Optimization and Management of Large-scale Scientific Workflows in Heterogeneous Network Environments: From Theory to Practice

Next-generation computation-intensive scientific applications feature large-scale computing workflows of various structures, which can be modeled as simple as linear pipelines or as complex as Directed Acyclic Graphs (DAGs). Supporting such computing workflows and optimizing their end-to-end network performance are crucial to the success of scientific collaborations that require fast system response, smooth data flow, and reliable distributed operation.We construct analytical cost models and formulate a class of workflow mapping problems with different mapping objectives and network constraints. The difficulty of these mapping problems essentially arises from the topological matching nature in the spatial domain, which is further compounded by the resource sharing complicacy in the temporal dimension. We provide detailed computational complexity analysis and design optimal or heuristic algorithms with rigorous correctness proof or performance analysis. We decentralize the proposed mapping algorithms and also investigate these optimization problems in unreliable network environments for fault tolerance.To examine and evaluate the performance of the workflow mapping algorithms before actual deployment and implementation, we implement a simulation program that simulates the execution dynamics of distributed computing workflows. We also develop a scientific workflow automation and management platform based on an existing workflow engine for experimentations in real environments. The performance superiority of the proposed mapping solutions are illustrated by extensive simulation-based comparisons with existing algorithms and further verified by large-scale experiments on real-life scientific workflow applications through effective system implementation and deployment in real networks

Performance optimization and energy efficiency of big-data computing workflows

Next-generation e-science is producing colossal amounts of data, now frequently termed as Big Data, on the order of terabyte at present and petabyte or even exabyte in the predictable future. These scientific applications typically feature data-intensive workflows comprised of moldable parallel computing jobs, such as MapReduce, with intricate inter-job dependencies. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on workflow completion time, energy consumption, and financial cost if executed in clouds, which remains largely unexplored. This dissertation conducts an in-depth investigation into the properties of moldable jobs and provides an experiment-based validation of the performance model where the total workload of a moldable job increases along with the degree of parallelism. Furthermore, this dissertation conducts rigorous research on workflow execution dynamics in resource sharing environments and explores the interactions between workflow mapping and task scheduling on various computing platforms. A workflow optimization architecture is developed to seamlessly integrate three interrelated technical components, i.e., resource allocation, job mapping, and task scheduling. Cloud computing provides a cost-effective computing platform for big data workflows where moldable parallel computing models are widely applied to meet stringent performance requirements. Based on the moldable parallel computing performance model, a big-data workflow mapping model is constructed and a workflow mapping problem is formulated to minimize workflow makespan under a budget constraint in public clouds. This dissertation shows this problem to be strongly NP-complete and designs i) a fully polynomial-time approximation scheme for a special case with a pipeline-structured workflow executed on virtual machines of a single class, and ii) a heuristic for a generalized problem with an arbitrary directed acyclic graph-structured workflow executed on virtual machines of multiple classes. The performance superiority of the proposed solution is illustrated by extensive simulation-based results in Hadoop/YARN in comparison with existing workflow mapping models and algorithms. Considering that large-scale workflows for big data analytics have become a main consumer of energy in data centers, this dissertation also delves into the problem of static workflow mapping to minimize the dynamic energy consumption of a workflow request under a deadline constraint in Hadoop clusters, which is shown to be strongly NP-hard. A fully polynomial-time approximation scheme is designed for a special case with a pipeline-structured workflow on a homogeneous cluster and a heuristic is designed for the generalized problem with an arbitrary directed acyclic graph-structured workflow on a heterogeneous cluster. This problem is further extended to a dynamic version with deadline-constrained MapReduce workflows to minimize dynamic energy consumption in Hadoop clusters. This dissertation proposes a semi-dynamic online scheduling algorithm based on adaptive task partitioning to reduce dynamic energy consumption while meeting performance requirements from a global perspective, and also develops corresponding system modules for algorithm implementation in the Hadoop ecosystem. The performance superiority of the proposed solutions in terms of dynamic energy saving and deadline missing rate is illustrated by extensive simulation results in comparison with existing algorithms, and further validated through real-life workflow implementation and experiments using the Oozie workflow engine in Hadoop/YARN systems

