Vol. 42, No. 8,
Aug. 2015
Digital Library
A Buffer Cache Replacement Algorithm for Considering both Hybrid Main Memory and Storage
PRAM is being considered as a potential successor to DRAM because of its characteristics such as byte-addressability, non-volatility, and high density. To gain its benefits, buffer cache replacement algorithm based on PRAM has been actively studied. However, most of the previous studies on buffer cache replacement algorithm limitedly exploit the byte-level performance of PRAM by focusing its limited lifetime and slower access latency compared to DRAM. In this paper, we propose a novel buffer cache replacement algorithm that fully considers the byte-level performance of PRAM and the performance of secondary storage. To take advantage of small size write on PRAM, proposed scheme keeps pages, which are frequently accessed with a small size write, on PRAM and allows the selective page migration from DRAM to PRAM. As a result, our scheme significantly reduces the number of PRAM writes. Our experimental results indicate for real workloads that our scheme reduces the number of PRAM writes by up to 92% and improves its performance by up to 62% compared to CLOCK.
SSD Caching for Improving Performance of Virtualized IoT Gateway
It is important to improve the performance of storage in the home cloud environment within the virtualized IoT gateway since the performance of applications deeply depends on storage. Though SSD caching is applied in order to improve the storage, it is only used for read-cache due to the limitations of SSD such as poor write performance and small write endurance. However, it isimportant to improve performance of the write operation in the home cloud server, in order to improve the end-user experience. This paper propose a novel SSD caching which considers write-data as well as read-data. We validate the enhancement in the performance of random-write by transforming it to the sequential patterns.
A Packet Classification Algorithm Using Bloom Filter Pre-Searching on Area-based Quad-Trie
As a representative area-decomposed algorithm, an area-based quad-trie (AQT) has an issue of search performance. The search procedure must continue to follow the path to its end, due to the possibility of the higher priority-matching rule, even though a matching rule is encountered in a node. A leaf-pushing AQT improves the search performance of the AQT by making a single rule node exist in each search path. This paper proposes a new algorithm to further improve the search performance of the leaf-pushing AQT. The proposed algorithm implements a leaf-pushing AQT using a hash table and an on-chip Bloom filter. In the proposed algorithm, by sequentially querying the Bloom filter, the level of the rule node in the leaf-pushing AQT is identified first. After this procedure, the rule database, which is usually stored in an off-chip memory, is accessed. Simulation results show that packet classification can be performed through a single hash table access using a reasonable sized Bloom filter. The proposed algorithm is compared with existing algorithms in terms of the memory requirement and the search performance.
Forgetting based File Cache Management Scheme for Non-Volatile Memory
Non-volatile memory (NVM) supports both byte addressability and non-volatility. These characteristics make it feasible for NVM to be employed at any layer of the memory hierarchy such as cache, memory and disk. An interesting characteristic of NVM is that, even though it supports non-volatility, its retention capability is limited. Furthermore NVM has tradeoff between its retention capability and write latency. In this paper, we propose a novel NVM-based file cache management scheme that makes use of the limited retention capability to improve the cache performance. Experimental results with real-workloads show that our scheme can reduce access latency by up to 31% (24.4% average) compared with the conventional LRU based cache management scheme.
BLE-OTP Authorization Mechanism for iBeacon Network Security
Hyunhee Jung, Dongryeol Shin, Kwangsu Cho, Choonsung Nam
Machine to Machine (M2M) technology has gained attention due to the fast diffusion of Internet of Things (IoT) technologies and smart devices. Most wireless network experts believe that Bluetooth Low Energy (BLE) Communications technology in an iBeacon network has amazing advantages in terms of providing communication services at a low cost in smartphone applications. Specifically, BLE does not require any pairing process during its communication phases, so it is possible to send a message to any node without incurring additional transmissions costs if they are within the BLE communication range. However, BLE does not require any security verification during communication, so it has weak security. Therefore, a security authorization process would be necessary to obtain customer confidence. To provide security functions for iBeacon, we think that the iBeacon Message Encryption process and a Decryption (Authorization) process should be designed and implemented. We therefore propose the BLE message Authorization Mechanism based on a One Time Password Algorithm (BLE-OTP). The effectiveness of our mechanism is evaluated by conducting a performance test on an attendance system based on BLE-OTP.
Real-Time Panorama Video Generation System using Multiple Networked Cameras
Panoramic image creation has been extensively studied. Existing methods use customized hardware, or apply post-processing methods to seamlessly stitch images. These result in an increase in either cost or complexity. In addition, images can only be stitched under certain conditions such as existence of characteristic points of the images. This paper proposes a low cost and easy-to-use system that produces realtime panoramic video. We use an off-the-shelf embedded platform to capture multiple images, and these are then transmitted to a server in a compressed format to be merged into a single panoramic video. Finally, we analyze the performance of the implemented system by measuring time to successfully create the panoramic image.
Scalable RDFS Reasoning Using the Graph Structure of In-Memory based Parallel Computing
MyungJoong Jeon, ChiSeoung So, Batselem Jagvaral, KangPil Kim, Jin Kim, JinYoung Hong, YoungTack Park
In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.
Design of Extended Real-time Data Pipeline System Architecture
Hoseung Shin, Sungwon Kang, Jihyun Lee
Big data systems are widely used to collect large-scale log data, so it is very important for these systems to operate with a high level of performance. However, the current Hadoop-based big data system architecture has a problem in that its performance is low as a result of redundant processing. This paper solves this problem by improving the design of the Hadoop system architecture. The proposed architecture uses the batch-based data collection of the existing architecture in combination with a single processing method. A high level of performance can be achieved by analyzing the collected data directly in memory to avoid redundant processing. The proposed architecture guarantees system expandability, which is an advantage of using the Hadoop architecture. This paper confirms that the proposed architecture is approximately 30% to 35% faster in analyzing and processing data than existing architectures and that it is also extendable.
