• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.180-186
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• 2021

In cloud computing service it is important to share the system resource among multiple instances according to user requirements. In particular, the issue of efficiently distributing I/O resources across multiple instances is paid attention due to the rise of emerging data-centric technologies such as big data and deep learning. However, it is difficult to evaluate the I/O resource distribution of a Linux container, which is one of the core technologies of cloud computing, since conventional I/O benchmarks does not support features related to container management. In this paper, we propose a new I/O performance benchmarking framework that can easily evaluate the resource distribution of Linux containers using existing I/O benchmarks by supporting container-related features and integrated user interface. According to the performance evaluation result with trace-replay benchmark, the proposed benchmark framework has induced negligible performance overhead while providing convenience in evaluating the I/O performance of multiple Linux containers.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.287-293
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• 2012

We introduce a methodology for the study of the application-level performance of time-sharing parallel jobs on a set of compute nodes in high performance clusters and report our findings. We assume that parallel jobs arriving at a cluster need to share a set of nodes with the jobs of other users, in that they must compete for processor time in a time-sharing manner and other limited resources such as memory and I/O in a space-sharing manner. Under the assumption, we developed a methodology to simulate job arrivals to a set of compute nodes, and gather and process performance data to calculate the percentage slowdown of parallel jobs. Our goal through this study is to identify a better combination of jobs that minimize performance degradations due to resource sharing and contention. Through our experiments, we found a couple of interesting behaviors for overlapped parallel jobs, which may be used to suggest alternative job allocation schemes aiming to reduce slowdowns that will inevitably result due to resource sharing on a high performance computing cluster. We suggest three job allocation strategies based on our empirical results and propose further studies of the results using a supercomputing facility at the San Diego Supercomputing Center.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.57-64
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• 2012

Computing environment combined with development of internet and IT technology is changing to cloud computing environment. In addition, cloud computing is revitalized more because of propagation of LTE and suggestion of N-screen Service. Virtualization is the point technology for suggest IT resource to service form to users in this cloud computing. This technology combines other system physically or divides one system logically and uses resource efficiently. Many users can be provided application and hardware as needed using this. But, lately various attack using weak point of virtualization technology are increasing rapidly. In this study, we analyze type and weak point of virtualization technology, the point of cloud computing. And we study about function and the position which intrusion detection system has to prepare in order to detect and block attack using this.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.129-136
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• 2006

Grid computing has been appeared for solving large-scaled data which are not solved by a single computer. Grid computing is a new generation platform which connects geographically distributed heterogeneous resources. However, gathering heterogeneous distributed resources produces many difficult problems. Especially. to assure resource reliability is one of the most critical problems. So, we propose a grid resource scheduling model using grid resource reliability measurement. We evaluate resource reliability based on resource status data and apply it to the grid scheduling model in DEVSJAVA modeling and simulation. This paper evaluates parameters such as resource utilization, job loss and average turn-around time and estimates experiment results of our model in comparison with those of existing scheduling models such as a random scheduling model and a round-robin scheduling model. These experiment results showed that the resource reliability measurement scheduling model provides efficient resource allocation and stable Job processing in comparison with a random scheduling model and a round-robin scheduling model.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.163-168
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• 2022

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

• ETRI Journal
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• v.43 no.1
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• pp.53-63
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• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.449-454
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• 2008

최근의 개인 휴대 장치들의 급속한 발전에 힘입어 pervasive computing 환경은 인프라 시스템의 제약에서 벗어나 개인 장치들의 협력에 의해서 상황에 맞는 서비스를 제공 받을 수 있도록 변하고 있다. 하지만 이러한 ad hoc pervasive 환경은 기존의 스마트 스페이스에서 보다 동적인 변화가 더욱 많은 환경이기 때문에 pervasive computing을 위한 기능들 중에서도 자원 관리가 가장 중요한 역할을 해야 한다. Pervasive computing 환경에서 자원 관리는 자원 모니터링(Resource Monitoring), 자원 발견(Resource Discovery), 자원 할당(Resource Allocation), 자원 적응(Resource Adaptation)의 4가지의 주요 기능으로 구분 될 수 있고, 동적인 변화가 많은 환경에서는 무엇보다도 자원 모니터링이 가장 중요시 되어야 한다. 자원 모니터링에서 정보의 수집은 pull이나 push 방식으로 이루어질 수 있는데, pull 방식은 사용자 요구에 맞춰 요구 했을 때의 가장 최신의 정보를 모아 줄 수 있다. 따라서 이는 자원 발견에 가장 적합하지만 언제 정보 수집을 요청할지는 정보 소비자의 입장에서는 알 수 없기 때문에 push 방식이 다른 여러 기능들을 지원하기 위해서 보다 적절하다. 하지만 대부분의 push 방식은 주기적으로 자신의 정보를 광고하는 방식으로 이루어 진다. 하지만 상황 적응(adaptation)입장에서 봤을 때 특정 수준의 민첩성을 요구하기 위해서는 광고주기를 조절 해야 하고, 이때 필요 이상으로 네트워크의 사용량을 늘릴 수 있다. 뿐만 아니라 변화가 많은지 적은지 등의 각 단말의 상황은 무시된 채 모든 단말들이 동일한 주기로 정보를 광고한다. 이러한 문제점을 해결 하기 위해서 본 논문은 자원 정보 제공자의 상황을 고려한 자원 정보 광고 기법을 제안한다.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.666-676
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• 2006

Rapid advances in ubiquitous computing and its pervasive influence over our society demand an efficient way to locate resources over the network. In mobile ad-hoc networks (MANETs) which is a special type of the sub-networks in ubiquitous environment, effective resource discovery is particularly important, due to their dynamics and the resource constraints on wireless nodes. In this paper, we propose an adaptive and efficient resource discovery strategy for MANETs. Our strategy also provides a solution to bridge different types of networks which coexist in ubiquitous environment.