• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.71-77
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• 2015

In this paper, we propose a scheduling method for signal measurement and control algorithm execution in a multi-motor drive controller. The multi-motor controller which is used for vehicle control receives position/velocity command and performs position/velocity control and current control. Internal resource allocation and control algorithm execution timing are very important when one microcontroller is used for multi-motor drives. The control performance of the velocity control system is verified by varying ADC(Analog to Digital Converter) conversion timing and algorithm execution timing using real experiments.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.133-138
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• 2014

Packet arrival-delay variation, so-called 'jitter' is one of the main factors that degrade the quality of voice in mobile devices at the Voice over Internet Protocol (VoIP). To resolve this issue, a playout scheduling based on adaptive jitter estimation for enhancing VoIP speech quality is proposed. The proposed algorithm copes with the effect of transmission jitter by expanding or compressing each packet according to the predicted network delay and variations. Additionally, the active network jitter estimation incorporates rapid detection of delay spikes and reacts to changes in network conditions. The experimental results have shown that the proposed algorithm delivers high voice quality in unstable network environment.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.1-5
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• 2015

In this study, we consider the transportation problem in port container terminals. It aims to determine positions in yards to place the containers at the adequate times. The containers on ship must be unloaded one by one from top to bottom, and placed in the main yard in order to reduce additional cost required for unnecessary unloading when getting out by customer with given timetable. The cost for transportation at container terminals could be reduced by a new approach in scheduling: move the containers from ship and stack them onto main yard that minimizes cost of yard crane operation when unloading for customer.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.217-222
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• 2009

This paper proposes the number of Optical Network Unit (ONU) based priority scheduling mechanism over Ethernet Passive Optical Network (EPON) to support multicast Quality of Service (QoS) for Internet Protocol Television (IPTV) service. Multicast QoS is effected by the receivers' number of a packet because multicast efficiency is determined by how many receivers are received multiple copied packets. Therefore, the proposed mechanism assigns a priority with the number of ONUs to allocate high priority to IPTV services used by many people and firstly transmits a packet with high priority. By doing so, we show that the proposed mechanism support favorite IPTV services with better and stable QoS in spite of congestion.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.61-69
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• 2021

On-demand broadcast is an effective data dissemination technique in mobile computing environments. This paper explores the issues for scheduling multi-data queries in on-demand broadcast environments, and proposes a new broadcast scheduling scheme named CASS. The proposed scheme prioritizes queries by reflecting the characteristics of multi-data queries, and selects the data that has not been broadcast in the query for the longest time as the broadcast data according to the query priority. Simulation is performed to evaluate the performance of CASS. The simulation results show that the proposed scheme outperforms other schemes in terms of the average response time since it can show highly desirable characteristics in the aspects of query data adjacency and data conflict rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.103-109
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• 2019

The automotive electronic system should provide a method that can be safely performed by loading a number of application programs having time constraints in several electronic control devices. In this paper, we propose a timing protection mechanism for AUTOSAR, which is a real - time operating system specification for automotive field, in order to observe the deadline of each task when scheduling real - time tasks. We propose a dynamic non-preemption algorithm to guarantee a flexible deadline for fixed priority or dynamic priority tasks, and a location where execution time can be monitored for errors, and suggest ways to implement the AUTOSAR time protection mechanism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2060-2073
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• 2022

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.