• Journal of Convergence Society for SMB
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• v.2 no.1
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• pp.41-50
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• 2012

In this research, we developed a scalable resource monitoring system for large scale scientific computing data centers. Usually, there are limitations and overheads for keeping track of every computing nodes because of the huge number of computing nodes. So, this research proposes a layered summarizing techniques during collection of all system resource information. The technique results in improved scalability by reducing the amount of information at higher layer. Our prototype system which is implemented with web service is applicable with the HTML5 mobile web technology on smart devices.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.10
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• pp.1101-1108
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• 2015

In this paper, to provide a method and system for providing a network separation virtual machine environment. How to provide this virtual machine environment include phase generating necessary virtual resource requirement for the perform of virtual function and transfer to network changing protocol about request of registration virtual resource. For this reason, Registration procedure is to use a virtual machine for a virtual computing resource allocation and separation combined network any time, it became possible between servers and clients, or mobile phone. At any time, it is possible to process the work in the same environment as in a computer to access the Internet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4197-4219
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• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.592-601
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• 2015

Multitenancy has gained growing importance with the development and evolution of cloud computing technology. In a multitenant environment, multiple tenants with different demands can share a variety of computing resources (e.g., CPU, memory, storage, network, and data) within a single system, while each tenant remains logically isolated. This useful multitenancy concept offers highly efficient, and cost-effective systems without wasting computing resources to enterprises requiring similar environments for data processing and management. In this paper, we propose a novel approach supporting multitenancy features for Apache Hadoop, a large scale distributed system commonly used for processing big data. We first analyze the Hadoop framework focusing on "yet another resource negotiator (YARN)", which is responsible for managing resources, application runtime, and access control in the latest version of Hadoop. We then define the problems for supporting multitenancy and formally derive the requirements to solve these problems. Based on these requirements, we design the details of multitenant Hadoop. We also present experimental results to validate the data access control and to evaluate the performance enhancement of multitenant Hadoop.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.41-47
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• 2013

In this paper, for this open system-based virtual resource monitoring system was designed. Virtual resources, CPU, memory, disk, network, each subdivided into parts, each modular implementation. Implementation results in real time CPU, memory, disk, network information, confirmed the results of monitoring. System designed to implement the Windows, Linux, Xen was used for the operating system, implementation language, C++ was used, the structure of the system, such as the ability to upgrade and add scalability and modularity by taking into account the features available in cloud computing environments applicable to cloud computing, virtual resource monitoring system has been implemented.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.35-43
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• 2020

Cloud computing integrates computing resources such as servers, storage, and networks with virtualization technology to provide suitable services according to user needs. Due to the structural characteristics of sharing physical resources based on virtualization technology, threats to availability can occur, so it is essential to respond to availability threats in cloud computing. Existing over-provisioning method is not suitable because it can generate idle resources and cause under-provisioning to degrade or disconnect service. System resources must be allocated in real-time according to the system load to guarantee the cloud system's availability. Through appropriate management measures, it is necessary to reduce the system load and increase the performance of the system. This paper analyzes the work response time according to the allocation or migration of virtual machines and discusses an efficient resource management method considering the system load.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.60-75
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• 2024

The demand of users, who want to focus on the core functionality of their applications without having to manage complex virtual environments in the cloud environment, has created a new computing model called serverless computing. Within the serverless paradigm, resource provisioning and server administration tasks are delegated to cloud services, facilitating application development exclusively focused on program logic. Serverless computing has upgraded the utilization of cloud computing by reducing the burden on cloud service users, and it is expected to become the basic model of cloud computing in the future. A serverless platform is responsible for managing the cloud virtual environment on behalf of users, and it is also responsible for executing serverless functions that compose applications in the cloud environment. Considering the characteristics of serverless computing in which users are billed in proportion to the resources used, the efficiency of the serverless platform is a very important factor for both users and service providers. This paper aims to identify various factors that affect the performance of serverless computing and analyze the latest research trends related to it. Drawing upon the analysis, the future directions for serverless computing that address key challenges and opportunities in serverless computing are proposed.