• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.28-36
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• 2017

The medium access control (MAC) protocol based on dynamic time division multiple access/time division duplex (TDMA/TDD) is responsible for random access control and radio resource allocation in dynamic traffic environments. These functions of random access and resource allocation are very important to prevent wastage of resources and improve MAC performance according to various network conditions. In this paper, we propose new random access and resource allocation schemes to guarantee quality of service (QoS) and provide priority services in a dynamic TDMA/TDD system. First, for the QoS guarantee, we propose an adaptive random access and resource allocation scheme by introducing an access probability. Second, for providing priority service, we propose a priority-based random access and resource allocation scheme by extending the first adaptive scheme in both a centralized and a distributed manner. The analysis and simulation results show that the proposed MAC protocol outperforms the legacy MAC protocol using a simple binary exponential backoff algorithm, and provides good differential performance according to priorities with respect to the throughput and delay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1006-1022
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• 2010

In this paper, a cross-layer resource allocation mechanism is proposed for wireless multimedia service. In particular, a game theory based on quality of service (QoS) for multimedia users is introduced to deal with the fairness of network resource allocation in wireless networks. Moreover, the channel states of wireless users are additionally regarded under the cross-layer design in WiMAX environment. In details, the bargaining solution is adopted to discover the efficient and fair resource allocation strategy for multimedia service in considering QoS in the peak signal-to-noise ratio (PSNR) and the channel states in the carrier-to-interface ratio (CINR). The proposed mechanism is illustrated and evaluated by simulation results of transmitting video sequences in WiMAX environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.512-518
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• 2013

A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.

• ETRI Journal
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• v.34 no.3
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• pp.341-351
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• 2012

We deal with a resource allocation problem for multimedia service discrimination in wireless networks. We assume that a service provider allocates network resources to users who can choose and access one of the discriminated services. To express the rational service selection of users, the utility function of users is devised to reflect both service quality and cost. Regarding the utility function of a service provider, total profit and efficiency of resource usage have been considered. The proposed service discrimination framework is composed of two game models. An outer model is a repeated Stackelberg game between a service provider and a user group, while an inner model is a service selection game among users, which is solved by adopting the Kalai-Smorodinsky bargaining solution. Through simulation experiments, we compare the proposed framework with existing resource allocation methods according to user cost sensitivity. The proposed framework performed better than existing frameworks in terms of total profit and fairness.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.21-41
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• 2005

This study analyzes the Internet utilization pattern of customer by comprehensively investigating the previous studies on the behavior pattern of customer in terms of Internet business. Based on the analysis, this study develops research framework that supports strategic decision-making for resource allocation in Internet business. Such research framework would be helpful for providing the typology of Internet business model that can be specialized by each industry. As a result of the simulation analysis, it was found that the optimal resource allocation portfolio providing maximum profits to the Internet bookstore involves large-scale investment on delivery service and customer support service which are the key factors for post-purchase customer satisfaction, regardless of the growth pattern or size of Internet bookstore market. Consequently, from the above analysis, the investment ratio of resources for the profit maximization of Internet bookstore was drawn. Conclusively, based on the comprehensive examination of the results, this study provided a framework for dynamic resource allocation decision-making, and proposed a management strategy which allows consumers to shop under more favorable environment, and simultaneously enables the Internet bookstore to accomplish management objectives such as continuous growth and profit maximization.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.95-100
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• 2022

Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

• ETRI Journal
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• v.37 no.4
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• pp.657-666
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• 2015

We propose a two-stage resource allocation algorithm for the high link utilization of an orthogonal frequency-division multiplexing passive optical network (OFDM-PON). An OFDM-PON is assumed to use a synchronous frame structure in supporting service differentiation. In distributing resources, the proposed algorithm first allocates a time window for each optical network unit (ONU), and then it arranges a subchannel, which is a group of subcarriers. This algorithm needs to satisfy two constraints. First, computations for the resource allocation should be done using a frame unit. Second, an ONU has to use a single subchannel to send upstream data for multiple services within a frame duration. We show through a computer simulation that the proposed algorithm improves the link utilization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2815-2839
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• 2020

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.161-168
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• 2012

The study of Cloud based system is emerging to become the core technology in IT field due to the tremendous growth of Cloud Computing. Researches to deliver applications to Thin-Client based mobile virtual machine and Desktop as a Service(DaaS) using Cloud Computing are conducted actively. In this paper, we propose a Cloud system to run the mobile application in the mobile Thin-Client device and resource allocation mechanism Dynamic Resource Allocation Manager for Mobile Application(DRAMMA). Thus, through performance check, we show DRAMMA has improved the utilization of Cloud system, less migration of virtual machines and decreased the error rate of resource allocation. Also our proposed system delivers service more efficiently than the previous resource allocation algorithm.