• Title, Summary, Keyword: 클라우드렛

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A Task Offloading Approach using Classification and Particle Swarm Optimization (분류와 Particle Swarm Optimization을 이용한 태스크 오프로딩 방법)

  • Mateo, John Cristopher A.;Lee, Jaewan
    • Journal of Internet Computing and Services
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    • v.18 no.1
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    • pp.1-9
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    • 2017
  • Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.

A Dynamic Task Distribution approach using Clustering of Data Centers and Virtual Machine Migration in Mobile Cloud Computing (모바일 클라우드 컴퓨팅에서 데이터센터 클러스터링과 가상기계 이주를 이용한 동적 태스크 분배방법)

  • Mateo, John Cristopher A.;Lee, Jaewan
    • Journal of Internet Computing and Services
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    • v.17 no.6
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    • pp.103-111
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    • 2016
  • Offloading tasks from mobile devices to available cloud servers were improved since the introduction of the cloudlet. With the implementation of dynamic offloading algorithms, mobile devices can choose the appropriate server for the set of tasks. However, current task distribution approaches do not consider the number of VM, which can be a critical factor in the decision making. This paper proposes a dynamic task distribution on clustered data centers. A proportional VM migration approach is also proposed, where it migrates virtual machines to the cloud servers proportionally according to their allocated CPU, in order to prevent overloading of resources in servers. Moreover, we included the resource capacity of each data center in terms of the maximum CPU in order to improve the migration approach in cloud servers. Simulation results show that the proposed mechanism for task distribution greatly improves the overall performance of the system.

A Design of Analyzing effects of Distance between a mobile device and Cloudlet (모바일 장치와 구름을 사이에 거리의 효과 분석설계)

  • Eric, Niyonsaba;Jang, Jong-Wook
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.11
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    • pp.2671-2676
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    • 2015
  • Nowadays, Mobile devices are now capable of supporting a wide range of applications. Unfortunately, some of applications demand an ever increasing computational power and mobile devices have limited resources due to their constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To deal with mobile devices' constraints, researchers envision extending cloud computing services to mobile devices using virtualization techniques to shift the workload from mobile devices to a powerful computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile devices), its functioning and in our future work, analyze effects of distance between cloudlet and mobile devices.

An Analysis System Using Big Data based Real Time Monitoring of Vital Sign: Focused on Measuring Baseball Defense Ability (빅데이터 기반의 실시간 생체 신호 모니터링을 이용한 분석시스템: 야구 수비능력 측정을 중심으로)

  • Oh, Young-Hwan
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.1
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    • pp.221-228
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    • 2018
  • Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.