering. It is implemented using the most widely used Direct3D API and applied to a well-known sample application to verify its feasibility and analyze its performance. The proposed method computes various kinds of graphics information with minimal overhead.

• Journal of IKEEE
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• v.28 no.3
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• pp.375-381
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• 2024

In this paper, we propose a lightweight object classification system using a LiDAR sensor for delivery service robots. The 3D point cloud data is encoded into a 2D pseudo image using a Pillar Feature Network (PFN), and then passed through a lightweight classification network designed based on Depthwise Separable Convolutional Neural Networks (DS-CNN). The implementation results show that the designed classification network has 9.08K parameters and 3.49M Multiply-Accumulate (MAC) operations, while supporting a classification accuracy of 94.94%.