Earticle

동형 암호는 암호화된 상태의 데이터를 이용해 연산을 수행할 수 있는 암호화 방법으로, 클라우드 서비스 등 원격서버에 데이터를 저장하고 사용하는 경우에 있어 프라이버시 문제를 해결하는 수단으로 최근 활발하게 연구되고 있다. 최근까지는 완전 동형 암호의 경우 큰 암호문에 한 비트 평문 밖에 저장할 수 없어 동형 암호의 실용성 문제가제기되었으나, 하나의 암호문에 여러 평문을 저장하는 일괄 완전 동형 암호가 Eurocrypt 2013에서 Cheon 등에의해 제시되었다. 본 논문에서는 이러한 일괄 완전 동형 암호 및 이의 응용을 직접 구현하고 성능을 평가하였으며, 추가적으로 멀티코어 환경에서의 병렬처리에 따른 최적화 가능성을 확인하였다.

Homomorphic encryption is an encryption scheme where operations are performed over encrypted data. An extensive research on homomorphic encryption has been done to solve the privacy issue in the applications such as cloud computing services that store critical data on remote servers. Until recently, a ciphertext of fully homomorphic encryption was able to deal with only a single plaintext bit, which was an issue from a practical viewpoint. However, in Eurocrypt 2013, Cheon et al. proposed a batch fully homomorphic encryption scheme that encrypts multiple plaintext bits in a single ciphertext. In this paper, we implement this batch fully homomorphic encryption scheme and its application and estimate the performance. In addition, we implemented this scheme on a multi-core environment to verify the effect of a parallel optimization.

최근 유전체 분석 기법과 시퀀싱 기기의 발전에 따라 개인별 맞춤 의학의 실현에 관한 기대가 높아지고 있다. 개인 맞춤 의학은 개인의 유전 정보를 분석하고 이 들의 특이성을 파악하여, 개인의 질병 예방 및 치료에 적용하고자 하는 전략이다. 그러나 한 개인의 시퀀스 분석을 위한 적정 커버리지(약 30 커버리지로 예측됨)의 시퀀싱 데이터는 100 GB를 넘는 방대한 크기를 가지므로 다수 개인의 유전 정보의 분석/비교를 위하여 대규모 데이터 처리/분석 환경 및 고성능 컴퓨팅 방식의 도입이 필수적이다. 본 연구에서는 클라우드 컴퓨팅 기술을 기반으로 하는 새로운 병렬 유전체 단위반복변위 (Copy Number Variation, CNV) 영역 추출 알고리즘을 제안한다. 제안된 방식에서는 다수의 컴퓨팅 노드를 활용하는 병렬 처리를 위하여 Hadoop의 MapReduce 오픈 소스를 사용하며, 대규모 유전자 시퀀스를 병렬로 처리하여 (개인_ID, 염색체_ID, 컨티그_ID) 단위로 CNV 영역을 추출, 보고한다.

Recent advances in genome analysis method and sequencing technology may allow for a greater degree of personalized medicine than is currently available. Personalized medicine is a future medical model emphasizing the systematic use of genetic information about an individual patient to apply and optimize patient's preventative and therapeutic care. However, as an enormous amount of data (around 100 GB or up to 30x read coverage data for an individual) is needed to analyze and compare genetic information among many individuals, it is essential to employ the environment of high-performance computing system. In this study, we propose a novel algorithm to detect CNV (Copy Number Variation) regions by carrying out parallel and transparency processing based on cloud computing in a part of new frontier technologies. This method is able to perform simultaneous tasking with large numbers of computing nodes using open source of 'MapReduce' in 'Hadoop' project. The CNV regions detected by the proposed method are reported as a unit of map consists of <individual_id>, <chromosome_id>, and <contig_id> by carrying out parallel processing for tremendous size of short read data from next generation sequencing instrument.

최근 클라우드 컴퓨팅이 발전함에 따라 데이터베이스 아웃소싱에 대한 관심이 증가하고 있다. 그러나 데이터베이스 를 아웃소싱하는 경우 데이터 소유자의 민감한 데이터가 노출될 수 있다는 문제점이 존재한다. 따라서 본 논문에서 는 프라이버시 보호를 지원하는 병렬 kNN 분류 알고리즘을 제안한다. 제안하는 알고리즘은 기존 기법과 동일한 수 준의 정보 보호 수준을 제공하면서 다중 CPU 코어를 활용한 병렬 처리를 지원한다. 이를 위해 제안하는 알고리즘 은 단일 CPU 코어 기반 데이터 처리 프로토콜을 다중 CPU 코어 기반 데이터 처리 프로토콜로 변환함으로써 효율 적인 질의처리를 수행한다. 또한 노이즈(Noise) 데이터를 전처리함으로써 정보 보호를 지원하는 동시에 효율적인 질의 처리가 가능하다. 마지막으로 성능 평가를 통해 제안하는 알고리즘이 기존 기법보다 질의처리 시간 측면에서 8~30배 성능이 우수함을 보인다.

