• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.57-68
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• 2012

The recent proliferation of various smart devices like the smartphone, tablet PC, and smart TV enables consumers to download various applications from the network and to access private files stored in their desktop server at any time and at any place. The trend of ubiquitous access seems to have become stronger and more diversified toward a ubiquitous network computing environment with the aggressive deployment of commercial cloud services. Recently, many Korean network service providers launched commercial B2C public cloud services, which were widely adopted by smart device users. They include Daum cloud, N drive, ucloud, and uplus box, mostly provided by major Korean telecom companies and portals. This paper aims to explore consumers' adoption behaviors toward the B2C public cloud services that were recently deployed in the Korean market. In order to achieve the goal, we identified key influencing factors that affect the consumers' adoption behaviors, based on an extension of the technology acceptance model (TAM). Several hundred smart device users were surveyed to test the generic regression model with the extended set of TAM variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.