• Educational Technology International
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• v.20 no.2
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• pp.137-168
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• 2019

This study examined the effects of information volume and distribution on learners' cognitive load and recall in a mobile augmented reality (AR) environment. Information volume refers to the degree of information users are provided in a learning task, while information distribution indicates the way in which information is distributed, either in a virtual or real format. Sixteen undergraduate students participated in the study, which employed a 2 × 3 randomized block factorial design with repeated measures. Information volume and distribution were independent variables, and factors in learners' cognitive load (mental effort, perceived ease of use, and perceived task difficulty) and recall test scores were the dependent variables. Information volume had significant main effects on perceived ease of use and task difficulty, and recall test scores, while information distribution had significant main effects on perceived task difficulty and test scores. A detailed discussion and implications are provided.

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.175-186
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• 2009

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.453-461
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• 2010

Specifying and designing the election algorithm in mobile ad hoc distributed systems is very difficult task. It is because mobile ad hoc systems are more prone to failures than conventional distributed systems. The aim of this paper is to propose a specification and design of the election algorithm in a specific ad hoc mobile computing environment. For this aim, we specify and design an election algorithm in this paper. In addition, we formally verify it and show that it is correct. This solution is based on the nodes detection algorithm that is a classical one for synchronous distributed systems.

• ETRI Journal
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• v.42 no.5
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• pp.748-760
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• 2020

MapReduce is a framework that can process huge datasets in parallel and distributed computing environments. However, a single machine failure during the runtime of MapReduce tasks can increase completion time by 50%. MapReduce handles task failures by restarting the failed task and re-computing all input data from scratch, regardless of how much data had already been processed. To solve this issue, we need the computed key-value pairs to persist in a storage system to avoid re-computing them during the restarting process. In this paper, the task failure resilience (TFR) technique is proposed, which allows the execution of a failed task to continue from the point it was interrupted without having to redo all the work. Amazon ElastiCache for Redis is used as a non-volatile cache for the key-value pairs. We measured the performance of TFR by running different Hadoop benchmarking suites. TFR was implemented using the Hadoop software framework, and the experimental results showed significant performance improvements when compared with the performance of the default Hadoop implementation.

• 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 Internet Computing and Services
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• v.8 no.1
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• pp.133-142
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• 2007

Large database processing is one of the most important technique in the information society, Since most large database is regionally distributed, the distributed database processing has been brought into relief. Communications and data compressions are the basic technologies for large database processing. In order to maximize those technologies, the execution time for the task, the size of data, and communication time between processors should be considered. In this paper, the dataflow scheme and vertically layered allocation algorithm have been used to optimize the distributed large database processing. The basic concept of this method is rearrangement of processes considering the communication time between processors. The paper also introduces measurement model of the execution time, the size of output data, and the communication time in order to implement the proposed scheme.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.631-633
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• 2003

인터넷 가반 분산/병렬 처리 프레임 워크 PDP(Parallel/Distributed Processing Scheme on Web)는 네트워크 내 유휴 상태 호스트들을 활용하여 대용량 작업을 병렬로 처리한다. 본 논문에서는 이러한 서브 작업을 할당받는 자원이 동작하는 네트워크 환경을 모니터링 함으로써 수시로 변화하는 네트워크 환경에 대처하는 방안을 제시한다. 특히 네트워크 환경 모니터링 예측 결과를 PDP의 작업 할당 알고리즘에 적용하여 네트워크 과부하 및 결함 등으로 인해 발생되는 작업 지연 요소에 적응적 대처함으로써 전체 작업 수행 처리율 향상을 도모하는 방법을 제안한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.116-126
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• 2015

In a distributed heterogeneous computing system, the performance of a parallel application greatly depends on its task scheduling algorithm. Therefore, in order to improve the performance, it is essential to consider some factors that can have effect on the performance of the parallel application in a given environment. One of the most important factors that affects the total execution time is a critical path. In this paper, we propose the CLTS algorithm for a task scheduling. The CLTS sets the priorities of all nodes to improve overall performance by applying leveling method to improve parallelism of task execution and by reducing the delay caused by waiting for execution of critical nodes in priority phase. After that, it conditionally uses insertion based policy or duplication based policy in processor allocation phase to reduce total schedule time. To evaluate the performance of the CLTS, we compared the CLTS with the DCPD and the HCPFD in our simulation. The results of the simulations show that the CLTS is better than the HCPFD by 7.29% and the DCPD by 8.93%. with respect to the average SLR, and also better than the HCPFD by 9.21% and the DCPD by 7.66% with respect to the average speedup.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.9-18
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• 2002

In this paper, we propose scalable scheduling techniques based on EDF to efficiently integrate hard real-time and multimedia soft real-time tasks in the distributed real-time multimedia database system. Hard tasks are guarangteed based on worst case execution times, whereas multimedia soft tasks are served based on mean execution times. This paper describes a served-based scheme for partitioning the CPU bandwidth among different task classes that coexist in the same system. To handle the problem of class overloads characterized by varying number of tasks and varying task arrival rates, thus scheme shows how to adjust the fraction of the CPU bandwidth assigned to each class. This scheme fixes the maximum time that each hard task can execute in the period of the server, whereas it can dynamically change the bandwidth reserved to each multimedia task. The proposed method is capable of minimizing the mean tardiness of multimedia tasks, without jeopardizing the schedulability of the hard tasks. The performance of this scheduling method is compared with that of similar mechanisms through simulation experiments.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.357-362
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• 2007

In distributed systems like cluster systems, in order to get more performance improvement, the initial task placement system precisely estimates and correctly assigns the resource requirement by the process. The resource-based initial job placement scheme needs the prediction of resource usage of a task in order to fit it to the most suitable hosts. However, the wrong prediction of resource usage causes serious performance degradation in dynamic load balancing systems. Therefore, in this paper, to resolve the problem due to the wrong prediction, we propose a new load metric. By the new load metric, the resource-based initial job placement scheme can work without priori knowledge about the type of process. Simulation results show that the dynamic load balancing system using the proposed approach achieves shorter execution times than the conventional approaches.