• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.42-54
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• 2008

In recent years, embedded systems have been expanding their application domains from traditional embedded systems such as military weapons, robots, satellites and digital convergence systems such as celluar phones, PMP(Portable Multimedia Player), PDAs(Personal Digital Assistants) to Next Generation Personal Computers(NGPCs) such as eating PCs, wearable computers. The NGPCs are network-based, human-centric digital information devices diverged from the traditional PCs used mainly for document writing, internet searching and database management. Wearable computers with battery capacity and memory size limitations have to use real-time operating systems with small footprints and low power management techniques to provide user's QoS in spite of hardware constraints. In this paper, we have designed and implemented a low-power RTOS (called eRTOS) for wearable computers. The implemented eRTOS has 18KB footprints and the dynamic power management and the device power management schemes are adapted in it. Experimental results with wearable computer applications show that the low power techniques could save energy up to 47 %.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.35-44
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• 2003

The effectiveness of buffer cache replacement algorithms is critical to the performance of I/O systems. In this paper, we propose the degree of inter-reference gap (DIG) based block replacement scheme that retains merits of the least recently used (LRU) such as simple implementation and good cache hit ratio (CHR) for general patterns of references, and improves CHR further. In the proposed scheme, cache blocks with low DIGs are distinguished from blocks with high DIGs and the replacement block is selected among high DIGs blocks as done in the low inter-reference recency set (LIRS) scheme. Thus, by having the effect of the partitioning the cache memory dynamically based on DIGs, CHR is improved. Trace-driven simulation is employed to verified the superiority of the DIG based scheme and shows that the performance improves up to about 175% compared to the LRU scheme and 3% compared to the LIRS scheme for the same traces.

• Geomechanics and Engineering
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• v.24 no.6
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• pp.519-529
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• 2021

In coal mining activities, the abutment stress of the coal has to undergo cyclic loading and unloading, affecting the strength and seepage characteristics of coal; additionally, it can cause dynamic disasters, posing a major challenge for the safety of coal mine production. To improve the understanding of the dynamic disaster mechanism of gas outburst and rock burst coupling, triaxial devices are applied to axial pressure cyclic loading-unloading tests under different axial stress peaks and different pore pressures. The existing empirical formula is use to perform a non-linear regression fitting on the relationship between stress and permeability, and the damage rate of permeability is introduced to analyze the change in permeability. The results show that the permeability curve obtained had "memory", and the peak stress was lower than the conventional loading path. The permeability curve and the volume strain curve show a clear symmetrical relationship, being the former in the form of a negative power function. Owing to the influence of irreversible deformation, the permeability difference and the damage of permeability mainly occur in the initial stage of loading-unloading, and both decrease as the number of cycles of loading-unloading increase. At the end of the first cycle and the second cycle, the permeability decreased in the range of 5.777 - 8.421 % and 4.311-8.713 %, respectively. The permeability decreases with an increase in the axial stress peak, and the damage rate shows the opposite trend. Under the same conditions, the permeability of methane is always lower than that of helium, and it shows a V-shape change trend with increasing methane pressures, and the permeability of the specimen was 3 MPa > 1 MPa > 2 MPa.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.193-204
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• 2006

In most existing distributed Web systems, incoming requests are distributed to servers via Domain Name System (DNS). Although such systems are simple to implement, the address caching mechanism easily results in load unbalancing among servers. Moreover, modification of the DNS is necessary to load considering the server's state. In this paper, we propose a new dynamic load balancing method using dynamic DNS update and round-robin mechanism. The proposed method performs effective load balancing without modification of the DNS. In this method, a server can dynamically be added to or removed from the DNS list according to the server's load. By removing the overloaded server from the DNS list, the response time becomes faster. For dynamic scheduling, we propose a scheduling algorithm that considers the CPU, memory, and network usage. We can select a scheduling policy based on resources usage. The proposed system can easily be managed by a GUI-based management tool. Experiments show that modules implemented in this paper have low impact on the proposed system. Furthermore, experiments show that both the response time and the file transfer rate of the proposed system are faster than those of a pure Round-Robin DNS.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.169-177
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• 2011

As demand for efficiency in handling dynamic XML data grows, new dynamic XML labeling schemes have been researched. The key idea of the dynamic XML labeling scheme is to find ancestor-descendent-sibling relationships and to minimize memory space to store total label, response time and range of relabeling incurred by update operations. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements by a simple divisibility test of labels. When a new element is inserted into the XML data using this scheme, it does not change the label values of existing nodes. However, since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel technique to effectively reduce the problem of label overflow. The suggested idea is based on tree decomposition. When label overflow occurs, the full tree is divided into several sub-trees, and nodes in each sub-tree are separately labeled. Through experiments, we show the effectiveness of our scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.896-915
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• 2023

The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.153-161
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• 2021

Real-time access is required to handle continuous and unstructured data and should be flexible in management under dynamic state. Platform can be built to allow data collection, storage, and processing from local-server or multi-server. Although the former centralize method is easy to control, it creates an overload problem because it proceeds all the processing in one unit, and the latter distributed method performs parallel processing, so it is fast to respond and can easily scale system capacity, but the design is complex. This paper provides data collection and processing on one platform to derive significant insights from various data held by an enterprise or agency in the latter manner, which is intuitively available on dashboards and utilizes Spark to improve distributed processing performance. All service utilize dockers to distribute and management. The data used in this study was 100% collected from Kafka, showing that when the file size is 4.4 gigabytes, the data processing speed in spark cluster mode is 2 minute 15 seconds, about 3 minutes 19 seconds faster than the local mode.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.1-14
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• 2008

This paper is on query processing in the mobile devices where memory capacity is limited. In case that a query against a large volume of XML data is processed in such a mobile device, techniques of fragmenting the XML data into chunks and of streaming and processing them are required. Such techniques make it possible to process queries without materializing the XML data in its entirety. The previous schemes such as XFrag[4], XFPro[5], XFLab[6] are not scalable with respect to the increase of the size of the XML data because they lack proper memory management capability. After some information on XML fragments necessary for query processing is stored, it is not deleted even after it becomes of no use. As such, when the XML fragments are dynamically generated and infinitely streamed, there could be no guarantee of normal completion of query processing. In this paper, we address scalability of query processing over dynamic XML fragment stream, proposing techniques of deleting information on XML fragments accumulated during query processing in order to extend the previous schemes. The performance experiments through implementation showed that our extended schemes considerably outperformed the previous ones in memory efficiency and scalability with respect to the size of the XML data.