• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of KIISE
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• v.41 no.10
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• pp.859-869
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• 2014

In recent years, the obfuscation techniques are commonly exploited to protect malwares, so obfuscated malwares have become a big threat. Especially, it is extremely hard to analyze virtualization-obfuscated malwares based on unusual virtual machines, because the original program is hidden by the virtual machine as well as its semantics is mixed with the semantics of the virtual machine. To confront this threat, we suggest a framework to analyze virtualization-obfuscated programs based on the dynamic analysis. First, we extract the dynamic execution trace of the virtualization-obfuscated executables. Second, we analyze the traces by translating machine instruction sequences into the intermediate representation and extract the virtual machine architecture by constructing dynamic context flow graphs. Finally, we extract abstract semantics of the original program using the extracted virtual machine architecture. In this paper, we propose a method to extract the information of the original program from a virtualization-obfuscated program by some commercial obfuscation tools. We expect that our tool can be used to understand virtualization-obfuscated programs and integrate other program analysis techniques so that it can be applied to analysis of the semantics of original programs using the abstract semantics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5642-5670
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• 2017

Antivirus is an important issue to the security of virtual machine (VM). According to where the antivirus system resides, the existing approaches can be categorized into three classes: internal approach, external approach and hybrid approach. However, for the internal approach, it is susceptible to attacks and may cause antivirus storm and rollback vulnerability problems. On the other hand, for the external approach, the antivirus systems built upon virtual machine introspection (VMI) technology cannot find and prohibit viruses promptly. Although the hybrid approach performs virus scanning out of the virtual machine, it is still vulnerable to attacks since it completely depends on the agent and hooks to deliver events in the guest operating system. To solve the aforementioned problems, based on in-memory signature scanning, we propose an agentless runtime antivirus system VirtAV, which scans each piece of binary codes to execute in guest VMs on the VMM side to detect and prevent viruses. As an external approach, VirtAV does not rely on any hooks or agents in the guest OS, and exposes no attack surface to the outside world, so it guarantees the security of itself to the greatest extent. In addition, it solves the antivirus storm problem and the rollback vulnerability problem in virtualization environment. We implemented a prototype based on Qemu/KVM hypervisor and ClamAV antivirus engine. Experimental results demonstrate that VirtAV is able to detect both user-level and kernel-level virus programs inside Windows and Linux guest, no matter whether they are packed or not. From the performance aspect, the overhead of VirtAV on guest performance is acceptable. Especially, VirtAV has little impact on the performance of common desktop applications, such as video playing, web browsing and Microsoft Office series.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2670-2677
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• 2014

The technique of virtual machine construction using hypervisor such as Xen and KVM is mainly used for implementation of cloud computing infrastructure. This technique is efficient in allocating and managing resources compared to the existing operation methods. However it requires high resource usage when constructing virtual machines and results in wasting of resources when not using the allocated resources. Docker is a technique based on the container method to resolve such a problem. This paper shows the container method such as Docker is efficient as a web construction technique by comparing virtual machine method to container method. It is shown to be especially useful when storing data into DB or storage devices in such environments of web server or program development. In the upcoming cloud computing environment the container method such as Docker is expected to improve the resource efficiency and the convenience of management.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.326-335
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• 2002

Recently there have been a number of researches to provide code mobility to lazy functional language (LFL) programs by translating LFL programs to Java programs. These approaches are basically baled on architectural similarities between abstract machines of LFLs and Java. The abstract machines of LFLs and Java programming language, Spineless Tagless G-Machine(STGM) and Java Virtual Machine(JVM) respectively, share important common features such as built- in garbage collector and stack machine architecture. Thus, we can provide code mobility to LFLs by translating LFLs to Java utilizing these common features. In this paper, we propose a new translation scheme which fully utilizes architectural common features between STGM and JVM. By redefining STGM as an eval-apply evaluation model, we have defined a new translation scheme which utilizes Java Virtual Machine Stack for function evaluation and totally eliminates stack simulation which causes array manipulation overhead in Java. Benchmark program translated to Java programs by our translation scheme run faster on JDK 1.3 than those translated by the previous schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4165-4187
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• 2016

As an emerging technology, cloud computing is a revolution in information technology that attracts significant attention from both public and private sectors. In this paper, we proposed a dynamic approach for live migration to obviate overloaded machines. This approach is applied on OpenStack, which rapidly grows in an open source cloud computing platform. We conducted a cost-aware dynamic live migration for virtual machines (VMs) at an appropriate time to obviate the violation of service level agreement (SLA) before it happens. We conducted a preemptive migration to offload physical machine (PM) before the overload situation depending on the predictive method. We have carried out a distributed model, a predictive method, and a dynamic threshold policy, which are efficient for the scalable environment as cloud computing. Experimental results have indicated that our model succeeded in avoiding the overload at a suitable time. The simulation results from our solution remarked the very efficient reduction of VM migrations and SLA violation, which could help cloud providers to deliver a good quality of service (QoS).

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1303-1310
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• 1999

In this paper, the Timed Petri-Nets(TPN) modeling of Modular Cell Manufacturing Systems(MCMS) was investigated to overcome the limit of batch mode operation, which has been one of the most popular manufacturing types to produce an extensive industrial output and to be able to adopt to suitable and quickly changing manufacturing environments. A model of the MCMS was developed in reference to the actual TFT-LCD manufacturing system. TFT-LCD manufacturing system is not mass-productive in batch mode, but it operates in the form of MCMS which consists of a sequence of several cells with four processes of operation, including those of color filter(C/F), TFT, cell, and module. The cell process is further regrouped in those of Front-End and Back-End. For the Back-End cell process, it is reconstructed into a virtual model, consisting of three cells. The TPN modeling encompasses those properties, such as states and operations of machines, the number of buffers, and the processing time. The performance of the modeling was further examined in terms of scheduling system. The productivity in each cells was examined with respect to the change of failure rate of the cell machines and Automatic Guided Vehicles(AGV) using simulation by TPN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1141-1163
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• 2019

In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.486-492
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• 2011

In this paper we analyzed performance of Andriod's Davik virtual machine(VM) using standard benchmark and compared the result with the embedded Java virtual machine. We used a well known benchmark suit named SPECJVM for the measurement. For the fair comparison, Sun Java embedded JVM is ported and the same benchmark is ported on it. The Odriod smartphone hardware platform is used as the target hardware. We have added a Just-In-Time compiler to Dalvik, which is not supported in the recent Android release, and measured performance improvement. The experiment result show that Dalvik achieved 15% and Dalvik with JIT shows 63% of the Sun's JVM performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.67-71
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• 2023

Recently, there are increasing cases of managing software labs by assigning virtual PCs in the cloud instead of physical PCs to each student. In this paper, we design and implement a Linux-based software practice platform that allows students to efficiently build their environments in the cloud. In our platform, instructors can create and control virtual machine templates for all students at once, and students practice on their own machines as administrators. Instructors can also troubleshoot each machine and restore its state. Meanwhile, the biggest obstacle to implementing this approach is the difficulty of predicting the costs of cloud services instantly. To cope with this situation, we propose a model that can estimate the cost of cloud resources used. By using daemons in each user's virtual machine, we instantly estimate resource usage and costs. Although our model has very low overhead, the predicted results are very close to the actual resource usage measured by cloud service providers. To further validate our model, we used the proposed platform in a Linux practice lecture for a semester and confirmed that the proposed model is very accurate.