The Parma Polyhedra Library: Toward a Complete Set of Numerical Abstractions for the Analysis and Verification of Hardware and Software Systems

Since its inception as a student project in 2001, initially just for the handling (as the name implies) of convex polyhedra, the Parma Polyhedra Library has been continuously improved and extended by joining scrupulous research on the theoretical foundations of (possibly non-convex) numerical abstractions to a total adherence to the best available practices in software development. Even though it is still not fully mature and functionally complete, the Parma Polyhedra Library already offers a combination of functionality, reliability, usability and performance that is not matched by similar, freely available libraries. In this paper, we present the main features of the current version of the library, emphasizing those that distinguish it from other similar libraries and those that are important for applications in the field of analysis and verification of hardware and software systems.Comment: 38 pages, 2 figures, 3 listings, 3 table

Novel processes for smart grid information exchange and knowledge representation using the IEC common information model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical Sciences Research Council (EPSRC

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

Efficient integration of software components for scientific simulations

Abstract unavailable please refer to PD

Cloud Computing cost and energy optimization through Federated Cloud SoS

2017 Fall.Includes bibliographical references.The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks

Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness

2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]XI n.s

Proof-of-Concept Application - Annual Report Year 1

In this document the Cat-COVITE Application for use in the CATNETS Project is introduced and motivated. Furthermore an introduction to the catallactic middleware and Web Services Agreement (WS-Agreement) concepts is given as a basis for the future work. Requirements for the application of Cat-COVITE with in catallactic systems are analysed. Finally the integration of the Cat-COVITE application and the catallactic middleware is described. --Grid Computing

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