• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.1-9
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• 2016

In this paper, for the improvement of quality of service(QoS) performance of heterogeneous networks, multi-cell scheduling is proposed. In order to implement the proposed algorithm, for the recognition of the impact on the throughput performance of users, macro-pico-cells that form distributed architecture were proposed. In operating heterogeneous networks, considering the centralized structure, a macro-RRH(Remote Radio Head) deployment scenario was proposed. For interference mitigation of the proposed system, by applying the optional sub-frame, through CQI(Channel Quality Indicator) measurement for each sub-frame period, constraint conditions were measured according to system situations. For the simplification, the pattern of the same ABS muting was assumed. In the above two multi-cell environments, the algorithm of high-speed load balancing maintenance was proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.1035-1044
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• 1998

A new multi-channel access scheme and the associated network architecture for a single-hop WDM local area network is proposed in this paper. The proposed architecture has Central Scheduling Node (CSN) for the transmission coordination among many users, which is one of the key issues in single-hop WDM networks. The data channels, source nodes, and destination nodes are selected at CSN in very simple menner. Our scheme can relive the control processing overhead at all nodes in the network which is caused in existing distributed scheduling algorithms. CSN is simple in the architecture can be implemented easily. in respect to the network performance, the maximum obtainable throughput is up to that of the ideal output queuing because of collision free scheduling. We use the MQMS (multi-queue multi-server) model for performance analaysis.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.51-59
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• 2003

It is effective to be located in the double nodes in the distributed object replication systems, then object which nodes share is the same contents. The nodes store an access information on their local cache as it access to the system. and then the nodes fetch and use it, when it needed. But with time the coherence Problems will happen because a data carl be updated by other nodes. So keeping the coherence of the system we need a mechanism that we managed the to improve to improve the performance and availability of the system effectively. In this paper to keep coherence in the shared memory condition, we can set the limited parallel performance without the additional cost except the coherence cost using it to keep the object at the proposed adaptive duplication object(ADO) algorithms. Also to minimize the coherence maintenance cost which is the bi99est overhead in the duplication method, we must manage the object effectively for the number of replication and location of the object replica which is the most important points, and then it determines the cos. And that we must study the adaptive duplication object management mechanism which will improve the entire run time.

• Journal of Digital Contents Society
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• v.5 no.3
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• pp.199-204
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• 2004

A problem of many fractal image compression algorithms providing good quality at low bit rate is that the decoding time rests on an iterative procedure whose complexity is imag-dependent. This paper proposes an iterative-free fractal image decoding algorithm to reduce the decoding time. In the proposed method, under the encoder previously with the same codebook image as an initial image to be used at the decoder, the fractal coefficients are obtained through calculating the similarity between the codebook image and an input image to be encoded. As the decoding time could be remarkably reduced. For verifying the validity and universality of proposed method, We evaluated and analyzed the performance of decoding time and image quality for a number of still images and a moving picture with different distributed characteristics.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.525-532
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• 2006

Compared with the previous recovery algorithms for causal message logging, Elnozahy's recovery algerian considerably reduces the number of stable storage accesses and enables live processes to execute their computations continuously while performing its recovery procedure. However, if causal message logging is used with asynchronous checkpointing, the state of the system may be inconsistent after having executed this algorithm in case of concurrent failures. In this paper, we show these inconsistent cases and propose a low-cost recovery algorithm for causal message logging to solve the problem. To ensure the system consistency, this algorithm allows the recovery leader to obtain recovery information from not only the live processes, but also the other recovering processes. Also, the proposed algorithm requires no extra message compared with Elnozahy's one and its additional overhead incurred by message piggybacking is significantly low. To demonstrate this, simulation results show that the first only increases about 1.0%$\sim$2.1% of the recovery information collection time compared with the latter.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.755-768
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• 2017

As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.8
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• pp.421-431
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• 2004

This paper presents adaptive execution techniques that determine whether parallelized loops are executed in parallel or sequentially in order to maximize performance. The adaptation and performance estimation algorithms are implemented in a compiler preprocessor. The preprocessor inserts code that automatically determines at compile-time or at run-time the way the parallelized loops are executed. Using a set of standard numerical applications written in Fortran77 and running them with our techniques on a distributed shared memory multiprocessor machine (SGI Origin2000), we obtain the performance of our techniques, on average, 26%, 20%, 16%, and 10% faster than the original parallel program on 32, 16, 8, and 4 processors, respectively. One of the applications runs even more than twice faster than its original parallel version on 32 processors.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.1-8
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• 2017

Given a multi-dimensional dataset of tuples, a skyline query returns a subset of tuples which are not 'dominated' by any other tuples. Skyline query is very useful in Big data analysis since it filters out uninteresting items. Much interest was devoted to the MapReduce-based parallel processing of skyline queries in large-scale distributed environment. There are three requirements to improve parallelism in MapReduced-based algorithms: (1) workload should be well balanced (2) avoid redundant computations (3) Optimize network communication cost. In this paper, we introduce MR-SEAP (MapReduce sample Skyline object Equality Angular Partitioning), an efficient angular space partitioning based skyline query processing using sampling-based pruning, which satisfies requirements above. We conduct an extensive experiment to evaluate MR-SEAP.