• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.57-67
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• 2021

Modern cloud computing is rapidly changing from traditional hypervisor-based virtual machines to container-based cloud-native environments. Due to limitations in I/O performance required for both virtual machines and containers, the use of high-speed storage (SSD, NVMe, etc.) is increasing, and in-memory computing using main memory is also emerging. Running a virtual environment on main memory gives better performance compared to other storage arrays. However, RAM used as main memory is expensive and due to its volatile characteristics, data is lost when the system goes down. Therefore, additional work is required to run the virtual environment in main memory. In this paper, we propose a hybrid in-memory storage that combines a block storage such as a high-speed SSD with main memory to safely operate virtual machines and containers on main memory. In addition, the proposed storage showed 6 times faster write speed and 42 times faster read operation compared to regular disks for virtual machines, and showed the average 12% improvement of container's performance tests.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.189-196
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• 2020

Recently, GPU cloud computing technology applying GPU(Graphics Processing Unit) devices to virtual machines is widely used in the cloud environment. In a cloud environment, GPU devices assigned to virtual machines can perform operations faster than CPUs through massively parallel processing, which can provide many benefits when operating high-performance computing services in a variety of fields in a cloud environment. In a cloud environment, a GPU device can help improve the performance of a virtual machine, but the virtual machine scheduler, which is based on the CPU usage time of a virtual machine, does not take into account GPU device usage time, affecting the performance of other virtual machines. In this paper, we test and analyze the performance degradation of other virtual machines due to the virtual machine that performs GPGPU(General-Purpose computing on Graphics Processing Units) task in the direct path based GPU virtualization environment, which is often used when assigning GPUs to virtual machines in cloud environments. Then to solve this problem, we propose a GPGPU task management method for a virtual machine.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.961-971
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• 2019

Isolation between virtual machines in a cloud computing environment is an important security factor. The violation of isolation between virtual machines leads to interferences of shared resources and the implementation of covert channels. In this paper, the structure of Hyper-V hypervisor is analyzed to implement covert channels between virtual machines. Hyper-V uses a mutex technique for mutual exclusion between virtual machines. It indicates that isolation of virtual machines is violated and covert channels can be implemented due to mutex. We implemented several covert channels by designing a method for searching mutex resources applicable to Hyper-V with complex architectures. The mutex-based covert channel is not hardware dependent. If the covert channel is detected or defended, the defensive technique can be avoided by using the other covert channel among several covert channels.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.524-527
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• 2011

자바는 프로그램의 신뢰성을 높인 언어의 특성상, 객체에 접근하는 명령어 마다 객체가 실제로 할당되었는지 되지 않았는지 확인하기 위하여 해당 객체가 널인지 아닌지 확인하는 널 포인터 검사를 수행한다. 모든 객체 접근마다 널 여부를 검사하는 것은 프로그램 상으로도 많은 overhead가 된다. 이 논문에서는 안드로이드의 자바 가상 머신인 달빅 가상 머신 상에서 필요 없는 널 포인터 검사를 제거함으로써 가상 머신의 성능 향상을 꾀하였다.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.239-251
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• 2018

In existing cloud services, information security and privacy concerns have been worried, and have become one of the major factors that hinder the popularization and promotion of cloud computing. As the cloud computing infrastructure, the security of virtual machine systems is very important. This paper presents an immune-inspired intrusion detection model in virtual machines of cloud computing environment, denoted I-VMIDS, to ensure the safety of user-level applications in client virtual machines. The model extracts system call sequences of programs, abstracts them into antigens, fuses environmental information of client virtual machines into danger signals, and implements intrusion detection by immune mechanisms. The model is capable of detecting attacks on processes which are statically tampered, and is able to detect attacks on processes which are dynamically running. Therefore, the model supports high real time. During the detection process, the model introduces information monitoring mechanism to supervise intrusion detection program, which ensures the authenticity of the test data. Experimental results show that the model does not bring much spending to the virtual machine system, and achieves good detection performance. It is feasible to apply I-VMIDS to the cloud computing platform.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.5
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• pp.123-136
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• 2021

RPC (Remote Procedure Call)-based Graphics Processing Unit (GPU) virtualization technology is one of the technologies for sharing GPUs with multiple user virtual machines. However, in a cloud environment, unlike CPU or memory, general GPUs do not provide a resource isolation technology that can limit the resource usage of virtual machines. In particular, in an RPC-based virtualization environment, since GPU tasks executed in each virtual machine are performed in the form of multi-process, the lack of resource isolation technology causes performance degradation due to resource competition. In addition, the GPU memory competition accelerates the performance degradation as the resource demand of the virtual machines increases, and the fairness decreases because it cannot guarantee equal performance between virtual machines. This paper, in the RPC-based GPU virtualization environment, analyzes the performance degradation problem caused by resource contention when the GPU memory requirement of virtual machines exceeds the available GPU memory capacity and proposes a GPU memory management technique to solve this problem. Also, experiments show that the GPU memory management technique proposed in this paper can improve the performance of GPGPU tasks.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.965-972
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• 2004

This Paper presents the experiences of the static and dynamic library function connection technique for the embedded virtual machines, base on the native function connection methods of the virtual machines such as KVM, Waba VM. For this goals, we implements the new native function table for the static and dynamic library function connection technique base on the native function connection methods of the virtual machines such as KVM, Waba VM. And we presents the variety experiment and analysis results using the implemented technique.

• The Journal of Korean Association of Computer Education
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• v.18 no.2
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• pp.83-90
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• 2015

In an virtualization environment, several virtual machines use physical resources together. If a specific virtual machine uses to much of the computing resources, other machines may not be working properly. There are various method to solve this problem. Most representative study is to migrate a specified virtual machines to a different server, a target server. In this study, server load can be transferred to a target server by the remigrate of the load imposed on virtual machine. It is still problematic that virtual machine has to remigrate to a different server. This thesis has proposed the algorithm determining the remigration targets by applying dynamic thresholds to solve those problems. The migration algorithm applies dynamic thresholds according to the following criteria. Firstly, the usage of CPU, network and memory; secondly, decide the set of artificial machine and the target server based on the resources surpassed thresholds; thirdly, determine artificial machines based on the resource usage in the target server.