• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.135-145
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• 2010

Efficient Directed Acyclic Graph(DAG) scheduling is critical for achieving high performance in Distributed Heterogeneous computing System(DHCS). In this paper, we present a new high-performance scheduling algorithm, called the LCFT(Levelized Critical First Task) algorithm, for DHCS. The LCFT algorithm is a list-based scheduling that uses a new attribute to efficiently select tasks for scheduling in DHCS. The complexity of LCFT is $O(\upsilon+e)(p+log\;\upsilon)$. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling algorithms such as PETS, HPS, HCPT and GCA in terms of the schedule length and SpeedUp. The comparison studies show that LCFT significantly outperforms PETS, HPS, HCPT and GCA in schedule length, SpeedUp.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.8-17
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• 2014

Petascale systems(so called supercomputers) have been mainly used for supporting communication-intensive and tightly-coupled parallel computations based on message passing interfaces such as MPI(HPC: High-Performance Computing). On the other hand, computing paradigms such as High-Throughput Computing(HTC) mainly target compute-intensive (relatively low I/O requirements) applications consisting of many loosely-coupled tasks(there is no communication needed between them). In Korea, recently emerging applications from various scientific fields such as pharmaceutical domain, high-energy physics, and nuclear physics require a very large amount of computing power that cannot be supported by a single type of computing resources. In this paper, we present our HTCaaS(High-Throughput Computing as a Service) which can leverage national distributed computing resources in Korea to support these challenging HTC applications and describe the details of our system architecture, job execution scenario and case studies of various scientific applications.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.349-357
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• 2006

We introduce a new performance evaluation methodology that helps programmers evaluate different tuning alternatives in order to improve program performance. This methodology permits measuring performance implications of using tuning alternatives. Specifically, the methodology predicts performance after workload migration for a distributed or parallel program in contrast to traditional performance methodlogies that quantify time spent in program components for bottleneck identification. The methodology thus provides guidance on workload migration. The methodology also permits predicting the performance impact of changing the underlying network. The methodology may evaluate performance incrementally and online during the execution of the program to be tuned. We show that our methodology, when it is implemented and used, permits accurately predicting the performance of different tuning alternatives for a test suite of six programs.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.196-204
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• 2012

Recently, many applications perform OLAP(On-Line Analytical Processing) over a very large volume of data. Multidimensional data cube is regarded as a core tool in OLAP analysis. This paper focuses on the method how to efficiently compute data cubes in parallel by using a popular parallel processing tool, MapReduce. We investigate efficient ways to implement PipeSort algorithm, a well-known data cube computation method, on the MapReduce framework. The PipeSort executes several (descendant) cuboids at the same time as a pipeline by scanning one (ancestor) cuboid once, which have the same sorting order. This paper proposed four ways implementing the pipeline of the PipeSort on the MapReduce framework which runs across 20 servers. Our experiments show that PipeMap-NoReduce algorithm outperforms the rest algorithms for high-dimensional data. On the contrary, Post-Pipe stands out above the others for low-dimensional data.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.429-439
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• 2006

A desktop grid, which is a computing grid composed of idle computing resources in a large network of desktop computers, is a promising platform for compute-intensive distributed computing applications. However, due to reliability and unpredictability of computing resources, effective scheduling of parallel computing applications on such a platform is a difficult problem. This paper proposes a new scheduling method aimed at reducing the total execution time of a parallel application on a desktop grid. The proposed method is based on utilizing the histories of execution behavior of individual computing nodes in the scheduling algorithm. In order to test out the feasibility of this idea, execution trace data were collected from a set of 40 desktop workstations over a period of seven weeks. Then, based on this data, the execution of several representative parallel applications were simulated using trace-driven simulation. The simulation results showed that the proposed method improves the execution time of the target applications significantly when compared to previous desktop grid scheduling methods. In addition, there were fewer instances of application suspension and failure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.101-108
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• 2007

Parallel processing algorithms, coupled with advanced networking and distributed computing architectures, improve the overall computational performance, dependability, and versatility of a digital signal processing system In this paper, novel parallel algorithms are introduced and investigated for advanced sonar algorithm, conventional matched-field processing (CMFP). Based on a specific domain, each parallel algorithm decomposes the sequential workload in order to obtain scalable parallel speedup. Depending on the processing requirement of the algorithm, the computational performance of the parallel algorithm reveals different characteristics. The high-complexity algorithm, CMFP shows scalable parallel performance on the array of DSP processors. The impact on parallel performance due to workload balancing, communication scheme, algorithm complexity, processor speed, network performance, and testbed configuration is explored.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.670-679
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• 2012

The recent dissemination of cloud computing makes the amount of data storage to be increased and the cost of storing the data grow rapidly. Accordingly, data and service requests from users also increases the load on the cloud storage. There have been many works that tries to provide low-cost and high-performance schemes on distributed file systems. However, most of them have some weaknesses on performing parallel and random data accesses as well as data accesses of frequent small workloads. Recently, improving the performance of distributed file system based on caching technology is getting much attention. In this paper, we propose a CHPC(Cloud storage High-Performance Caching) framework, providing parallel caching, distributed caching, and proxy caching in distributed file systems. This study compares the proposed framework with existing cloud systems in regard to the reduction of the server's disk I/O, prevention of the server-side bottleneck, deduplication of the page caches in each client, and improvement of overall IOPS. As a results, we show some optimization possibilities on the cloud storage systems based on some evaluations and comparisons with other conventional methods.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.686-689
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• 2004

Computational grids are emerging as a new infrastructure for internet-based parallel and distributed computing. Grid systems are enables the sharing, exchanging, discovery and aggregation of resources which distributed multiple administrative domains, organizations and enterprises. Accounting information service is one of the main obstacles to widespread adoption of the grid. But, most of grid portals do not support accounting information service. In this paper, we design an accounting information service and build a web-based grid portal including account management service and accounting information service.