• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.

• International Journal of Contents
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• 제6권3호
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• pp.19-24
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• 2010

Task scheduling is an integrated component of computing with the emergence of grid computing. In this paper, we address two different task scheduling models, which are static Round-Robin (RR) and dynamic Fastest Site First (FSF) task scheduling method, using extended timed marked graphs, which is a special case of Stochastic Petri Nets (SPN). Stochastic reward nets (SRN) is an extension of SPN and provides compact modeling facilities for system analysis. We build hierarchical SRN models to compare two task scheduling methods. The upper level model simulates task scheduling and the lower level model implements task serving process for different sites with multiple servers. We compare these two models and analyze their performances by giving reward measures in SRN.

• 대한전자공학회논문지SD
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• 제46권7호
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• pp.22-28
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• 2009

멀티프로세서가 임베디드 시스템에서 널리 쓰임에 따라 지원되는 전력 최소화 기법을 이용하여 태스크를 수행하기 위해 필요한 에너지의 소모량을 줄여야 할 필요성이 대두된다. 본 논문은 동적 전압 스케일링 및 전력 셧다운을 이용하여 에너지 소모를 최소화 하는 태스크 스케줄링 알고리즘을 멀티프로세서 환경을 위해 제안하였다. 제안된 알고리즘에서는 전력 셧다운시의 에너지 및 타이밍 오버헤드를 고려하여 반복적으로 태스크 할당 및 태스크 순서화를 수행한다. 제안된 반복적인 태스크 스케줄링을 통해 전체 에너지 소모를 줄이는 가장 좋은 해를 얻을 수 있었다. 전체 에너지 소모는 리니어 프로그래밍 모델 및 전력 셧다운의 임계 시간을 고려하여 계산되었다. 실제 어플리케이션으로부터 추출된 표준 태스크 그래프에 기반을 둔 실험 결과를 통해 하드웨어 자원 및 시간제한에 따른 에너지 소모 관계를 분석하였다. 실험 결과를 볼 때 제안된 알고리즘은 기존의 우선권 기반의 태스크 스케줄링에 대해서 의미 있는 성능 향상을 얻을 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.987-992
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• 2016

This study introduces an example environment where wireless devices are mobile, devices use dynamic voltage scaling, devices and tasks are heterogeneous, tasks have deadline, and the computation and communication power is dynamically changed for energy saving. For this type of environment, the efficient system-level energy management and resource management for task completion can be an essential part of the operation and design of such systems. Therefore, the resources are assigned to tasks and the tasks may be scheduled to maximize a goal which is to minimize energy usage while trying to complete as many tasks as possible by their deadlines. This paper also introduces mobility of nodes and variable transmission power for communication which complicates the resource management/task scheduling problem further.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of Computing Science and Engineering
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• 제7권1호
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• pp.30-43
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• 2013

Multicore and multiprocessor systems with dynamic voltage scaling architectures are being used as one of the solutions to satisfy the growing needs of high performance applications with low power constraints. An important aspect that has propelled this solution is effective task/application scheduling and mapping algorithms for multiprocessor systems. This work proposes an energy aware, offline, probability-based unified scheduling and mapping algorithm for multiprocessor systems, to minimize the number of processors used, maximize the utilization of the processors, and optimize the energy consumption of the multiprocessor system. The proposed algorithm is implemented, simulated and evaluated with synthetic task graphs, and compared with classical scheduling algorithms for the number of processors required, utilization of processors, and energy consumed by the processors for execution of the application task graphs.

• 한국통신학회논문지
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• 제33권3C호
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• pp.219-233
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• 2008

스케줄링의 목적은 입력 작업(DAG)에 대한 스케줄 결과 길이를 최소화하는 것이다. 이런 스케줄링 문제는 잘 알려진 '정해진 시간 내에 해결하기 어려운 문제(NP-complete)'이며 최적의 스케줄링 결과 값을 얻기 위해서는 휴리스틱으로 해결해야 한다. 선후 관계의 제약을 갖는 노드들의 스케줄링을 효율적으로 수행하기 위해 부모 노드와 이질 프로세서에 대한 정보를 고려하는 TANH(the Task duplication based scheduling Algorithm for Network of Heterogeneous systems), GDL, BIL, TDS과 같은 많은 알고리즘이 제안되었다. 본 논문은 기존의 TANH 스케줄링에서 나타나는 여러 개의 부모 노드와 이질 프로세서에 대한 다양한 경우를 충분히 고려하지 못한 점을 보안하여 향상된 스케줄링을 수행할 수 있는 DTSC (Duplication based Task Scheduling with Communication Cost in Heterogeneous Systems)알고리즘을 제안하였다. 제안된 알고리즘의 성능은 기존 TANH, GDL 알고리즘과 비교하였으며, 스케줄링의 성능 향상을 보여 주었다.

• 한국통신학회논문지
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• 제34권6B호
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• pp.641-647
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• 2009

본 논문은 임베디드 시스템에서 불필요한 전력 소모를 감소하기 위해 개선된 예측 동적 전력 관리 구조와 태스크 스케줄링 알고리듬을 제안한다. 제안된 알고리듬은 불필요한 전력 소모를 최소화하기 위해 미리 스케줄링을 한다. 제안된 예측 동적 전력 관리는 수행 오버 헤드를 경감하기 위해서 스케줄링 라이브러리를 제공한다. 실험 결과 제안된 알고리듬은 동적 전력 관리를 적용한 LLF 알고리듬과 비교하여 평균 22.3% 전력 소모 감소를 보인다.

• Journal of Information Processing Systems
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• 제7권4호
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• pp.707-716
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• 2011

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.