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

• ETRI Journal
• /
• v.27 no.1
• /
• pp.43-52
• /
• 2005

In this paper, we introduce an adaptive radio resource allocation for IP-based mobile satellite services. We also present a synchronous multibeam CDMA satellite system using an orthogonal resource sharing mechanism among downlink beams for the adaptive packet transmission. The simulation results, using a Ka-band mobile satellite channel and various packet scheduling schemes, show that the proposed system and resource allocation scheme improves the beam throughput by more than two times over conventional systems. The simulation results also show that, in multibeam satellite systems, a system-level adaptation to a user's channel and interference conditions according to user locations and current packet traffic is more efficient in terms of throughput improvement than a user-level adaptation.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.41 no.2
• /
• pp.1-8
• /
• 2004

In this paper, we proposed ARAHE(Adaptive Resource Allocation algorithm for HIPERLAN/2 with Error channel). It uses EIB(Error Indication Bits) for efficient resource allocation. We evaluate the performance of ARAHE by simulation and the result shows ARAHE has better performance than current method in the case of delay, utilization and TSR(Transmission Success Rate).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.393-402
• /
• 2017

Just like terrestrial cellular networks, underwater cellular communication networks, which can manage the overall network resource by adaptively allocating backhaul resource for each base station according to its ingress traffic, are necessary. In this paper, a new resource allocation protocol is proposed for the underwater cellular communication network, allocating backhaul resource of a base station proportional to its ingress traffic to the base station. This protocol is classified into two types dependent upon allocation period: the resource allocation protocol with adaptive period and that with fixed period. In order to determine a proper resource allocation protocol, the performance of the two protocols, in terms of reception rate, message overhead, and latency is compared and investigated via simulation. As a result, the resource protocol with adaptive period outperforms that with fixed period; the resource allocation protocol with fixed period results in a maximum of $10^2$ order longer queueing delay as well as $10^2$ order greater message overhead than that with adaptive period.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.12
• /
• pp.2294-2314
• /
• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.8C
• /
• pp.758-770
• /
• 2002

In this paper, we propose a novel resource allocation scheme which can maximize capacity for QoS guarantee of Assured Service in Differentiated Services. Performance of the proposed resource allocation scheme is analyzed with each buffer management scheme such as RIO and Adaptive RIO. To prevent an early random drop of the admitted In-profile packet, Adaptive RIO scheme updates parameters of RIO scheme every time interval according to the estimated numbers of maximum packet arrivals of In-profile traffic and Out-of-profile traffic during the next time interval. The numbers of maximum packet arrivals during the next time interval are estimated based on the buffer size determined by the network topology and the ratio of bandwidth allocated to each subclass. We can find from simulation results that proposed resource allocation scheme with Adaptive RIO can guarantee QoS and can maximize capacity for Assured Service.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.7
• /
• pp.219-230
• /
• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.1
• /
• pp.38-55
• /
• 2013

Wireless network systems provide fast data transmission rates and various services to users of mobile devices such as smartphones and smart pads. Because many people use high-performance mobile devices, the use of real-time multimedia services is increasing rapidly. However, the preoccupation of resources by real-time traffic users is causing harm to other services-for example, frequent call interference, lowered service quality, and poor network performance. This paper suggests a resource allocation algorithm for effective traffic service support in a hybrid network. The main objective is to obtain an optimum value of data rates by comparing user requirements with the amount of resources that can be allocated. A new mechanism based on Adaptive-Quality of Service (QoS) and a monitoring system based on Queue-Aware are proposed. Adaptive-QoS supports effective resource control according to the type of traffic service, and the monitoring system based on Queue-Aware measures the amount of resources in order to calculate the maximum that can be allocated. We apply our algorithm to a test system and use Qualnet 4.5.1 to evaluate its performance.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.8
• /
• pp.81-88
• /
• 2009

Many studies and policies are suggested for customer satisfaction to survive in multimedia content service markets. there are policies like a segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM. The problem of this policy is fixed allocation of media server resources. It is inefficient for costly media server resource. To resolve the problem and enhance the utilization of media server resource, the ACRFA (Adaptive Client Request Filtering Algorithm) was suggested per cluster to allocate media server resources by flexible resource allocation method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.5
• /
• pp.691-708
• /
• 2010

An important issue of supporting multi-users with diverse quality-of-service (QoS) requirements over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resource while, at the same time, to meet each communication service QoS requirement. In this work, we study the problem of a variety of communication services over multi-media heterogeneous cognitive OFDM system. We first divide the communication services into two parts. Multimedia applications such as broadband voice transmission and real-time video streaming are very delay-sensitive (DS) and need guaranteed throughput. On the other side, services like file transmission and email service are relatively delay tolerant (DT) so varying-rate transmission is acceptable. Then, we formulate the scheduling as a convex optimization problem, and propose low complexity distributed solutions by jointly considering channel assignment, bit allocation, and power allocation. Unlike prior works that do not care computational complexity. Furthermore, we propose the FAASA (Fairness Assured Adaptive Sub-carrier Allocation) algorithm for both DS and DT users, which is a dynamic sub-carrier allocation algorithm in order to maximize throughput while taking into account fairness. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.