With the recent development of cloud computing, interest in database outsourcing is increasing. However, when outsourcing a database, there is a problem that sensitive data of the data owner may be exposed. Therefore, in this paper, we propose a parallel kNN classification algorithm that supports information protection. The proposed algorithm supports parallel processing using multiple CPU cores while providing the same level of information protection as the existing techniques. For this, the proposed algorithm performs efficient query processing by converting a data processing protocol based on a single CPU core into a data processing protocol based on multiple CPU cores. By preprocessing noise data, it also supports information protection and enables efficient query processing. Finally, it is shown from performance evaluation that the proposed algorithm is 8 to 30 times better than the existing techniques, in terms of query processing time.

The demands of data analysis and processing make the parallel computing platforms are continuously developed. But the existing parallel computing models which are the core of platforms present the characteristics of diversification, high pertinence and short cycle. So a synthetic model of supporting flexible platforms urgently needs to be researched. This work researches a synthetic model to shield the heterogeneity of parallel computing models under the theories of λ-calculus, functional language and graph reduction. Based on the MapReduce and BSP models, first of all, the performing principles of models are analyzed. And then the unified modalities of models are found. Finally, the synthetic model having high performance is developed with graph reduction rules, and the experiment results are also shown.

With the rapid development of the 3G network, traditional calculation methods are unable to adapt to the data scene that telecom users' Network access behavior's data scale increase rapidly dozens of TB. The cloud techniques such as Hadoop platform are introduced to solve the data storage problem. The appropriate data mining algorithms are designed from the perspective of practical application. This paper improves the traditional decision tree SPRINT algorithms, proposes a parallel computing program and successfully applies to the Hadoop platform.

Compute Unified Device Architecture (CUDA) is a mature parallel computing architecture, which can significantly accelerate performance of the computation intensive algorithm. In this paper, FastSLAM algorithm based on the probability model is further studied and the resampling algorithm for the path estimation is improved. In the resampling phase, resampling rules are redesigned and the previous data limitations are broken for the purpose of parallelization. We propose the FastSLAM algorithm based on CUDA, which accelerates robot localization and mapping. The experiment results show that FastSLAM_CUDA can achieve a significant speedup over the FastSLAM with many particles.

To improve the performance of the Magnetotelluric Occam inversion, by in-depth analysis of the sequential algorithm, we develop a multi-level hybrid parallel computing scheme for MT Occam inversion based on MPI+OpenMP+CUDA and implement it on a small heterogeneous cluster. We implement the parallel algorithm for solving linear equations with Gauss elimination, jacobian matrix, cross-product matrix calculations and Cholesky decomposition. Through reasonable decomposition, combination and mapping of computing tasks, the scheme reduces the data traffic and realizes the purpose of load balancing. By changing the matrix storage order，the memory access speed is significantly increased. The scheme is tested with multiple synthesis data from 2-D theoretical models and the execution efficiency of sequential code and parallel code on a 4 nodes PC cluster is comparatively analyzed. The test results show that the realization of this hybrid parallel algorithm is feasible and efficient. Compared with the sequential code and pure message passing algorithm, the inversion speed is obviously increased.

The polygon construction is the emphasis and difficulty for constructing the topological relationship of spatial data. Many scholars continuous study and innovate for the algorithm. The traditional computational process mainly involves three steps in algorithms: (1) Determining the relationship between adjacent arcs. (2) Searching polygons. (3) Determining the topological relationship between arcs and polygons. With the increase of the number of data and the need to improve the automatization, speed and complexity of algorithm, the serial algorithm has become more and more difficult in polygon construction and is often more time consuming. The algorithm of polygon construction is meeting challenge with the development of computer technology. When processing the large-scale linear data, an efficient strategy to reduce the time of complex operations in algorithm has not been proposed in spatial data field. We propose a novel algorithm to construct polygons automatically in the parallel environment base on the new IT technology. The key of the algorithm is paralleling the more time consuming operation (search, sort. etc.) and improving the speed of polygon construction. According to the characteristics of directed rings in graph model, we construct topological polygons. The experimental results on multicore computers show that the algorithm is efficient parallel performance for polygon construction.

Traditional data management is usually based on relational databases, which are capable of managing small amounts of data. But relational databases have some difficulty in inquiry, management, and analysis of large amounts of data and magnanimity data. The method of effective management of magnanimity data is a problem deserving of study. In this paper, traditional relational databases are moved to Hadoop, in order to implement query and analysis on Hadoop. This paper changes the amount of data record and number of nodes in clusters, and records the query time in different conditions. Advantages and disadvantages of query on Hadoop can be analyzed by comparing the statistics with the query time on relational database Oracle. The factors affecting the time of query on Hadoop can be found by analysis. Furthermore, the result is also a reference material of future research and data managements on cloud platforms.

