• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.19-27
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• 2021

Tasks scheduling have been gaining attention in both industry and research. The scheduling that ensures independent task execution is critical in real-time systems. While task scheduling has gained a lot of attention in recent years, there have been few works that have been implemented into real-time architecture. The efficiency of the classical scheduling strategy in real-time systems, in particular, is still understudied. To reduce total waiting time, we apply three scheduling approaches in this paper: First In/First Out (FIFO), Shortest Execution Time (SET), and Shortest-Longest Execution Time (SLET). Experimental results have demonstrated the efficacy of the SLET in comparison with the others in most cases in a wide range of configurations.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.169-173
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• 2015

The linear scheduling method or line-of-balance (LOB) is a popular choice for projects that involve repetitive tasks during project execution. The method, however, produces deterministic schedule that does not convey a range of potential project outcomes under uncertainty. This results from the fact the basic scheduling parameters such as crew production rates are estimated to be deterministic based on single-point value inputs. The current linear scheduling technique, therefore, lacks the capability of reflecting the fluctuating nature of the project operation. In this paper the authors address the issue of how the variability of operation and production rates affects schedule outcomes and show a more realistic description of what might be a realistic picture of typical projects. The authors provide a solution by providing a more effective and comprehensive way of incorporating the crew performance variability using a Monte Carlo simulation technique. The simulation outcomes are discussed in terms of how this stochastic approach can overcome the shortcomings of the conventional linear scheduling technique and provide optimum schedule solutions.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1095-1098
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• 1993

This paper presents linear programming neural networks for job-shop scheduling. The starting times of tasks and constraints are formulated as the linear programming problem. A modified Hopfield neural network is proposed for solving job-shop scheduling.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.86-95
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• 2009

Efficient DAG scheduling is critical for achieving high performance in heterogeneous computing environments. Finding an optimal solution to the problem of scheduling an application modeled by a directed acyclic graph(DAG) onto a set of heterogeneous machines is known to be an NP-complete problem. In this paper we propose a new list scheduling algorithm, called the Heterogeneous Rank-Path Scheduling(HRPS) algorithm, to exploit all of a program's available parallelism in distributed heterogeneous computing system. The primary goal of HRPS is to minimize the schedule length of applications. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling algorithm such as CPOP, HCPT and FLB in term of the schedule length. The comparison studies show that HRPS significantly outperform CPOP, HCPT and FLB in schedule length.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.48-57
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• 2010

A comparison of two kinds of scheduling optimization algorithms is presented in this paper. As satellite control and operation techniques have been developed, satellite missions became more complicated and overall quantity of missions also increased. These changes require more specific consideration and a huge amount of computation for the satellite mission scheduling. Therefore, it is a good strategy to make a scheduling optimization algorithm for the efficient satellite mission scheduling operation. In this paper, two kinds of scheduling optimization algorithms are designed with tabu-search algorithm and genetic algorithm respectively. These algorithms are applied for the same mission scenario and the results of each algorithm are compared and analyzed.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1478-1490
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• 2011

Recently, multi-core processors have been drawing significant interest from the embedded systems research and industry communities due mainly to their potential for achieving high performance and fault-tolerance at low cost in such products as automobiles and cell phones. To process multimedia data, a scheduling algorithm is required to meet timing constraints of periodic tasks in the system. Though Pfair scheduling algorithm can meet all the timing constraints while achieving 100% utilization on multi-core based system theoretically, however, the algorithm incurs high scheduling overheads including frequent core migrations and system-wide synchronizations. To mitigate the problems, we propose a real-time scheduling algorithm for multi-core based system so that system-wide scheduling is performed only when it is absolutely necessary. Otherwise the proposed algorithm performs scheduling within each core independently. The experimental results by extensive simulations show that the proposed algorithm dramatically reduces the scheduling overheads up to as negligible one when the utilization is under 80%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.12-23
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• 2007

The purpose of this studγ if to understand problem of present bus scheduling system and to develop optimal bus scheduling model which improve bus passenger's degree of satisfaction(DOS) and bus company's operation efficiency at the same time. This study developed optimal bus scheduling model, which reflected bus passenger's degree of satisfaction(DOS), applied to existing model that summery of bus operation cost($C_o$), passenger queuing time cost($C_{pw}$) and passenger travel time cost($C_{pl}$). And optimal bus scheduling model which developed in this study is optimized that using LINGO program based on linear program. Also by using the general case in Busan, compare total cost of present bus scheduling system and existing scheduling model with total cost of optimal bus scheduling model which reflected bus passenger's degree of satisfaction(DOS).

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.4
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• pp.369-375
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• 2001

This paper proposes a dual real-time scheduling design under real-time constraints kernel environments. In this paper, we have designed both the real-time kernel and the general kernel that have their different properties to satisfy these properties, that is, interrupt latency, scheduling precision, and message passing. In real-time tasks, interrupt processing should be run. In general kernel, non real-time tasks or general tasks are run. Also, when tasks conflict, it executed the mixed priority scheduling that non real-time kernel executed static scheduling and real-time kernel executed dynamic priority transformation scheduling, that is, least-laxity-first/minimization preemption scheduling. We have compared the results of this study for performance of the proposal real-time kernel with both RT Linux 0.5a and QNX 4.23A, that is, of interrupt latency scheduling precision and message passing.