• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1033-1048
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• 2018

This paper proposes an integrated lighting enabler system (ILES) based on standard machine-to-machine (M2M) platforms. This system provides common services of end-to-and M2M communication for smart lighting system. It is divided into two sub-systems, namely end-device system and server system. On the server side, the M2M platform OpenMTC is used to receive data from the sensors and send response for activating actuators. At the end-device system, a programmable smart lighting device is connected to the actuators and sensors for communicating their data to the server. Some experiments have been done to prove the system concept. The experiment results show that the proposed integrated lighting enabler system is effective to reduce the power consumption by 25.22% (in average). The proving of significance effect in reducing power consumption is measured by the Wilcoxon method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.75-85
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• 2012

In general, cellular network is deployed as distributed network architecture, so network equipment is spread out all over the coverage area. This case causes high cost. Recently, centralized network architecture has been deployable at cellular network since base station was able to be virtualized due to advance in computing power. The centralized network architecture in cellular network adjusts its cell radius dynamically and minimizes power consumption of the network. This paper introduces a new centralized deployment way of cellular network using SDR and cloud computing technology. Then, advantage and feasibility of the new network will be reviewed by implementing this novel network.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.4
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• pp.318-328
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• 2011

Stage-level reconfigurable chip multiprocessor (CMP) aims to achieve highly reliable and fault tolerant computing by using interwoven pipeline stages and on-chip interconnect for communicating with each other. The existing crossbar-switch based stage-level reconfigurable CMPs offer high reliability at the cost of significant area/power overheads. These overheads make realizing large CMPs prohibitive due to the area and power consumed by heavy interconnection networks. On other hand, area/power-efficient architectures offer less reliability and inefficient stage-level resource utilization. In this paper, I propose a hierarchical multiplexing interconnection structure in lieu of crossbar interconnect to design area/power-efficient stage-level reconfigurable CMP. The proposed approach is able to keep the reliability offered by the crossbar-switch while reducing the area and power overheads. Experimental results show that the proposed approach reduces area by up to 21% and power by up to 32% when compared with the crossbar-switch based interconnection network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.91-94
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• 2004

Ubiquitous sensor network is to manage and collect information autonomously by communicating user around device. Security requirements in Ubiquitous based on sensor network are as follows: a location of sensor, a restriction of performance by low electric power, communication by broadcasting, etc. We propose new mutual authentication protocol using a low power of sensor node. This protocol solved a low power problem by reducing calculation overload of sensor node using two steps, RM(Register Manager) and AM(Authentication Manager). Many operations performing the sensor node itself have a big overload in low power node. Our protocol reduces the operation number from sensor node. Also it is mutual authentication protocol in Ubiquitous network, which satisfies mutual authentication, session key establishment, user and device authentication, MITM attack, confidentiality, integrity, and is safe the security enemy with solving low electric power problem.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.280-286
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• 2012

The last decades have witnessed an increasingly indispensible role of high performance computing (HPC) in science, business and financial sectors, as well as military and national security areas. To introduce key aspects of HPC to a broader community, an HPC session was organized for the first time ever for the United States and Korea Conference (UKC) during 2012. This paper summarizes four invited talks that each covers scientific HPC applications, large-scale parallel file systems, administration/maintenance of supercomputers, and green technology towards building power efficient supercomputers of the next generation.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.73-81
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• 2020

The internet of things (IoT) using cloud computing is applied to nuclear industry in which the nuclear power plant (NPP) accident is analyzed for the safety assessment. The Fukushima NPP accident is modeled for the accident simulations where the earthquake induced plant failure accident is used for analyzing the cloud computing technology. The fast and reasonable treatment in the natural disaster was needed in the case of the Fukushima. The real time safety assessment (RTSA) and the Monte-Carlo real time assessment (MCRTA) are constructed. This cloud computing could give the practicable method to prepare for the future similar accident.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.175-186
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• 2009

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.6
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• pp.678-684
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• 2015

Near-threshold computing (NTC) is now regarded as a promising candidate for innovative power reduction, which cannot be achieved with conventional super-threshold computing (STC). However, performance degradation and vulnerability to process variation in the NTC regime are the primary concerns. In this paper, we propose a voltage- and frequency-tuning methodology for mitigating the process-variation-induced problems in NTC-based manycore architectures. To implement the proposed methodology, we build up multiple-voltage multiple-frequency (MVMF) islands and apply a voltage-frequency tuning algorithm based on the critical-path monitoring technique to reduce the effects of process variation and maximize energy efficiency in the post-silicon stage. Experimental results show that the proposed methodology reduces overall power consumption by 8.2-20.0%, compared to existing methods in variation-sensitive NTC environments.