• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.11-21
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• 2006

Importance of and need for grid resource management have accelerated in accordance with increasing development of grid computing. However, it is very complex to distribute and utilize resources efficiently in geographically dispersed environments. This is due to the different access policies and constraints of grid resource owners. Users request resources according to their needs. Operators of a grid computing system need to be able to monitor the system states for reflecting these demands. So, a grid computing system needs a resource management policy that monitors states of resources and then allocates resources. This paper proposes a user demand-based grid trade management model that provides an efficient resource management by the trade allocation based on a users' demand and providers' supply strategy. To evaluate performance, this paper measures increasing rate of resource trades, average response time of trades, and processing time utilization. Firstly, the average increasing rates of trade are 585.7% and 322.6% higher than an auction model and a double auction model. Secondly, the average response time of the user demand-based grid trade management model is maintained between 3 and 5 simulation time. Finally, it is found that the processing time utilization is an average of 145.4% and 118.0% higher than an auction model and a double auction model. These empirical results demonstrate the usefulness of the user demand-based grid trade management model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3096-3117
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• 2013

To execute the performance driven Grid applications, an effective and scalable workflow scheduling is seen as an essential. To optimize cost & makespan, in this paper, we propose a Scalable Cost-Time Trade-off (SCTT) model for scheduling workflow tasks. We have developed a heuristic algorithm known as Scalable Cost-Time Trade-off Scheduling (SCTTS) with a lower runtime complexity based on the proposed SCTT model. We have compared the performance of our proposed approach with other heuristic and meta-heuristic based scheduling strategies using simulations. The results show that the proposed approach improves performance and scalability with different workflow sizes, task parallelism and heterogeneous resources. This method, therefore, outperforms other methods.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.