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

클라우드 컴퓨팅 환경기반에서 수치 모델링과 머신러닝을 통한 지구과학 자료생성에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2022. 8. 조양기.To investigate changes and phenomena on Earth, many scientists use high-resolution-model results based on numerical models or develop and utilize machine learning-based prediction models with observed data. As information technology advances, there is a need for a practical methodology for generating local and global high-resolution numerical modeling and machine learning-based earth science data. This study recommends data generation and processing using high-resolution numerical models of earth science and machine learning-based prediction models in a cloud environment. To verify the reproducibility and portability of high-resolution numerical ocean model implementation on cloud computing, I simulated and analyzed the performance of a numerical ocean model at various resolutions in the model domain, including the Northwest Pacific Ocean, the East Sea, and the Yellow Sea. With the containerization method, it was possible to respond to changes in various infrastructure environments and achieve computational reproducibility effectively. The data augmentation of subsurface temperature data was performed using generative models to prepare large datasets for model training to predict the vertical temperature distribution in the ocean. To train the prediction model, data augmentation was performed using a generative model for observed data that is relatively insufficient compared to satellite dataset. In addition to observation data, HYCOM datasets were used for performance comparison, and the data distribution of augmented data was similar to the input data distribution. The ensemble method, which combines stand-alone predictive models, improved the performance of the predictive model compared to that of the model based on the existing observed data. Large amounts of computational resources were required for data synthesis, and the synthesis was performed in a cloud-based graphics processing unit environment. High-resolution numerical ocean model simulation, predictive model development, and the data generation method can improve predictive capabilities in the field of ocean science. The numerical modeling and generative models based on cloud computing used in this study can be broadly applied to various fields of earth science.지구의 변화와 현상을 연구하기 위해 많은 과학자들은 수치 모델을 기반으로 한 고해상도 모델 결과를 사용하거나 관측된 데이터로 머신러닝 기반 예측 모델을 개발하고 활용한다. 정보기술이 발전함에 따라 지역 및 전 지구적인 고해상도 수치 모델링과 머신러닝 기반 지구과학 데이터 생성을 위한 실용적인 방법론이 필요하다. 본 연구는 지구과학의 고해상도 수치 모델과 머신러닝 기반 예측 모델을 기반으로 한 데이터 생성 및 처리가 클라우드 환경에서 효과적으로 구현될 수 있음을 제안한다. 클라우드 컴퓨팅에서 고해상도 수치 해양 모델 구현의 재현성과 이식성을 검증하기 위해 북서태평양, 동해, 황해 등 모델 영역의 다양한 해상도에서 수치 해양 모델의 성능을 시뮬레이션하고 분석하였다. 컨테이너화 방식을 통해 다양한 인프라 환경 변화에 대응하고 계산 재현성을 효과적으로 확보할 수 있었다. 머신러닝 기반 데이터 생성의 적용을 검증하기 위해 생성 모델을 이용한 표층 이하 온도 데이터의 데이터 증강을 실행하여 해양의 수직 온도 분포를 예측하는 모델 훈련을 위한 대용량 데이터 세트를 준비했다. 예측모델 훈련을 위해 위성 데이터에 비해 상대적으로 부족한 관측 데이터에 대해서 생성 모델을 사용하여 데이터 증강을 수행하였다. 모델의 예측성능 비교에는 관측 데이터 외에도 HYCOM 데이터 세트를 사용하였으며, 증강 데이터의 데이터 분포는 입력 데이터 분포와 유사함을 확인하였다. 독립형 예측 모델을 결합한 앙상블 방식은 기존 관측 데이터를 기반으로 하는 예측 모델의 성능에 비해 향상되었다. 데이터합성을 위해 많은 양의 계산 자원이 필요했으며, 데이터 합성은 클라우드 기반 GPU 환경에서 수행되었다. 고해상도 수치 해양 모델 시뮬레이션, 예측 모델 개발, 데이터 생성 방법은 해양 과학 분야에서 예측 능력을 향상시킬 수 있다. 본 연구에서 사용된 클라우드 컴퓨팅 기반의 수치 모델링 및 생성 모델은 지구 과학의 다양한 분야에 광범위하게 적용될 수 있다.1. General Introduction 1 2. Performance of numerical ocean modeling on cloud computing 6 2.1. Introduction 6 2.2. Cloud Computing 9 2.2.1. Cloud computing overview 9 2.2.2. Commercial cloud computing services 12 2.3. Numerical model for performance analysis of commercial clouds 15 2.3.1. High Performance Linpack Benchmark 15 2.3.2. Benchmark Sustainable Memory Bandwidth and Memory Latency 16 2.3.3. Numerical Ocean Model 16 2.3.4. Deployment of Numerical Ocean Model and Benchmark Packages on Cloud Clusters 19 2.4. Simulation results 21 2.4.1. Benchmark simulation 21 2.4.2. Ocean model simulation 24 2.5. Analysis of ROMS performance on commercial clouds 26 2.5.1. Performance of ROMS according to H/W resources 26 2.5.2. Performance of ROMS according to grid size 34 2.6. Summary 41 3. Reproducibility of numerical ocean model on the cloud computing 44 3.1. Introduction 44 3.2. Containerization of numerical ocean model 47 3.2.1. Container virtualization 47 3.2.2. Container-based architecture for HPC 49 3.2.3. Container-based architecture for hybrid cloud 53 3.3. Materials and Methods 55 3.3.1. Comparison of traditional and container based HPC cluster workflows 55 3.3.2. Model domain and datasets for numerical simulation 57 3.3.3. Building the container image and registration in the repository 59 3.3.4. Configuring a numeric model execution cluster 64 3.4. Results and Discussion 74 3.4.1. Reproducibility 74 3.4.2. Portability and Performance 76 3.5. Conclusions 81 4. Generative models for the prediction of ocean temperature profile 84 4.1. Introduction 84 4.2. Materials and Methods 87 4.2.1. Model domain and datasets for predicting the subsurface temperature 87 4.2.2. Model architecture for predicting the subsurface temperature 90 4.2.3. Neural network generative models 91 4.2.4. Prediction Models 97 4.2.5. Accuracy 103 4.3. Results and Discussion 104 4.3.1. Data Generation 104 4.3.2. Ensemble Prediction 109 4.3.3. Limitations of this study and future works 111 4.4. Conclusion 111 5. Summary and conclusion 114 6. References 118 7. Abstract (in Korean) 140박

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems