Vol. 44, No. 10,
Oct. 2017
Digital Library
QEMU/KVM Based In-Memory Block Cache Module for Virtualization Environment
TaeHoon Kim, KwangHyeok Song, JaeChun No, SungSoon Park
http://doi.org/10.5626/JOK.2017.44.10.1005
Recently, virtualization has become an essential component of cloud computing due to its various strengths, including maximizing server resource utilization, easy-to-maintain software, and enhanced data protection. However, since virtualization allows sharing physical resources among the VMs, the system performance can be deteriorated due to device contentions. In this paper, we first investigate the I/O overhead based on the number of VMs on the same server platform and analyze the block I/O process of the KVM hypervisor. We also propose an in-memory block cache mechanism, called QBic, to overcome I/O virtualization latency. QBic is capable of monitoring the block I/O process of the hypervisor and stores the data with a high access frequency in the cache. As a result, QBic provides a fast response for VMs and reduces the I/O contention to physical devices. Finally, we present a performance measurement of QBic to verify its effectiveness.
Efficient Similarity Analysis Methods for Same Open Source Functions in Different Versions
http://doi.org/10.5626/JOK.2017.44.10.1019
Binary similarity analysis is used in vulnerability analysis, malicious code analysis, and plagiarism detection. Proving that a function is equal to a well-known safe functions of different versions through similarity analysis can help to improve the efficiency of the binary code analysis of malicious behavior as well as the efficiency of vulnerability analysis. However, few studies have been carried out on similarity analysis of the same function of different versions. In this paper, we analyze the similarity of function units through various methods based on extractable function information from binary code, and find a way to analyze efficiently with less time. In particular, we perform a comparative analysis of the different versions of the OpenSSL library to determine the way in which similar functions are detected even when the versions differ.
Health State Clustering and Prediction Based on Bayesian HMM
http://doi.org/10.5626/JOK.2017.44.10.1026
In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.
Controlling a Traversal Strategy of Abstract Reachability Graph-based Software Model Checking
http://doi.org/10.5626/JOK.2017.44.10.1034
Although traversal strategies are important for the performance of model checking, many studies have ignored the impact of traversal strategies in model checking with a block-encoded abstract reachability graph. Studies have considered traversal strategies only for an abstract reachability graph without block-encoding. Block encoding plays a crucial role in the model checking performance. This paper therefore describes Dual-traversal strategy, a simple and novel technique to control traversal strategies in a block-encoded abstract reachability graph. This method uses two traversal strategies for a model checking, one for effective block-encoding, and the other for traversal in an encoded abstract reachability graph. Dual-traversal strategy is very simple and can be implemented without overhead compared to the existing single-traversal strategy. We implemented the Dual-traversal strategy in an open source model checking tool and compare the performances of different traversal strategies. The results show that the model checking performance varies from the traversal strategies for the encoded abstract reachability graph.
Linkage Expansion in Linked Open Data Cloud using Link Policy
http://doi.org/10.5626/JOK.2017.44.10.1045
This paper suggests a method to expand linkages in a Linked Open Data(LOD) cloud that is a practical consequence of a semantic web. LOD cloud, contrary to the first expectation, has not been used actively because of the lack of linkages. Current method for establishing links by applying 〈owl:sameAs〉 to explicit links and attaching the links to LODs have restrictions on reflecting target LODs’ changes in a timely manner and maintaining them periodically. Instead of attaching them, this paper suggests that each LOD should prepare a link policy and publish it together with the LOD. The link policy specifies target LODs, predicate pairs, and similarity degrees to decide on the establishment of links. We have implemented a system that performs in-depth searching through LODs using their link policies. We have published APIs of the system to Github. Results of the experiment on the in-depth searching system with similarity degrees of 1.0 ～ 0.8 and depth level of 4 provides searching results that include 91%~98% of the trustworthy links and about 170% of triples expanded.
Improved Real-time Transmission Scheme using Temporal Gain in Wireless Sensor Networks
Taehun Yang, Hyunchong Cho, Sangdae Kim, Cheonyong Kim, Sang-Ha Kim
http://doi.org/10.5626/JOK.2017.44.10.1062
Real-time transmission studies in wireless sensor networks propose a mechanism that exploits a node that has a higher delivery speed than the desired delivery speed in order to satisfy real-time requirement. The desired delivery speed cannot guarantee real-time transmission in a congested area in which none of the nodes satisfy the requirement in one hop because the desired delivery speed is fixed until the packet reaches the sink. The feature of this mechanism means that the packet delivery speed increases more than the desired delivery speed as the packet approaches closer to the sink node. That is, the packet can reach the sink node earlier than the desired time. This paper proposes an improved real-time transmission by controlling the delivery speed using the temporal gain which occurs on the packet delivery process. Using the received data from a previous node, a sending node calculates the speed to select the next delivery node. The node then sends a packet to a node that has a higher delivery speed than the recalculated speed. Simulation results show that the proposed mechanism in terms of the real-time transmission success ratio is superior to the existing mechanisms.
