• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4108-4122
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• 2016

To address challenges of an unprecedented growth in mobile data traffic, the ultra-dense network deployment is a cost efficient solution to off-load the traffic over other small cells. However, the real traffic is often much lower than the peak-hour traffic and certain small cells are superfluous, which will not only introduce extra energy consumption, but also impose extra interference onto the radio environment. In this paper, an elastic energy efficient cell management scheme is proposed based on flow scheduling among multi-layer ultra-dense cells by a SDN controller. A significant power saving was achieved by a cell-level energy manager. The scheme is elastic for energy saving, adaptive to the dynamic traffic distribution in the office or campus environment. In the end, the performance is evaluated and demonstrated. The results show substantial improvements over the conventional method in terms of the number of active BSs, the handover times, and the switches of BSs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1359-1375
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• 2015

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1282-1301
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• 2015

Traditional infrastructure has been superseded by cloud computing, due to its cost-effective and ubiquitous computing model. Cloud computing not only brings multitude of opportunities, but it also bears some challenges. One of the key challenges it faces is recovery of computing nodes, when an Information Technology (IT) failure occurs. Since cloud computing mainly depends upon its nodes, physical servers, that makes it very crucial to recover a failed node in time and seamlessly, so that the customer gets an expected level of service. Work has already been done in this regard, but it has still proved to be trivial. In this study, we present a Cost-Time aware Genetic scheduling algorithm, referred to as CTaG, not only to globally optimize the performance of the cloud system, but also perform recovery of failed nodes efficiently. While modeling our work, we have particularly taken into account the factors of network bandwidth and customer's monetary cost. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results show performance gain of our work over the others, in some particular scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3302-3320
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• 2014

An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

• 한국인터넷방송통신학회논문지
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• 제10권5호
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• pp.293-298
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• 2010

유비쿼터스 센서 네트워크(Ubiquitous Sensor Network) 기술의 핵심은 저전력 무선 통신기술과 효율적 라우팅을 위한 적절한 자원할당 기술이다. 센서 네트워크에서 효율적인 자원할당을 위해서는 서비스에 따른 차별화된 자원할당 방식이 필요하다. 이를 위하여, 본 논문에서는 유선망에 사용되는 PQ와 WRR의 단점을 보완하여 USN에 적용이 가능한 스케줄러 알고리즘을 제안한다. 제안된 알고리즘은 센서 네트워크에서 각 클래스의 큐 상태를 체크하여 퍼지 이론을 적용한 제어 정책에 따라 WRR 스케쥴러의 가중치를 동적으로 할당하였다. 시뮬레이션 결과 제안된 알고리즘은 EF 클래스의 패킷 손실률에서 WRR 스케쥴러 방식보다 평균 6.5% 향상되었으며, AF4 클래스에서는 PQ 방식보다 평균 45% 향상된 결과를 보였다.

• 한국인터넷방송통신학회논문지
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• 제18권1호
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• pp.37-41
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• 2018

본 논문에서는 참고문헌[1] 논문에서 제안한 고유의 링크 스케줄링 방법이 적용된 IEEE802.15.4e TSCH (Time-Slotted Channel Hopping)의 전력 소비 측면에서 절감 능력을 기존 단일채널 IEEE802.15.4와 비교하여 분석한다. TSCH 방식이 기존 방식에 비해 어떤 트래픽 조건하에서도 전력소모가 적게 하는 것으로 나타난다. 그 이유는 첫째, 충돌이 발생하지 않는 스케줄링 방식으로 인해 백오프 시간이 없다는 점과 둘째, MAC 오프셋 시간변수들의 차이로 인한 것으로 판단된다. 마지막으로 TSCH에서는 디바이스들은 자신의 스케줄이 아닌 타임 슬롯 동안은 sleep을 통해 전력 소모를 줄일 수 있음을 볼 수 있다.

• 한국인터넷방송통신학회논문지
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• 제18권2호
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• pp.55-62
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• 2018

VANET은 급격히 부상하고 있는 서비스 영역으로 수 ms 수준의 엄격한 latency 요구사항을 가진다. 이러한 수준의 낮은 latency를 보장하기 위해 latency가 보장되면서 coordinator에 의한 scheduling 없이 join/leave가 자유로운 Autonomous TDMA(ATDMA)가 제안된 바 있다. 본 연구에서는 hidden node, 채널 페이딩, 노드 밀집도 변이 등이 존재하는 non-perfect decoding 환경에서 동작하도록 ATDMA를 확장한 ATDMA revision(ATDMA-R)을 고안하였다. 또한 시뮬레이션을 통해 ATDMA와 ATDMA-R의 성능을 비교하여 ATDMA 대비 ATMDMA-R이 다양한 실제적 환경에서 강인성을 보이는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권5호
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• pp.691-708
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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권9호
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.