• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1423-1435
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• 2010

Building wireless sensor networks requires a constituting sensor node to consider the following limited hardware resources: a small battery lifetime limiting available power supply for the sensor node, a low-power microprocessor with a low-performance computing capability, and scarce memory resources. Despite such limited hardware resources of the sensor node, the sensor node platform needs to activate real-time sensing, guarantee the real-time processing of sensing data, and exchange data between individual sensor nodes concurrently. Therefore, in this paper, we propose an energy-efficient real-time task scheduling technique for battery-powered wireless sensor nodes. The proposed energy-efficient task scheduling technique controls the microprocessor's operating frequency and reduces the power consumption of a task by exploiting the slack time of the task when the actual execution time of the task can be less than its worst case execution time. The outcomes from experiments showed that the proposed scheduling technique yielded efficient performance in terms of guaranteeing the completion of real-time tasks within their deadlines and aiming to provide low power consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3876-3895
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• 2014

Recently, Heterogeneous Network (HetNet) with Coordinated Multi-Point (CoMP) scheme is introduced into Long Term Evolution-Advanced (LTE-A) systems to improve digital services for User Equipments (UEs), especially for cell-edge UEs. However, Radio Resource Management (RRM), including Resource Block (RB) scheduling and Power Allocation (PA), in this scenario becomes challenging, due to the intercell cooperation. In this paper, we investigate the RRM problem for downlink transmission of HetNet system with Joint Processing (JP) CoMP (both joint transmission and dynamic cell selection schemes), aiming at maximizing weighted sum data rate under the constraints of both transmission power and backhaul capacity. First, joint RB scheduling and PA problem is formulated as a constrained Mixed Integer Programming (MIP) which is NP-hard. To simplify the formulation problem, we decompose it into two problems of RB scheduling and PA. For RB scheduling, we propose an algorithm with less computational complexity to achieve a suboptimal solution. Then, according to the obtained scheduling results, we present an iterative Karush-Kuhn-Tucker (KKT) method to solve the PA problem. Extensive simulations are conducted to verify the effectiveness and efficiency of the proposed algorithms. Two kinds of JP CoMP schemes are compared with a non-CoMP greedy scheme (max capacity scheme). Simulation results prove that the CoMP schemes with the proposed RRM algorithms dramatically enhance data rate of cell-edge UEs, thereby improving UEs' fairness of data rate. Also, it is shown that the proposed PA algorithms can decrease power consumption of transmission antennas without loss of transmission performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1704-1712
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• 2014

The unused Worst-Case Execution Time (WCET) allocated to a real-time task occurs when the actual execution time of the task can be far less than the WCET preassigned to the task for a schedulability test. Any unused WCET allocated to the task can be exploited to reduce the power consumption of battery-powered sensor nodes through real-time power-aware scheduling techniques. From the distribution perspective of the unused WCET, the unused WCET distribution policy is classified into three types: Conservative Unused WCET (CU-WCET), Moderate Unused WCET (MU-WCET), and Aggressive Unused WCET (AU-WCET) distribution policies. We evaluated the performance of real-time power-aware scheduling techniques incorporating each of three unused WCET distribution policies in terms of low power consumption.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3358-3365
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• 2012

This paper presents a scheduling scheme for die casting processes considering power usage. The scheduling problem of a shift-based die casting process is represented by a linear programming (LP) model that maximizes the average efficiency of melting furnaces in regard of the usage of molten alloy, where the product quantities of each shift are used as primary variables. In this research, we propose a novel LP model that considers power usage of foundries. The developed LP model can derive product plans in which the expended power of a casting shift does not exceed a prescribed limit, while optimizing the efficiency of alloy usage. The simulation result of a case study demonstrates the superiority and applicability of the proposed scheme. This paper serves as a basic research on the role of foundries as an intelligent costumer in smart grid environment where the limit of power usage should be fulfilled.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.234-240
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• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.3
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• pp.93-100
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• 2004

Test scheduling of SoC (System-on-a-chip) is very important because it is one of the prime methods to minimize the testing time under limited power consumption of SoC. In this paper, a heuristic algorithm, in which test resources are selected for groups and arranged based on the size of product of power dissipation and test time together with total power consumption in core-based SoC is proposed. We select test resource groups which has maximum power consumption but does not exceed the constrained power consumption and make the testing time slot of resources in the test resource group to be aligned at the initial position in test space to minimize the idling test time of test resources. The efficiency of proposed algorithm is confirmed by experiment using ITC02 benchmarks.

• The KSFM Journal of Fluid Machinery
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• v.15 no.2
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• pp.36-40
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• 2012

Pitch control of wind turbine is activated above rated wind speed for the purpose of rated power regulation. When we design pitch controller, its gain-scheduling is essential due to nonlinear characteristics of aerodynamic torque. In this study, 2-mass model including a vibration mode of drive-train for a 2 MW wind turbine is considered and pitch control with gain-scheduling using a linearization analysis of the nonlinear aerodynamic torque is applied. Some simulation results for the pitch gain-scheduling under step wind speed are presented and investigated. It is shown that gain-scheduling in pitch control is important especially in the region of high wind speeds when there exists a vibration mode of drive-train.

• Journal of Computing Science and Engineering
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• v.7 no.1
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.1
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• pp.1-8
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• 2011

Efficient power management plays a crucial role to strengthen competitiveness in the market of portable mobile commodities. This paper presents a proactive power management technique, called by Energy-Aware Scheduling policY (EASY), to exploit the sleep time information of running applications. Different from previous power management approaches focusing on power conservation in standby mode, the proposed scheme characterizes each application program's operational characteristic in active mode by observing how long the task stays in sleep state of CPU scheduler. Based on the measured sleep time, the proposed EASY speculates an adequate CPU clock frequency according to the current CPU workload and scales the frequency directly to the predicted one. Experimental results show that the proposed scheme reduces the power consumption by 10-30% on average compared to traditional DPM approach, with a minimal impact on the performance overhead.