Scientific workflow scheduling problem is a combinatorial optimization problem. In the real application, the scientific workflow generally has thousands of task nodes. Scheduling large-scale workflow has huge computational overhead. In this paper, a parallel algorithm for scientific workflow scheduling is proposed so that the computing speed can be improved greatly. Our method used ant colony optimization approaches on the GPU. Thousands of GPU threads can parallel construct solutions. The parallel ant colony algorithm for workflow scheduling was implemented with CUDA C language. Scheduling problem instances with different scales were tested both in our parallel algorithm and CPU sequential algorithm. The experimental results on NVIDIA Tesla M2070 GPU show that our implementation for 1000 task nodes runs in 5 seconds, while a conventional sequential algorithm implementation runs in 104 seconds on Intel Xeon X5650 CPU. Thus, our GPU-based parallel algorithm implementation attains a speed-up factor of 20.7.

In this paper, the parallel algorithm of JPEG coding based on GPU is proposed, most image compression systems have efficiency problem and the real-time of wireless multimedia sensor networks (WMSN) which used in image compression and transmission is also an issue need to be solved, so in this paper parallel computation is used in JPEG coding, it is an effective ways to solve these problems. The system of JPEG coding system mainly has eight parts: discrete cosine transform (DCT) and inverse DCT, quantization and inverse quantization, Zig-zag ordering and inverse Zig-zag ordering, Huffman coding and decoding. The proposed parallel algorithm of JPEG coding makes all the parts of JPEG system run on GPU, so the speed of JPEG coding is improved significantly. DCT and Huffman coding are wildly used in image processing; therefore the proposed parallel algorithm can be used in many fields about image compression and processing. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of DCT-JPEG algorithm. The experimental results show that compared with conventional JPEG coding, the maximum speedup ratio of parallel algorithm of JPEG coding can reach more than 120 times, and the reconstructed image has almost the same performance with the serial algorithm in terms of objective quality and subjective effect.

In the big data era, with the parallel evolution of computer architecture, computing changes and modifications of industrial application mode resource expansion capability, we need to explore a new parallel computing model, to reflect the properties and large data applications form the current parallel machines, and a variety of mainstream big data processing system for unified theoretical analysis to guide large data applications tuning. Currently, despite the large data programming model study made many achievements, and is widely used in the TB level or even PB-class data processing and analysis, but the corresponding computational model study has just begun. From traditional parallel computing model, research big data programming model and large data computation model, summed up the three basic problems of large data model, in theory, need to be addressed: the three elements of the problem model, scalability and fault tolerance issues and performance optimization. Around these three questions, on the one hand and performance optimization model to calculate the theoretical study of data from a large, on the other hand these performance optimization methods in case of an actual big data.

This research contributes to the on-going research effort by exploring alternate methods for soft computing optimization the highly nonlinear and uncertain systems. This research addresses two basic issues related to the control of an uncertain system; (1) design of a robust feedback controller, and (2) the design of a parallel artificial intelligence based optimization to increase the result qualification. The robust backstepping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstepping controller is designed to achieve this objective. The controller developed in this research is designed in two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation. Next, the fuzzy logic methodology applied to it to solution uncertainty problem by parallel optimization. The fuzzy model free optimization is formulated to minimize the problem of nonlinear formulation of uncertain systems.

In social media services and social network services, it is necessary to collect, analyze and process their big data with low maintenance cost. Therefore, distributed-parallel data processing on cloud platform is getting spotlight as useful solution for them. In this paper, we present a new architecture of DIVE-C: DIstributed-parallel Virtual Environment on Cloud computing platform for distributed parallel data processing applications which offers a transparent virtual computing environment in order to provide a way easy to launch user’s distributed parallel applications. It hides the complexity of the cloud, and helps users to focus on their new applications and core services. DIVE-C uses agent-based resource management scheme to configure VM resources and application deployment for offering various distributed-parallel application models. VM resources are automatically provided by unified cloud management layer. Furthermore, an easy-to-use web interface of DIVE-C offers convenience to users. We implemented a prototype of DIVE-C, and its experiment results show the competitive performance of DIVE-C for dynamic resource and virtual computing environment provisioning for various data processing models.

E-commerce is a cyber, electronic and informational business activities. Electronic commerce has been developing rapidly in recent years in China. Cloud computing provides a new idea for the development of electronic commerce. In practice, cloud computing also has a very good combination of e-commerce. To predict the profit of electronic commerce can find the existing problems, grasp the development trend, and make better management of electronic commerce. In this paper, we study the profit prediction of electronic commerce. Then, we propose an improved parallel PSO-LSSVM algorithm, and use this algorithm to predict the benefits of electronic commerce. Experimental results show that the proposed algorithm is effective and reliable.

The paper Proposed Item parallel collaborative filtering recommendation algorithm (IP-CF). Through designing efficient parallel algorithm, compute-extensive procedures are distributed to different processing nodes in Hadoop platform. Taking advantage of parallel computing, we accelerate the response of recommendation. The experimental results show that our proposed algorithm IP-CF is more efficient and scalable than current parallel algorithms.