• 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%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.201-212
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• 2002

This paper proposes a reconfigurable scheduler model that can support various real-time scheduling algorithms. The proposed model consists of two hierarchical upper and lower components, task scheduler and scheduling framework, respectively. The scheduling framework provides a job dispatcher and software timers. The task scheduler implements an appropriate scheduling algorithm, which supports a specific real-time application, based on the scheduling framework. If system developers observe internal kernel interfaces to communicate between two hierarchical components, they can implement a new scheduling algorithm independent of complex low kernel mechanism. Once a task scheduler is developed, it can be reused in a new real-time system in future. In Real-Time Linux (5), we implemented the proposed scheduling framework and several representative real-time scheduling algorithms. Throughout these implementations, we confirmed that a new scheduling algorithm could be developed independently without updates of complex low kernel modules. In order to confirm efficiency of the proposed model, we measured the performance of representative task schedulers. The results showed that the scheduling overhead of proposed model, which has two separated components, is similar to that of a classic monolithic kernel scheduler.

• The KIPS Transactions:PartA
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• v.18A no.4
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• pp.143-150
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• 2011

This paper presents an EDF based scheduling algorithm for scheduling imprecise computation model where each task consists of mandatory part and optional part. Imprecise computation is useful to manage overload condition. In overload situation, some optional parts should be removed. The proposed DOP algorithm removes optional parts of earlier deadline tasks to enhance flexibly for newly arriving tasks. A simulation result shows that DOP has better performance than other algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.48-61
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• 2017

An IaaS service of a cloud computing environment makes itself attractive for running large scale parallel application thanks to its innate characteristics that a user can utilize a desired number of high performance virtual machines without maintenance cost. The total execution time of a parallel application on a high performance computing environment depends on a task scheduling algorithm. Most studies on task scheduling algorithms on cloud computing environment try to reduce a user cost, and studies on task scheduling algorithms that try to reduce total execution time are rarely carried out. In this paper, we propose a task scheduling algorithm called an HAGD and a performance enhancement method called a group task duplication method of which the HAGD utilizes. The group task duplication method simplifies previous task duplication method, and the HAGD uses the group task duplication method or a task insertion method according to the characteristics of a computing environment and an application. We found that the proposed algorithm provides superior performance regardless of the characteristics in terms of normalized total execution time through performance evaluations.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.62-68
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• 2005

This paper presents a scheme to enhance fixed priority scheduling algorithms on multiprocessors. This scheme gives the highest priority to jobs with zero laxity and schedules them Prior to other jobs. A fixed priority algorithm employing this scheme strictly dominates the original one; it can schedule all task sets schedulable by the fixed priority algorithm and some task sets not schedulable by the fixed priority algorithm. Simulation results show that the proposed scheme improves fixed priority algorithms in terms of the number of schedulable task sets and schedulable utilization bound.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.49-56
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• 2007

Grid provides a platform for the efficient execution of large-scale computing in science and engineering. In this environment, resource management and task scheduling are complex undertaking. We designed and implemented a web-based scheduling toolkit(GridTool), which can model a system and simulate scheduling scheme in Grid computing. The GridTool used GridSim, a toolkit in java-environment, as a tool for simulation. The GridTool is able to perform resource modeling, task modeling, algorithm compiling, simulation, and performance evaluation efficiently in web environment. The GridTool can be applied as a platform for Grid research and can be used to analyze the efficiency of scheduling algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.499-502
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• 2010

Because of the substantial increase in program complexity and appearance of mega program, the needs to devide the program into small task with multiple partitions, and perform a scheduling based on the priority is required. And also, a program can be developed on specific environment according to the diversify of development environment. for instance, there are some restrictions upon O/S environment such as Embedded or Windows. therefore, the adaptive scheduling technique which perform multiple task partitioning process regardless environment or O/S is suggested. In this study, Adaptive scheduling technique algorithm and its application to be described.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.103-110
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• 2010

Due to the increasing amount of electronic control system in a vehicle, the automotive software is increasingly sophisticated and complicated. Therefore it may be faced a time critical problem caused by its complexity. In order to solve such problems, the automotive electronic system can use a real-time scheduling mechanism based on predictability. We first consider the standard specification of the AUTOSAR OS and uC/OS-II such as its scheduling theory with time determinism. In this paper, we propose the scheduling algorithm to be conformable to a conformance class of OSEK/VDX specification. Algorithm analysis shows that our scheduling algorithm outperforms an existing Trampoline OS by intuition.