• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4849-4864
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• 2019

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.31-37
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• 2020

The cloud computing industry is regarded as a key element of the ICT industry and an important industry that will be a watershed for the future development of ICT industry. Korea has established the 1st~2nd cloud computing development basic plan to induce the growth of the cloud industry. However, the domestic information security guide provides technical vulnerability analysis criteria for Unix and Windows servers, DBMS, network equipment, and security equipment, but fails to provide vulnerability analysis criteria for hypervisors that are key elements of cloud computing. Organizations that have deployed cloud systems will be able to assist in vulnerability analysis using the criteria presented in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.107-118
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• 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4165-4187
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• 2016

As an emerging technology, cloud computing is a revolution in information technology that attracts significant attention from both public and private sectors. In this paper, we proposed a dynamic approach for live migration to obviate overloaded machines. This approach is applied on OpenStack, which rapidly grows in an open source cloud computing platform. We conducted a cost-aware dynamic live migration for virtual machines (VMs) at an appropriate time to obviate the violation of service level agreement (SLA) before it happens. We conducted a preemptive migration to offload physical machine (PM) before the overload situation depending on the predictive method. We have carried out a distributed model, a predictive method, and a dynamic threshold policy, which are efficient for the scalable environment as cloud computing. Experimental results have indicated that our model succeeded in avoiding the overload at a suitable time. The simulation results from our solution remarked the very efficient reduction of VM migrations and SLA violation, which could help cloud providers to deliver a good quality of service (QoS).

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

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.99-105
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• 2021

The cloud computing evolution has brought us increasing necessity to manage virtual resources. For this reason, Kubernetes has developed to realize autonomous resource management in a large scale. It provides cloud computing infrastructure to handle cluster creations and deletions in a secure virtual computing environment. In the paper, we provide a monitoring scheme in which users can observe securely encrypted protocols while each Kubernetes component exchanges their packets. Eventually, users can utilize the proposed scheme for debugging as well as monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.868-877
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• 2013

This paper will provide the cloud service based on PaaS that can enhance reusability of development in the cloud computing environment. The cloud service based on PaaS is the cloud service of platform in the side of development, which provide the reusable framework service that is beyond the existing development tool or management tool service. This reusable framework service will be enhanced reusability using a variety of distributed services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4759-4775
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• 2015

The reduction of power consumption in large-scale datacenters is highly-dependent on the use of virtualization to consolidate multiple workloads. However, these consolidation strategies must also take into account additional important parameters such as performance, reliability, and profitability. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. In this paper, we put forward a data center monitoring strategy which dynamically alters its approach depending on the cloud system's current state. Results show that our proposed scheme outperformed strategies which only focus on a single metric such as SLA-Awareness and Energy Efficiency.