• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권11호
• /
• pp.5357-5381
• /
• 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.

• International Journal of Advanced Culture Technology
• /
• 제8권3호
• /
• pp.223-230
• /
• 2020

Companies have made an investment of cost and time to optimize processing of a new business model in a cloud environment, applying collaboration technology utilizing business processes in a social network. The collaborative processing method changed from traditional BPM to the cloud and a mobile cloud environment. We proposed a collaborative system for operating processes in social networks using MOXMDR-DAI+ (eXtended Metadata Registry-Data Access & Integration based multimedia ontology). The system operating cloud-based collaborative processes in application of MOXMDR-DAI+, which was suitable for data interoperation. MOXMDR-DAI+ applied to this system was an agent effectively supporting access and integration between multimedia content metadata schema and instance, which were necessary for data interoperation, of individual local system in the cloud environment, operating collaborative processes in the social network. In operating the social network-based collaborative processes, there occurred heterogeneousness such as schema structure and semantic collision due to queries in the processes and unit conversion between instances. It aimed to solve the occurrence of heterogeneousness in the process of metadata mapping using MOXMDR-DAI+ in the system. The system proposed in this study can visualize business processes. And it makes it easier to operate the collaboration process through mobile support. Real-time status monitoring of the operation process is possible through the dashboard, and it is possible to perform a collaborative process through expert search using a community in a social network environment.

• 한국정보통신학회논문지
• /
• 제19권9호
• /
• pp.2114-2120
• /
• 2015

차량 클라우드 기술은 차량 통신 환경기술과 유무선 인터넷에서 활용되고 있는 클라우드 컴퓨팅을 융합한 기술로서 새로운 IT 패러다임으로 부각 받고 있다. 차량 환경에서 효율적인 통신을 사용자로부터 컴퓨터, 센서, 통신, Device, 리소스 등을 제공하여 도로 교통 환경에서 크게 기여할 수 있을 것으로 예상하고 있다. 하지만 차량 클라우드 환경을 적용하기 위해서는 보안문제가 해결되어야 하며, 차량환경의 보안위협과 유․무선환경에서 발생하는 공격기법에 대하여 안전하게 해결되어야 한다. 그러므로 본 논문에서는 차량 클라우드 환경에서 차량과 차량, 차량과 노변과의 안전한 통신을 수행하도록 보안 프레임 워크를 설계하였다. 차량환경의 안전성과 보안성은 차량기반 및 클라우드 환경의 보안요구사항을 만족하였으며, 기존의 차량네트워크의 통신 프로토콜보다 효율성을 높였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권9호
• /
• pp.3663-3679
• /
• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• 대한임베디드공학회논문지
• /
• 제8권6호
• /
• pp.303-309
• /
• 2013

Cloud storage services are used for efficient sharing or synchronizing of user's data across multiple mobile devices. Although cloud storages provide flexibility and scalability in storing data, security issues should be handled. Currently, typical cloud storage services offer data encryption for security purpose but we think such method is not secure enough because managing encryption keys by software and identifying users by simple ID and password are main defectives of current cloud storage services. We propose a secure data access method to cloud storage in mobile environment. Our framework supports hardware-based key management, attestation on the client software integrity, and secure key sharing across the multiple devices. We implemented our prototype using ARM TrustZone and TPM Emulator which is running on secure world of the TrustZone environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권12호
• /
• pp.5780-5802
• /
• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• International Journal of Contents
• /
• 제8권4호
• /
• pp.7-11
• /
• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.

• 한국대기환경학회지
• /
• 제6권2호
• /
• pp.176-182
• /
• 1990

A one dimensional cumulus cloud model has been developed to investigate the size distribution of hydrometeors at different heights and at different stages of cloud development. The model results show that the exponential distribution of droplet radius that was employed by many investigators can misinterpret the cloud dynamic fields during the life cycle of cumulus cloud. The results also show the bimodal distribution of cloud water mixing ratios at 25 $\mu$m and 645 $\mu$m in radius at 45 minutes of simulation time. The developed model can be applied in future on the parameterization of cloud microphysical processes and air pollution models.

• 한국멀티미디어학회논문지
• /
• 제19권2호
• /
• pp.300-307
• /
• 2016

Cloud computing that provide a elastic computing service is more complex compared to the existing computing systems. Accordingly, it has become increasingly important to maintain the stability and reliability of the computing system. And troubleshooting and real-time monitoring to address these challenges must be performed essentially. For these goals, the handling of the log data is needed, but this task in cloud computing environment may be more difficult compared to the traditional logging system. In addition, there are another challenges in order to have the admissibility of the collected log data in court. In this paper, we design secure logging service that provides the management and reliability of log data in a cloud computing environment and then analyze the proposed system.

• Journal of Multimedia Information System
• /
• 제7권2호
• /
• pp.125-136
• /
• 2020

Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.