• Journal of Energy Engineering
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• v.6 no.1
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• pp.11-18
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• 1997

This paper describes a daily operation scheduling of cogeneration systems using Genetic Algorithm. The simulation was performed in the case of bottoming cycle. The efficiency of cogeneration system which has nonlinear characteristic is obtained by the least square method based on the real data of industrial cogeneration system. In this paper, Genetic Algorithm is coded as a vector of floating point representation which can reduce computation time and obtain high precision The simulated results show that the genetic algorithm can be efficiently applied to establish the operation scheduling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1544-1572
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• 2011

Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.413-420
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• 2008

Network bandwidth is one of the major factors that impact the cost of a video service. In this paper we propose approaches to reducing the bandwidth requirement for transporting MPEG-4 video traffic over wireless LANs while guaranteeing a required level of quality of service(QoS). To support high quality video playbacks, video frames must be transported to the client prior to their playback times. A real-time transmission scheduling is used for this purpose, which transmits each frame assigned with a priority according to its importance. It addresses the challenge for a scheduling algorithm that efficiently handles the changing workloads of MPEG-4 video traffic. The goal of our research is to maximize the number of frames that are transported within their deadlines while minimizing the tardiness of frames that missed their deadlines. The performance of the proposed method is compared with that of similar service mechanisms through extensive simulation experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1122-1129
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• 2007

For a system with multiple real-time tasks of different deadlines, it is very difficult to find the optimal checkpoint interval because of the complexity in considering the scheduling of tasks. In this paper, we determine the optimal checkpoint interval for multiple real-time tasks that are scheduled by RM(Rate Monotonic) algorithm. Faults are assumed to occur with Poisson distribution. Checkpoints are inserted in the execution of task with equal distance in the same task, but different distances in other tasks. When faults occur, rollback to the latest checkpoint and re-execute task after the checkpoint. We derive the equation of maximum slack time for each task, and determine the number of re-executable checkpoint intervals for fault recovery. The equation to check the schedulibility of tasks is also derived. Based on these equations, we find the probability of all tasks executed within their deadlines successfully. Checkpoint intervals which make the probability maximum is the optimal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.196-206
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability, compared with mechanically scanned array radar. AESA radar brings a new challenges, radar resource management(RRM), which is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed stochastic radar beam scheduling algorithm using simulated annealing(SA), and evaluated the performance on the multi-function radar scenario. As a result, we showed that our proposed algorithm is superior to previous dispatching rule based scheduling algorithm from the viewpoint of beam processing latency and the number of scheduled beams, with real time capability.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1219-1224
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• 2018

Nowaday, smart mobile devices on Internet of Things are required to process and deliver greate amount of data in real-time. Therefore, heterogeneous mult-core architecture such the big.LITTLE core architecture, which shows high energy conservation while guaranteeing high performance, are widely employed on up to date smart mobile devices. The LITTLE cores should be highly utilized to gain higher energy conservation because LITTLE cores have much higher energy efficiency than big cores. In this paper, we propose a core selection algorithm, which tries to firstly assign a real-time task on a LITTLE core rather a big core while the task can be finished within its own deadline. We also perform simulation as performance evaluation to show that our proposed algorithm shows higher energy conservation while guaranteeing the required performance.

• Journal of KIISE
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• v.42 no.11
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• pp.1332-1338
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• 2015

Recently, growing attention has been paid to multi-GPU rendering to support real-time high-quality rendering at high resolution. In order to attain high performance in real-time multi-GPU rendering, great care needs to be taken to reduce the overhead of data transfer among GPUs and frame composition. This paper presents a novel multi-GPU algorithm that greatly enhances split frame rendering with implicit query-based synchronization. In order to support implicit synchronization in frame composition, we further present a message queue-based scheduling algorithm. We carried out an experiment to evaluate our algorithm, and found that our algorithm improved rendering performance up to 200% more than previously existing algorithms.

• ETRI Journal
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• v.28 no.1
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• pp.31-44
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• 2006

In a packet switching network, congestion is unavoidable and affects the quality of real-time traffic with such problems as delay and packet loss. Packet fair queuing (PFQ) algorithms are well-known solutions for quality-of-service (QoS) guarantee by packet scheduling. Our approach is different from previous algorithms in that it uses hardware time achieved by sampling a counter triggered by a periodic clock signal. This clock signal can be provided to all the modules of a routing system to get synchronization. In this architecture, a variant of the PFQ algorithm, called digitized delay queuing (DDQ), can be distributed on many line interface modules. We derive the delay bounds in a single processor system and in a distributed architecture. The definition of traffic contribution improves the simplicity of the mathematical models. The effect of different time between modules in a distributed architecture is the key idea for understanding the delay behavior of a routing system. The number of bins required for the DDQ algorithm is also derived to make the system configuration clear. The analytical models developed in this paper form the basis of improvement and application to a combined input and output queuing (CIOQ) router architecture for a higher speed QoS network.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.215-227
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• 2014

In modern computer architectures, caches are widely used to shorten the gap between processor speed and memory access time. However, caches are time-unpredictable, and thus can significantly increase the complexity of worst-case execution time (WCET) analysis, which is crucial for real-time systems. This paper proposes a time-predictable two-level scratchpad-based architecture and an ILP-based static memory objects assignment algorithm to support real-time computing. Moreover, to exploit the load/store latencies that are known statically in this architecture, we study a Scratch-pad Sensitive Scheduling method to further improve the performance. Our experimental results indicate that the performance and energy consumption of the two-level scratchpad-based architecture are superior to the similar cache based architecture for most of the benchmarks we studied.