• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.1-17
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• 2016

The separated management and operation of commercial IP/optical multilayer networks makes network operators look for a unified control plane (UCP) to reduce their capital and operational expenditure. Software-defined networking (SDN) provides a central control plane with a programmable mechanism, regarded as a promising UCP for future optical networks. The general control and scheduling mechanism in SDN-based optical burst switching (OBS) networks is insufficient so the controller has to process a large number of messages per second, resulting in low network resource utilization. In view of this, this paper presents the burst-flow scheduling mechanism (BFSM) with a proposed scheduling algorithm considering channel usage. The simulation results show that, compared with the general control and scheduling mechanism, BFSM provides higher resource utilization and controller performance for the SDN-based OBS network in terms of burst loss rate, the number of messages to which the controller responds, and the average latency of the controller to process a message.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.177-182
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• 2018

The home-and-way round-robin sports leagues scheduling problem with minimum brake is very hard to solve in polynomial time. This problem is NP-hard, the complexity status is not yet determined. This paper suggests round-robin sports leagues scheduling algorithm not computer-aided program but by hand with O(n) time complexity for arbitrary number of teams n with always same pattern. The algorithm makes a list of mathes using $n{\times}n$ canonical latin square for n=even teams. Then trying to get home(H) and away(A) with n-2 minimum number of brakes. Also, we get the n=odd scheduling with none brakes delete a team own maximum number of brakes from n=even scheduling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5800-5818
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• 2018

Interference may occur when several co-located wireless body area networks (WBANs) share the same channel simultaneously, which is compressed by resource scheduling generally. In this paper, a QoS-aware Adaptive Coloring (QAC) scheduling algorithm is proposed, which contains two components: interference sets determination and time slots assignment. The highlight of QAC is to determine the interference graph based on the relay scheme and adapted to the network QoS by multi-coloring approach. However, the frequent resource assignment brings in extra energy consumption and packet loss. Thus we come up with a launch condition for the QAC scheduling algorithm, that is if the interference duration is longer than a threshold predetermined, time slots rescheduling is activated. Furthermore, based on the relative distance and moving speed between WBANs, a prediction model for interference duration is proposed. The simulation results show that compared with the state-of-the-art approaches, the QAC scheduling algorithm has better performance in terms of network capacity, average delay and resource utility.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3201-3223
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• 2020

Wireless communication has become the promising technology in the recent times because of its applications in Internet of Things( IoT) devices. The IEEE 802.15.4e has become the key technology for IoT devices which utilizes the Time-Slotted Channel Hopping (TSCH) networks for the communication between the devices. In this paper, we develop a Two Level Scheduling Algorithm (TLSA) for scheduling multiple packets with different arrival rate at the source nodes in a TSCH networks based on the link activated by a centralized scheduler. TLSA is developed by considering three types of links in a network such as link i with packets arrival type 1, link j with packets arrival type 2, link k with packets arrival type 3. For the data packets arrival, two stages in a network is considered.At the first stage, the packets are considered to be of higher priority.At the second stage, the packets are considered to be of lower priority.We introduce level 1 schedule for the packets at stage 1 and level 2 schedule for the packets at stage 2 respectively. Finally, the TLSA is validated with the two different energy functions i.e., y = e^ax - 1 and y = 0.5x² using MATLAB 2017a software for the computation of average and worst ratios of the two levels.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.225-236
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• 2003

In this paper, we propose a wireless scheduling algorithm to provide the Internet delay proportional differentiated services in wireless networks. For considering network environments that have burst and location-dependent channel errors, our proposed WDPS(Wireless Delay Proportional Service) scheduling algorithm adaptively serves packets in class queues based on the delivered delay performance for each class. The remarkable characteristics of WDPS scheduler are supporting a fair relative delay service, providing graceful throughput and delay compensation, and avoiding class queue blocking problem. Through simulations, we show that the algorithm achieves the desirable properties to provide delay proportional services in wireless networks.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.154-162
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• 2001

In this paper, we investigate the scheduling algorithm of router system for Internet service based on the qualify-of-service (QoS) level of the input source traffics. We suggest an approprite scheduling algorithm in order to satisfy their QoS requirements for the loss-sensitive traffic and delay-sensitive traffic. For this purpose, we first study the service requirements of the multiplexer in Internet and the definition of QoS based on the ITU-T white recommendations. Second. we suggest a functional architecture of the multiplexer and the scheduling algorithm to satisfy various QoS requirements for Internet service. Finally. the performance measures of interest, namely steady-state packet loss probability and average delay, are discussed by simulation results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.37-42
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• 2021

NOMA (Non-orthogonal multiple Access) system has been focused on the next generation cellular system for higher spectral efficiency. However, this requires user scheduling as the NOMA system is a multi-user system which accesses simultaneously. There are two representative scheduling schemes, proportionate scheduling (FP) and cumulative distribution function (CFD) scheduling. The PF scheduling is applied, the cell edge user is hard to obtain a transmit opportunity. Recently, CDF scheduling is obviously noted that it offers the same possibility of transmission for a user regardless of the location in a cell. We consider an uplink NOMA system with CDF scheduling, and obtain the channel access probabilities, the outage probabilities of the system with different number of users and different kinds of weights through simulation. The results indicate that the likelihood of each user accessing the channel is the same and the probability of failure decreases as the number of users increases. We found that the effect of the probability of failure is negligible as the weight of the cell edge user increases.