• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.99-109
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• 2007

IEEE 802.lie is being proposed to improve QoS by IEEE 802.11 working group. HCCA (HCF Controlled Channel Access) a centralized polling based mechanism of IEEE 802.11e, needs a scheduling algorithm that decides on how the available radio resources are allocated to the polled STAs. In IEEE 802.l1e standard Reference Scheduler is presented. Reference Scheduler Polls all STAs in a polling list by the same interval that causes ineffectively frequent polling. It increases not only the overhead but it decreases the TXOP (Transmission Opportunity) utilization. In this paper, we propose the scheduling and admission control algorithm that poll stations depending on the MSI (Maximum Service Interval)o( stations to solve these shortcomings. In our proposed algorithm a station is polled by an interval close to its MSI, so polling overhead decrease and TXOP utilization increases than Reference Scheduler. Simulation results show that our algorithm outperforms Reference Scheduler. Our algorithm maintains higher aggregate throughput and services mere stations than Reference Scheduler.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.94-102
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• 2010

In recent years, embedded systems such as cellular phones, Portable Multimedia Player, intelligent appliance, automobile engine control are reshaping the way people live, work, and play. Thereby, services application to guarantee various requirements of users become increasingly sophisticated and complicated, such embedded computing platforms use real-time operating systems (RTOSs) with time determinism. These RTOSs must not only provide predictable services but must also be efficient and small in size. Kernel services should also be deterministic by specifying how long each service call will take to execute. Having this information allows the application designers to better plan their real-time application software so as not to miss the deadline of each task. In this paper, we present the complete generalized real-time scheduling algorithm using multi-dimensional methodology to determine the highest priority in the ready list with 2r levels of priorities in a constant time without additional memory overhead.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.99-110
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• 2019

As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.293-307
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• 2004

We consider the problem of scheduling tasks of a precedence constrained task graph, where each task has its execution time and deadline, onto a set of identical processors in a way that simultaneously minimizes the number of processors required and the total tardiness of tasks. Most existing approaches tend to focus on the minimization of the total tardiness of tasks. In another methods, solutions to this problem are usually computed by combining the two objectives into a simple criterion to be optimized. In this paper, the minimization is carried out using a multiobjective genetic algorithm (GA) that independently considers both criteria by using a vector-valued cost function. We present various GA components that are well suited to the problem of task scheduling, such as a non-trivial encoding strategy. a domination-based selection operator, and a heuristic crossover operator We also provide three local improvement heuristics that facilitate the fast convergence of GA's. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific genetic algorithm, the Performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.116-126
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• 2015

In a distributed heterogeneous computing system, the performance of a parallel application greatly depends on its task scheduling algorithm. Therefore, in order to improve the performance, it is essential to consider some factors that can have effect on the performance of the parallel application in a given environment. One of the most important factors that affects the total execution time is a critical path. In this paper, we propose the CLTS algorithm for a task scheduling. The CLTS sets the priorities of all nodes to improve overall performance by applying leveling method to improve parallelism of task execution and by reducing the delay caused by waiting for execution of critical nodes in priority phase. After that, it conditionally uses insertion based policy or duplication based policy in processor allocation phase to reduce total schedule time. To evaluate the performance of the CLTS, we compared the CLTS with the DCPD and the HCPFD in our simulation. The results of the simulations show that the CLTS is better than the HCPFD by 7.29% and the DCPD by 8.93%. with respect to the average SLR, and also better than the HCPFD by 9.21% and the DCPD by 7.66% with respect to the average speedup.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.1
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• pp.6-11
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• 2009

This paper presents a new dynamically adaptable polling scheme for efficient support of real-time traffic over an IEEE 802.11 wireless LAN network. The poll scheduling plays an important role in IEEE 802.11 PCF. However, the current version of the polling list management scheme proposed in the IEEE 802.11 standard is inefficient when a variable number of mobile stations have variable packets to transmit. If Point Coordinator has an exact information on the station status, it is possible to efficiently perform polling. In this paper, we suggest an adaptable polling scheme to meet requirements of the stations. In our scheme, each station transmits packets including a piggyback information to inform that it wants to receive a poll in the next polling duration. Simulation results indicate that our scheme may reduces the packet discard ratio and real-time packet transfer delay.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.261-268
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• 1996

Hardware-software codesign becomes improtant to effectively sagisfy perfomrance goals, because designers can trade-off in the way hardware and software components work teogether to exhibit a specified behavior. In this paper, a hardware-software pratitioning algorithm is presetned, in which the system behavioral description containing a mixture of hardware and software components is partitioned into hardware part and software part. The partitioning algorithm tries to minimize the given cost function under constraints on hardware resources or latency. Recursive moving of operations between the hardware and software parts is used to find a near optimum partition and the list scheduling approach is used to estimate the hardware area and latency. Since memory may take substantial protion of the hardware part, memory cost is included in sthe hardware cost. Experimental resutls show that our algorithm is effective.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.174-176
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• 1997

This paper proposes a unit commitment scheduling method based on hybrid genetic algorithm(GA). When the systems are scaled up, conventional genetic algorithms suffer from computational time limitations because of the growth of the search space. So greatly reduce the search space of the GA and to efficiently deal with the constraints of the problem, priority list unit ordering scheme are incorporated as the initial solution and the minimum up and down time constraints of the units are included. The violations of other constraints are handled by integrating penalty factors. To show the effectiveness of the proposed method. test results for system of 10 units is compared with results obtained using other methods.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.572-574
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• 2000

리스트 기반 스케줄링은 하드웨어의 자원이 일정한 수준으로 제한된 상황에서 스케줄링에 필요한 컨트롤 스텝의 수를 최소화하고자 하는데 중점을 둔 일련의 스케줄링 기법 중 하나로, 연산의 우선 순위를 결정한 뒤, 그 우선 순위에 의거하여 준비된 일단의 연산들에 대한 스케줄링을 수행하는 방식이다. 따라서 연산의 우선 순위를 결정하는데 고려되는 조건들은 리스트 기반 스케줄링의 성능에 직접적인 영향을 주며, 현재까지 다양한 우선 순위 결정 조건들이 제시되어 있다. 그런데, 최종 합성 결과는 상위 수준 합성의 대상이 되는 입력 그래프의 특성에 따라 그 성능이 좌우되므로 일반적인 의미에서 최적의 우선 순위 결정 조건이란 존재하지 않는다. 본 논문에서는 단일한 우선 순위 조건하에서 경합하는 연산들의 우선 순위 결정 조건이란 존재하지 않는다. 본 논문에서는 단일한 우선 순위 조건하에서 경합하는 연산들의 우선 순위 결정시 다양한 우선 순위 결정 조건들을 균형있게 고려하여 보다 효율적인 스케줄링 결과를 얻는 방법을 제시하였다.