• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.747-748
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• 2008

In this paper, we propose a new algorithm for task scheduling consisting of subtask partitioning and subtask priority scheduling steps in order to keep the peak power under the system specification. The subtask partitioning stepis performed to minimize the idle operation time for processors by dividing a task into multiple subtasks using the least square method developed with power consumption pattern of tasks. In the subtask priority scheduling step, a priority is assigned to a subtask based on the power requirement and the power variation of subtask so that the peak power violation can be minimized and the task can be completed within the execution time deadline.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.76-89
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• 2010

The performance of Distributed Heterogeneous Computing System depends on the algorithm which schedules input DAG graph. Among various scheduling algorithms, list scheduling algorithm provides superior performance with low complexity. List scheduling consists of task prioritizing phase and processor allocation phase, but most studies only focus on task prioritizing phase. In this paper, we propose LIP policy which has the same complexity with traditional allocation policies but has superior performance. The performance of LIP has been observed by applying them to task prioritizing phase of traditional list scheduling algorithms, HCPT, HEFT, GCA, and PETS. The results show that LIP has better performance than insertion-based policy and non-insertion-based policy, which are traditional processor allocation policies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2371-2377
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• 2010

As the substantial increment in program complexity and appearance of mega program, the programs need to be divided to small tasks with multiple partitions and performed with a priority based scheduling. And also, a program development has to be progressed according to diversify of development environment. For instance, there are some restrictions upon O/S environment such as embedded O/S or windows. Therefore, the adaptive scheduling technique which performs multiple task partitioning process, regardless environment or O/S, is suggested. In this study, In this study, the adaptive scheduling technique algorithm and its applied examples are described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5465-5480
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• 2018

Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.

• The KIPS Transactions:PartA
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• v.17A no.6
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• pp.259-264
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• 2010

In case of scheduling tasks with shared resources in multiprocessor systems, Global Earliest Deadline First (GEDF) algorithm, equally applied Earliest Deadline First (EDF) which runs scheduling with deadline criterion, makes schedulability decline because GEDF typically does not have a specific process in order to handle tasks with shared resources. In this paper, we propose Earliest Deadline First with Partitioning (EDFP) for tasks with shared resources which partitions a task into two kinds of subtasks that include critical sections to access to shared resources, gives their own deadline respectively and manages them. As a result of simulations, EDFP shows better performance than GEDF for tasks with shared resources since system load goes up and the number of processor increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1501-1518
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• 2016

This paper presents an effective management of VM (Virtual Machine) for heterogeneous cloud using Common Deployment Model (CDM) brokering mechanism. The effective utilization of VM is achieved by means of task scheduling with VM placement technique. The placements of VM for the physical machine are analyzed with respect to execution time of the task. The idle time of the VMis utilized productively in order to improve the performance. The VMs are also scheduled to maintain the state of the current VM after the task completion. CDM based algorithm maintains two directories namely Active Directory (AD) and Passive Directory (PD). These directories maintain VM with proper configuration mapping of the physical machines to perform two operations namely VM migration and VM roll back. VM migration operation is performed from AD to PD whereas VM roll back operation is performed from PD to AD. The main objectives of the proposed algorithm is to manage the VM's idle time effectively and to maximize the utilization of resources at the data center. The VM placement and VM scheduling algorithms are analyzed in various dimensions of the cloud and the results are compared with iCanCloud model.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.304-310
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• 2021

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.381-386
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• 2006

In this paper, we designed and implemented a web-based grid scheduling platform(WGridSP), which can model a system and simulate scheduling scheme in grid computing. WGridSP used GridSim, a grid scheduling toolkit in java-environment, as a tool for simulation and is able to Perform resource modeling, task modeling, algorithm compiling, simulation, and Performance evaluation rapidly in web environment. WGridSP can be applied as a foundation for grid research and can be used to analyze the efficiency of scheduling algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.26-36
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• 2000

This paper proposes a neural network algorithm for real-time multiprocessor scheduling problem. The proposed algorithm is developed base on Hopfield neural network for a benefit of parallel processing, in order to finish a requested task within a deadline time. To compare the performance of the proposed algorithm, we used EDA and LLA algorithm that has studied real-time multiprocessor scheduling before. The proposed algorithm is implemented hardware using VHDL.