• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1480-1493
• /
• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.163-172
• /
• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Proceedings of the Korea Water Resources Association Conference
• /
• 1992.07a
• /
• pp.280-287
• /
• 1992

• The Journal of Korean Association of Computer Education
• /
• v.13 no.5
• /
• pp.81-90
• /
• 2010

Cloud computing is an Internet-based computing paradigm that provides services in a virtualized form composed of plenty of resources sharable. In Cloud computing environments, gossip protocols are engaged as a method to rapidly disseminate the state information for innumerable resources. Although gossip protocols provide a robust and scalable multicast, there is a drawback that requires redundant messages in satisfying 100% of reachability. In our study, we propose a Group-based Gossip Multicast Protocol in order to reduce the message overhead while delivering the state information efficiently. Furthermore, we verified the performance of the proposed protocol through experiments.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.8
• /
• pp.9-15
• /
• 2013

As the resources of cloud computing are essentially heterogeneous and jobs have various characteristics, resource allocation to jobs is one of important problems. We define this issue as a multi-criteria decision-making problem. This paper proposes a priority-based job scheduling algorithm based on analytic hierarchy process (AHP). On the first step, jobs are classified based on their preferences. On the second step, response time, system utilization, and load becomes decision criteria based on the AHP algorithm. Jobs are allocated to adequate resources through their priorities that are calculated by the AHP algorithm. Through analysis and experiment of the proposed algorithm, we are to confirm that the scheme can schedule jobs as well as utilize its resource efficiently.

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.1
• /
• pp.11-19
• /
• 2021

The emerging Fifth Generation (5G) network technology brings us a new demand for low latency services. However, it may not be possible for long-distanced cloud computing servers to support users with satisfactory low latency services. For this reason, Multi-access Edge Computing (MEC) technology are gaining attraction since it is designed to provide low latency services to users by placing cloud computing resources to base-stations or mobile switching centers nearby users. Accordingly, it is necessary to verify the deployed containers on the MECs are reliable enough to provide low latency services empirically. For the purpose, we develop a testing tool to verify the reliability as well as network resources status of running MECs by deploying containers on the MECs in a Kubernetes environment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.05a
• /
• pp.183-186
• /
• 2013

Modern day despite technology advancements that manufacture a new generation of mobile devices with generous resources, the fact that they can offer only limited processing capacity still remains a painful experience. So far, a number of research studies have been carried out, trying to eliminate problems arising from shortcomings in the connection between thin clients and cloud networks, yet little have been found efficient. In this paper, we present a novel approach, taking advantage of collaboration of thin and thick clients, particularly aiming at optimizing data distribution by splitting data and utilizing cloud computing (CC) resources so that expected Quality-of-Service (QoS) requirements can be met. Moreover, we conduct simulations to evaluate our approach. Our results evaluation shows that our approach has better performance than existing approaches.

• Journal of Platform Technology
• /
• v.10 no.4
• /
• pp.70-81
• /
• 2022

In this paper, we proposed a Virtual Machine Placement (VMP) method to provide live vertical scaling services for cloud resources. Since free space on the physical server must be secured in advance for vertical scaling, a "general-mixed-vertical" mode conversion algorithm based on the FirstFit placement strategy that variably adjusts the allocation ratio of virtual servers to physical servers for this purpose is presented. Simulations were performed using parameters such as vertical scaling ratio, virtualization ratio, and free resource ratio. When the vertical scaling ratio is 50%, considering free space, 150% of resources are required as a whole, but simulation results of the proposed algorithm show that only up to 125% of free space is required.

• The Journal of Society for e-Business Studies
• /
• v.17 no.1
• /
• pp.111-136
• /
• 2012

The global recession has made it more difficult for companies to invest in IT, and they are increasingly aware of the environmental costs of so doing. In these circumstances, cloud computing has emerged as a new paradigm in the business IT sector. Governments, institutes and companies around the world, as well as specifically in Korea since 2009, have turned to this model of providing IT resources. This study is concerned to identify those characteristics of cloud computing that affect its introduction on a company's part; it offers a theoretical framework describing cloud services and seeks to establish causal linkages between antecedent factors and a company's introduction and application of this form of IT provision. The features of cloud computing in particular contexts that the study selected for analysis were its scalability, speed, security, potential compatibility with existing services, efficiency, economic feasibility, dependency and credibility. The study thus related these to whether or not cloud computing was adopted, verifying adjustment effects for cloud services. On the basis of a survey of enterprise IT decision-makers, it emerged through a statistical analysis of correlations that cloud computing's efficiency, economic feasibility and credibility had an effect on its introduction. This study's results should be of use to vendors and potential purchasers of cloud computing services. It is one of the first pieces of research on cloud computing from the customer perspective, based on the perceived characteristics of cloud services as they are seen and valued by users.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.262-263
• /
• 2012

하나의 클라우드 서비스 제공자가 제공하는 컴퓨팅 자원의 양과 종류에는 한계가 있다. 이를 극복하려면 클라우드 협업 기술인, 다중 클라우드 자원 브로커를 활용하여, 이종 자원을 탐색하여 사용자에게 제공하는 기술이 필요하다. 본 논문에서는 다중 클라우드 자원 브로커 시스템의 구조를 제안하고, 이종 자원 탐색에 필요한 성능 지표와 LP 모델을 이용한 최적화 기법을 제안하고자 한다.