• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.1-8
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• 2016

This paper proposes a novel broadcast scheduling algorithm for wireless large-scale networks based on theory of complex networks. In the proposed algorithm, the network topology is formed based on a scale-free network and the probability of link distribution is analyzed. In this paper, the characteristics of complex systems are analyzed (which are not concerned by the existing broadcast scheduling algorithm techniques) and the optimization of network transmission efficiency and network time delay are provided.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.59-67
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• 2008

New system solves scheduling problem in medical services. The existing scheduling system by FCFS is for a hospital but new system expresses the condition of a hospital and the profit of separate system's parties by the automated negotiation. This system expresses user preference time as a priority number and is designed that the schedule is changed according to the priority number with a negotiation strategy for a patient. And a hospital makes a schedule according to the policy. The system supports customer satisfaction in medical services by considering not only equipment efficiency but also the patent waiting time unlike the existing system considering the idle time for a equipment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1302-1320
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• 2015

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. hus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed ork is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper roposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level ontains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to itigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will ynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is roposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM rchitecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job iolations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance f AMSS is better than other algorithms.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1559-1566
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• 2017

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.237-252
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• 2017

In recent years, data centers have become the core infrastructure to deal with big data processing. For these big data applications, network transmission has become one of the most important factors affecting the performance. In order to improve network utilization and reduce job completion time, in this paper, by real-time monitoring from the application layer, we propose job-aware priority scheduling. Our approach takes the correlations of flows in the same job into account, and flows in the same job are assigned the same priority. Therefore, we expect that flows in the same job finish their transmissions at about the same time, avoiding lagging flows. To achieve load balancing, two approaches (Flow-based and Spray) using ECMP (Equal-Cost multi-path routing) are presented. We implemented our scheme using NS-2 simulator. In our evaluations, we emulate real network environment by setting background traffic, scheduling delay and link failures. The experimental results show that our approach can enhance the Hadoop job execution efficiency of the shuffle stage, significantly reduce the network transmission time of the highest priority job.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4419-4435
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• 2015

The use of an efficient packet scheduling scheme for a forward link in satellite communication networks is very important to support fairness for each return channel satellite terminal (RCST) and the service differentiations for user traffics. To support fairness and QoS for each RCST with service-level agreement (SLA), the Adaptive Coding and Modulation (ACM) system in a satellite hub has to process packets with considering modulation and coding (MODCOD) and packet types. Although a DVB-S2 system with ACM scheme has higher transmission efficiency, it cannot offer fairness or quality of service (QoS) to RCSTs. Because the data are transmitted with high MODCOD in regions with clear skies, while data are transmitted using low MODCOD in regions experiencing rain events. In this paper, we propose a two-step scheduling scheme offering fairness and QoS to RCSTs, while minimizing a decrease in throughput. The proposed scheme is carried out performance evaluations using a computer simulation. As results of this simulation, the proposed scheduler was shown to support bandwidth fairness to an individual RCST, and provide a level of QoS differentiation for user traffics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3594-3610
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• 2015

Recent advancements in broadband wireless communication technologies enable mobile users to receive video streaming services with various smart devices. The long term evolution (LTE) network provides high bandwidth and low latency for several emerging mobile applications. This paper proposes the buffer aware scheduling (BAS) approach to schedule the downlink video traffic in LTE network. The proposed BAS scheme applies the weighting function to heuristically adjust the scheduling priority by considering the buffer status and channel condition of UE so as to reduce the time that UE stays in the connected state without receiving data. Both of 1080P and 2160P resolution video streaming sources were applied for exhaustive simulations to examine the performance of the proposed scheme by comparing to that of the fair bandwidth (FB) and the best channel quality indicator (CQI) schemes. The simulation results indicate that the proposed BAS scheme not only achieves better performance in power saving, streaming delivery time, and throughput than the FB scheme while maintaining the similar performance as the best CQI scheme in light traffic load. Specifically, the proposed scheme reduces streaming delivery time and generates less signaling overhead than the best CQI scheme when the traffic load is heavy.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.1-9
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• 2011

Wireless mobile networks that exploit generalized PF scheduling can dynamically allocate network resources by using scheduling parameters. There are limitations to predict throughputs by the conventional stochastic approach in general. Moreover the limitations make it difficult to find appropriate scheduling parameters for achieving the demanded throughputs. This paper derives a prediction algorithm that predicts throughputs of the networks by using deterministic approach. A throughput adjust algorithm and a throughput switching algorithm are derived from the prediction algorithm. The performance of the throughput prediction/switching algorithms is evaluated by a simulator based on IEEE 802.16m system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.15-16
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• 2018

본 논문에서는 다수의 센서 노드로 구성된 Internet of Things (IoT) 환경에서 새로운 환경에 대해 적응하는데 걸리는 시간을 줄이기 위한 새로운 스케줄링 기법을 제안한다. IoT 환경에서는 데이터 수집 및 전송 패턴이 사전에 정의되어 있지 않기 때문에 기존 정적인 Packet scheduling 기법으로는 한계가 있다. Q-learning은 네트워크 환경에 대한 사전지식 없이도 반복적 학습을 통해 Scheduling policy를 확립할 수 있다. 본 논문에서는 기존 Q-learning 스케줄링 기법을 기반으로 각 큐의 패킷 도착률에 대한 bound 값을 이용해 Q-table과 Reward table을 초기화 하는 새로운 Q-learning 스케줄링 기법을 제안한다. 시뮬레이션 결과 기존 기법에 비해 변화하는 패킷 도착률 및 서비스 요구조건에 적응하는데 걸리는 시간이 감소하였다.