Testing Android Applications Considering Various Contexts Inferred from Permissions
Kwangsik Song, Ah-Rim Han, Sehun Jeong, Sungdeok Cha
The context-awareness of mobile applications yields several issues for testing, since mobile applications should be able to be tested in any environment and under any contextual input. In previous studies of testing for Android applications as an event-driven system, many researchers have focused on using generated test cases considering only Graphical User Interface (GUI) events. However, it is difficult to find failures that could be detected when considering the changes in the context in which applications run. It is even more important to consider various contexts since the mobile applications adapt and use the new features and sensors of mobile devices. In this paper, we provide a method of systematically generating various executing contexts from permissions. By referring to the lists of permissions, the resources used by the applications for running Android applications can be easily inferred. To evaluate the efficiency of our testing method, we applied the method on two open source projects and showed that it contributes to improve the statement code coverage.
Sensor Selection Strategies for Activity Recognition in a Smart Environment
The recent emergence of smart phones, wearable devices, and even the IoT concept made it possible for various objects to interact one another anytime and anywhere. Among many of such smart services, a smart home service typically requires a large number of sensors to recognize the residents’ activities. For this reason, the ideas on activity recognition using the data obtained from those sensors are actively discussed and studied these days. Furthermore, plenty of sensors are installed in order to recognize activities and analyze their patterns via data mining techniques. However, if many of these sensors should be installed for IoT smart home service, it raises the issue of cost and energy consumption. In this paper, we proposed a new method for reducing the number of sensors for activity recognition in a smart environment, which utilizes the principal component analysis and clustering techniques, and also show the effect of improvement in terms of the activity recognition by the proposed method.
A Video Quality Control Scheme Based on Content Characteristics for Improving QoE in DASH Environments
Recently, the web-based adaptive streaming service, DASH (Dynamic Adaptive Streaming over HTTP), is receiving more attention. However, existing network-based and buffer-based video quality control schemes in DASH environments make oscillation of segment throughput, causing degradation of the quality of experience (QoE) with frequent quality changes and playback interruptions because these schemes do not consider the content characteristics. In this paper, we propose a C-DASH (Content Characteristics based Dynamic Adaptive Streaming over HTTP) scheme in order to improve the QoE in DASH environments. The C-DASH scheme performs seamless and smooth quality control based on the segment throughput, buffer status, and segment size of the content. Based on simulation results, it is confirmed that the C-DASH scheme can improve the QoE, when compared with the existing quality control schemes.
Improvement of Runtime Intrusion Prevention Evaluator (RIPE)
Hyungyu Lee, Damho Lee, Taehwan Kim, Donghwang Cho, Sanghoon Lee, Hoonkyu Kim, Changwoo Pyo
Runtime Intrusion Prevention Evaluator (RIPE), published in 2011, is a benchmark suite for evaluating mitigation techniques against 850 attack patterns using only buffer overflow. Since RIPE is built as a single process, defense and attack routines cannot help sharing process states and address space layouts when RIPE is tested. As a result, attack routines can access the memory space for defense routines without restriction. We separate RIPE into two independent processes of defense and attacks so that mitigations based on confidentiality such as address space layout randomization are properly evaluated. In addition, we add an execution mode to test robustness against brute force attacks. Finally, we extend RIPE by adding 38 attack forms to perform format string attacks and virtual table (vtable) hijacking attacks. The revised RIPE contributes to the diversification of attack patterns and precise evaluation of the effectiveness of mitigations.
A Priority Based Multipath Routing Mechanism in the Tactical Backbone Network
Yongsin Kim, Sang-heon Shin, Younghan Kim
The tactical network is system based on wireless networking technologies that ties together surveillance reconnaissance systems, precision strike systems and command and control systems. Several alternative paths exist in the network because it is connected as a grid to improve its survivability. In addition, the network topology changes frequently as forces and combatants change their network access points while conducting operations. However, most Internet routing standards have been designed for use in stable backbone networks. Therefore, tactical networks may exhibit a deterioration in performance when these standards are implemented. In this paper, we propose Priority based Multi-Path routing with Local Optimization(PMPLO) for a tactical backbone network. The PMPLO separately manages the global and local metrics. The global metric propagates to other routers through the use of a routing protocol, and it is used for a multi-path configuration that is guaranteed to be loop free. The local metric reflects the link utilization that is used to find an alternate path when congestion occurs, and it is managed internally only within each router. It also produces traffic that has a high priority privilege when choosing the optimal path. Finally, we conducted a simulation to verify that the PMPLO can effectively distribute the user traffic among available routers.
Construction of Korean Knowledge Base Based on Machine Learning from Wikipedia
Seok-won Jeong, Maengsik Choi, Harksoo Kim
The performance of many natural language processing applications depends on the knowledge base as a major resource. WordNet, YAGO, Cyc, and BabelNet have been extensively used as knowledge bases in English. In this paper, we propose a method to construct a YAGO-style knowledge base automatically for Korean (hereafter, K-YAGO) from Wikipedia and YAGO. The proposed system constructs an initial K-YAGO simply by matching YAGO to info-boxes in Wikipedia. Then, the initial K-YAGO is expanded through the use of a machine learning technique. Experiments with the initial K-YAGO shows that the proposed system has a precision of 0.9642. In the experiments with the expanded part of K-YAGO, an accuracy of 0.9468 was achieved with an average macro F1-measure of 0.7596.
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