• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.391-397
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• 2015

Nonvolatile memories in memory hierarchy have been investigated to reduce its energy consumption because nonvolatile memories consume zero leakage power in memory cells. One of the difficulties is, however, that the endurance of most nonvolatile memory technologies is much shorter than the conventional SRAM and DRAM technology. This has limited its usage to only the low levels of a memory hierarchy, e.g., disks, that is far from the CPU. In this paper, we study the use of a new type of nonvolatile memories - the Phase Change Memory (PCM) with a DRAM buffer system as the main memory. Our design reduced the total energy of a DRAM main memory of the same capacity by 80%. These results indicate that it is feasible to use PCM technology in place of DRAM in the main memory for better energy efficiency.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.91-101
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• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.457-464
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• 2010

The haze removal algorithm using dark channel prior, which was proposed by He et al., is an efficient algorithm and presents impressive results. But its high memory and computational requirements limit its applications. In this paper, we propose a method to improve the memory usage and calculation speed. We notice that the matting process accounts for most calculation time, so we replace the matting process with a fast bilateral filtering scheme. Using the bilateral filter, we can reduce the memory usage, but its computational complexity is still high. To reduce the computational complexity as well, we adapt a hierarchical structure for the bilateral filtering. Experimental results show that the proposed algorithm can remove haze in a picture effectively, while requiring much less computations than the He et al.'s method.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.4
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• pp.201-207
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• 2012

Currently, NAND Flash memory has been widely used in consumer storage devices due to its non-volatility, stability, economical feasibility, low power usage, durability, and high density. However, a high capacity of NAND flash memory causes the high power consumption and the low performance. In the convention memory research, a hierarchical filter mechanism can archive an effective performance improvement in terms of the power consumption. In order to attain the best filter structure for NAND flash memory, we selected a direct-mapped filter, a victim filter, a fully associative filter and a 4-way set associative filter for comparison in the performance analysis. According to the results of the simulation, the fully associative filter buffer with a 128byte fetching size can obtain the bet performance compared to another filter structures, and it can reduce the energy*delay product(EDP) by about 93% compared to the conventional NAND Flash memory.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.151-156
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• 2015

Recently, an embedded flash memory has been widely used for the Internet of Things(IoT). Due to its nonvolatility, economical feasibility, stability, low power usage, and fast speed. With respect to power consumption, the embedded memory system must consider the most significant design factor. The objective of this research is to design high performance and low power NAND flash memory architecture including a dual buffer as a replacement for NOR flash. Simulation shows that the proposed NAND flash system can achieve better performance than a conventional NOR flash memory. Furthermore, the average memory access time of the proposed system is better that of other buffer systems with three times more space. The use of a small buffer results in a significant reduction in power consumption.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.77-80
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• 2013

SURF algorithm is an algorithm to extract feature points and to generate descriptors from input images. It is robust to change of environment such as scale, rotation, illumination and view points. Because of these features, it is used for many image processing applications such as object recognition, constructing panorama pictures and 3D image restoration. But there is disadvantage for real time operation because many recognition algorithms such as SURF algorithm requires a lot of calculations. In this paper, we propose a design of feature extractor and descriptor generator based on SURF for high memory efficiency. The proposed design reduced a memory access and memory usage to operate in real time.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.413-420
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• 2006

Most vendors of digital signal processors (DSPs) support a Harvard architecture, which has two or more memory buses, one for program and one or more for data and allow the processor to access multiple words of data from memory in a single instruction cycle. We already addressed how to efficiently assign data to multi-memory banks in our previous work. This paper reports on our recent attempt to optimize run-time memory. The run-time environment for dual data memory banks (DBMBs) requires two run-time stacks to control activation records located in two memory banks corresponding to calling procedures. However, activation records of two memory banks for a procedure are able to have different size. As a consequence, dual run-time stacks can be unbalanced whenever a procedure is called. This unbalance between two memory banks causes that usage of one memory bank can exceed the extent of on-chip memory area although there is free area in the other memory bank. We attempt balancing dual run-time slacks to enhance efficiently utilization of on-chip memory in this paper. The experimental results have revealed that although our algorithm is relatively quite simple, it still can utilize run-time memories efficiently; thus enabling our compiler to run extremely fast, yet minimizing the usage of un-time memory in the target code.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.223-228
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• 2005

Several researchers have pointed out that the energy consumption in memory takes a dominant fraction on the energy budget of a whole embedded system. This applies to the embedded Java virtual machine tn, and to develop a more energy-efficient JVM it is absolutely necessary to optimize the energy usage in Jana memory. In this paper we have analyzed the logical memory access pattern in JVM as it executes numerous number of bytecode instructions while running a Java program. The access pattern gives us an insight how to design and select a suitable memory technology for Java memory. We present the memory access pattern for the three logical data spaces of JVM: heap, operand stack, and local variable array. The result saws that operand stack is accessed most frequently and uniformly, whereas heap used least frequently and non-uniformly among the three. Both heap and local variable array are accessed mostly in read-only fashion, but no remarkable difference is found between read and write operations for operand stack usage.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.1-9
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• 2016

Algorithms implementing distributed shared memory (DSM) were developed for ensuring consistency. The performance of DSM algorithms is dependent on system and usage parameters. However, ensuring these algorithms to tolerate faults is a problem that needs to be researched. In this study, we proposed fault-tolerant scheme for DSM system and analyzed reliability and fault-tolerant overhead. Using our analysis, we can choose a proper algorithm for DSM on error prone environment.