Network Topology Discovery with Load Balancing for IoT Environment
Hyunsu Park, Jinsoo Kim, Moosung Park, Youngbae Jeon, Jiwon Yoon
http://doi.org/10.5626/JOK.2017.44.10.1071
With today"s complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor"s resource usage.
Static Analysis of Large Scale Software Repositories Using WALA and Boa
http://doi.org/10.5626/JOK.2017.44.10.1081
A program analysis of a large-scale open-source software repository has a significant meaning in that it allows us to examine the changes and improvements of the software in repositories, and this brings more reliable results based on a large amount of programs. In this paper, we introduce a new static analysis framework WALABOA, which enables a scalable static analysis of large-scale software repositories. In addition, we show new findings from applying WALABOA, together with a module comparing the analysis results from a static analysis and a dynamic analysis, in evaluation of the field-based analysis, one of JavaScript static analysis techniques used in WALA.
Context Based Real-time Korean Writing Correction for Foreigners
Young-Keun Park, Jae-Min Kim, Seong-Dong Lee, Hyun Ah Lee
http://doi.org/10.5626/JOK.2017.44.10.1087
Educating foreigners in Korean language is attracting increasing attention with the growing number of foreigners who want to learn Korean or want to reside in Korea. Existing spell checkers mostly focus on native Korean speakers, so they are inappropriate for foreigners. In this paper, we propose a correction method for the Korean language that reflects the contextual characteristics of Korean and writing characteristics of foreigners. Our method can extract frequently used expressions by Koreans by constructing syllable reverse-index for eojeol bi-gram extracted from corpus as correction candidates, and generate ranked Korean corrections for foreigners with upgraded edit distance calculation. Our system provides a user interface based on keyboard hooking, so a user can easily use the correction system along with other applications. Our system improves the detection rate for foreign language users by about 45% compared to other systems in foreign language writing environments. This will help foreign users to judge and correct their own writing errors.
Motion Area Detection Algorithm based on Irregularity of Light
http://doi.org/10.5626/JOK.2017.44.10.1094
In this paper, a motion image is detected based on the irregularity of lights. This motion image is extracted by modifying the reflected light region of the 3 way-diff algorithms. 3 way-diff algorithm extracts reflected light region using the 3-successive image. In this algorithm, The reflected light region is a region generated by light in the image production process and is finally created around all objects. The algorithm shows a process to extracting the region. This process is a simple operation, but doesn’t have a defined formula for light. This paper judges that the reflected light region is a kind of noise at the 3 way-diff algorithms and defines the formula for extracting the reflected light region. It shows that compared with the proposed algorithm and existing algorithm through experiment.
Fast Influence Maximization in Social Networks
Yun-Yong Ko, Kyung-Jae Cho, Sang-Wook Kim
http://doi.org/10.5626/JOK.2017.44.10.1105
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.
Efficient and Privacy-Preserving Near-Duplicate Detection in Cloud Computing
Changhee Hahn, Hyung June Shin, Junbeom Hur
http://doi.org/10.5626/JOK.2017.44.10.1112
As content providers further offload content-centric services to the cloud, data retrieval over the cloud typically results in many redundant items because there is a prevalent near-duplication of content on the Internet. Simply fetching all data from the cloud severely degrades efficiency in terms of resource utilization and bandwidth, and data can be encrypted by multiple content providers under different keys to preserve privacy. Thus, locating near-duplicate data in a privacy-preserving way is highly dependent on the ability to deduplicate redundant search results and returns best matches without decrypting data. To this end, we propose an efficient near-duplicate detection scheme for encrypted data in the cloud. Our scheme has the following benefits. First, a single query is enough to locate near-duplicate data even if they are encrypted under different keys of multiple content providers. Second, storage, computation and communication costs are alleviated compared to existing schemes, while achieving the same level of search accuracy. Third, scalability is significantly improved as a result of a novel and efficient two-round detection to locate near-duplicate candidates over large quantities of data in the cloud. An experimental analysis with real-world data demonstrates the applicability of the proposed scheme to a practical cloud system. Last, the proposed scheme is an average of 70.6% faster than an existing scheme.
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