• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.24-30
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• 2014

This study investigates computing offloading effect of cloud in real-time video personal broadcast service, whose server is mobile device. Mobile device does not have enough computing resource for encoding video. The computing burden is offloaded to cloud, which has abundant resources in terms of computing, power, and storage compared to mobile device. By reducing computing burden, computation energy can be saved while transmission data amount increases because of decreasing compression efficiency. This study shows that the optimal operation point can be found adaptively to time-varying LTE communication condition result of tradeoff analysis between offloaded computation burden and increase in amount of transmitted data.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.513-522
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• 2016

We propose a mechanism for monitoring load in quality of service (QoS)-enabled wireless networks and show how it can be used for network management as well as for dynamic pricing. Mobile network traffic, especially video, has grown exponentially over the last few years and it is anticipated that this trend will continue into the future. Driving factors include the availability of new affordable, smart devices, such as smart-phones and tablets, together with the expectation of high quality user experience for video as one would obtain at home. Although new technologies such as long term evolution (LTE) are expected to help satisfy this demand, the fact is that several other mechanisms will be needed to manage overload and congestion in the network. Therefore, the efficient management of the expected huge data traffic demands is critical if operators are to maintain acceptable service quality while making a profit. In the current work, we address this issue by first investigating how the network load can be accurately monitored and then we show how this load metric can then be used to provide creative pricing plans. In addition, we describe its applications to features like traffic offloading and user satisfaction tracking.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1102-1108
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• 2020

Mobile edge computing is a technology that can prepare for a new era of cloud computing and compensate for shortcomings by processing data near the edge of the network where data is generated rather than centralized data processing. It is possible to realize a low-latency/high-speed computing service by locating computing power to the edge and analyzing data, rather than in a data center far from computing and processing data. In this article, we develop a virtual mobile edge computing testbed environment where the cloud and edge nodes divide computing tasks from mobile terminals using the EdgeCloudSim simulator. Performance of offloading techniques for distribution of computing tasks from mobile terminals between the central cloud and mobile edge computing nodes is evaluated and analyzed under the virtual mobile edge computing environment. By providing a virtual mobile edge computing environment and offloading capabilities, we intend to provide prior knowledge to industry engineers for building mobile edge computing nodes that collaborate with the cloud.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4849-4864
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• 2019

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.689-692
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• 2021

This paper is a prior study to improve the efficiency of offloading based on mobile agents to optimize allocation of computing resources and reduce latency that support user proximity of application services in a Fog/Edge Computing (FEC) environment. We propose an algorithm that effectively reduces the execution time and the amount of memory required when extracting optimal moving patterns from the vast set of spatio-temporal movement history data of moving objects. The proposed algorithm can be useful for the distribution and deployment of computing resources for computation offloading in future FEC environments through frequency-based optimal path extraction.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.103-111
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• 2016

Offloading tasks from mobile devices to available cloud servers were improved since the introduction of the cloudlet. With the implementation of dynamic offloading algorithms, mobile devices can choose the appropriate server for the set of tasks. However, current task distribution approaches do not consider the number of VM, which can be a critical factor in the decision making. This paper proposes a dynamic task distribution on clustered data centers. A proportional VM migration approach is also proposed, where it migrates virtual machines to the cloud servers proportionally according to their allocated CPU, in order to prevent overloading of resources in servers. Moreover, we included the resource capacity of each data center in terms of the maximum CPU in order to improve the migration approach in cloud servers. Simulation results show that the proposed mechanism for task distribution greatly improves the overall performance of the system.

• 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.

• ETRI Journal
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• v.40 no.1
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• pp.61-71
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• 2018

In this study, we developed hybrid control algorithms in smart base stations (SBSs) along with devised communication, caching, and computing techniques. In the proposed scheme, SBSs are equipped with computing power and data storage to collectively offload the computation from mobile user equipment and to cache the data from clouds. To combine in a refined manner the communication, caching, and computing algorithms, game theory is adopted to characterize competitive and cooperative interactions. The main contribution of our proposed scheme is to illuminate the ultimate synergy behind a fully integrated approach, while providing excellent adaptability and flexibility to satisfy the different performance requirements. Simulation results demonstrate that the proposed approach can outperform existing schemes by approximately 5% to 15% in terms of bandwidth utilization, access delay, and system throughput.

• Annual Conference of KIPS
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• 2013.05a
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• pp.88-90
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• 2013

With the continuous growth of a number of high-quality multimedia services for handheld devices, the lack of power resources becomes an increasingly critical issue. One of the ways to overcome existing problem is to make multimedia data processing more efficient. In order to do that this paper introduces a video streaming application for Android platform which, while being used along with offloading technique, may provide an efficient progressive download service for user devices along with relief of media servers.