WS-PGRADE workflows for cloud-based distributed simulation

Modeling and Simulation (M&S) is used for systems analysis and decision making in existing or new systems. Modeling large and complex organizations produces large-scale simulations that are difficult or impossible to run on a single computer. Such experiment execution requires high computation. Distributed Simulation (DS) allows modeling of large systems as smaller submodels that execute on different nodes of a computer network and interoperate with each other in order to compose larger systems. Furthermore, cloud computing offers on-demand access to multiple compute resources. Consequently, being able to run DS on cloud resources allows for more experimentation with large-scale simulations in a cost-effective way. However, deploying DS and in fact Cloud-based DS presents significant technical challenges. This paper proposes a framework for deploying DS on the cloud in a transparent manner using the CloudSME Simulation Platform based on WS-PGRADE workflows. A healthcare case study is used to demonstrate our approach.Special thanks to Dr. Tamas Kiss and Mr Hannu Visti, Centre for Parallel Computing, University of Westminster for supporting in this study

Open source interface for distribution system modeling in power system co-simulation applications and two algorithms for populating feeder models, An

2017 Spring.Includes bibliographical references.The aging electric infrastructure power system infrastructure is undergoing a transformative change mainly triggered by the large-scale integration of distributed resources such as distributed generation, hybrid loads, and home energy management systems at the end-use level. The future electric grid, also referred to as the Smart Grid, will make use of these distributed resources to intelligently manage the day to day power system operations with minimum human intervention. The proliferation of these advanced Smart Grid resources may lead to coordination problems to maintain the generation-demand balance at all times. To ensure their safe integration with the grid, extensive simulation studies need to be performed using distributed resources. Simulation studies serve as an economically viable alternative to avoid expensive failures. They also serve as an invaluable platform to study energy consumption behavior, demand response, power system stability, and power system state estimation. Traditionally, power system analysis has been performed in isolated domains using simulation tools for the transmission and distribution systems. Moreover, modeling all the power system assets using a single power system tool is difficult and inconclusive. From the Smart Grid perspective, a common simulation platform for different power systems analysis tools is essential. A co-simulation framework enables the interaction of multiple power system tools, each modeling a single domain in detail, to run simultaneously and provide a holistic power system overview. To enable the co-simulation framework, a data exchange platform between the transmission and distribution system simulators is proposed to model transmission and distribution assets on different simulation testbeds. A graphical user interface (GUI) is developed as a frontend tool for the data exchange platform and makes use of two developed algorithms that simplifies the task of: 1. modeling distribution assets consisting of diverse feeder datasets for the distribution simulator and balanced three-phase level assets for the transmission system simulator, and 2. populating the distribution system with loads having stochastic profiles for timestep simulations. The load profiles used in the distribution system models are created using concepts from one-dimensional random walk theory to mimic the energy consumption behavior of residential class of consumers. The algorithms can simulate large scale distribution system assets linked to a transmission system for co-simulation applications. The proposed algorithms are tested on the standard test system – Roy Billinton Test System (RBTS) to model detailed distribution assets linked to a selected transmission node. Two open source power system simulators—MATPOWER© and GridLAB-D© are used for the transmission and distribution simulation process. The algorithms accurately create detailed distribution topology populated with 4026 residential loads expanded from the transmission node, bus 2 in RBTS. Thus, an automated modeling of power system transmission and distribution assets is proposed along with its application using a standard test system is provided

The MONARC toolset for simulating large network-distributed processing systems

The next generation of High Energy Physics experiments have envisaged the use of network-distributed Petabyte-scale data handling and computing systems of unprecedented complexity. The general concept is that of a "Data Grid Hierarchy" in which the central facility at the European Laboratory for Particle Physics (CERN) in Geneva will interact and coherently manage tasks shared by and distributed amongst national "Tier1 (National) Regional Centres" situated in the US, Europe, and Asia. CERN and the Tier1 Centers will further communicate and task-share with the Tier2 Regional Centers, Tier3 centers serving individual universities or research groups, and thousands of "Tier4" desktops and small servers. The design and optimization of systems with this level of complexity requires a realistic description and modeling of the data access patterns, the data flow across the local and wide area networks, and the scheduling and workload presented by hundreds of jobs running concurrently on large scale distributed systems exchanging very large amounts of data. The simulation toolset developed within the "Models Of Networked Analysis at Regional Centers" - MONARC project provides a code and execution time-efficient design and optimisation framework for large scale distributed systems. A process-oriented approach for discrete event simulation has been adopted because it is well suited to describe various activities running concurrently, as well the stochastic arrival patterns typical of this class of simulations. Threaded objects or "Active Objects" provide a natural way to map the specific behaviour of distributed data processing (and the required flows of data across the networks) into the simulation program. This simulation program is based on Java2(™) technology because of the support for the necessary methods and techniques needed to develop an efficient and flexible distributed process oriented simulation. This includes a convenient set of interactive graphical presentation and analysis tools, which are essential for the development and effective use of the simulation system. The design elements, status and features of the MONARC simulation tool are presented. The program allows realistic modelling of complex data access patterns by multiple concurrent users in large scale computing systems in a wide range of possible architectures. Comparison between queuing theory and realistic client-server measurements is also presented

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Data-Driven Distributed Modeling, Operation, and Control of Electric Power Distribution Systems

The power distribution system is disorderly in design and implementation, chaotic in operation, large in scale, and complex in every way possible. Therefore, modeling, operating, and controlling the distribution system is incredibly challenging. It is required to find solutions to the multitude of challenges facing the distribution grid to transition towards a just and sustainable energy future for our society. The key to addressing distribution system challenges lies in unlocking the full potential of the distribution grid. The work in this dissertation is focused on finding methods to operate the distribution system in a reliable, cost-effective, and just manner. In this PhD dissertation, a new data-driven distributed ($D^3M$) framework using cellular computational networks has been developed to model power distribution systems. Its performance is validated on an IEEE test case. The results indicate a significant enhancement in accuracy and performance compared to the state-of-the-art centralized modeling approach. This dissertation also presents a new distributed and data-driven optimization method for volt-var control in power distribution systems. The framework is validated for voltage control on an IEEE test feeder. The results indicate that the system has improved performance compared to the state-of-the-art approach. The PhD dissertation also presents a design for a real-time power distribution system testbed. A new data-in-the-loop (DIL) simulation method has been developed and integrated into the testbed. The DIL method has been used to enhance the quality of the real-time simulations. The assets combined with the testbed include data, control, and hardware-in-the-loop infrastructure. The testbed is used to validate the performance of a distribution system with significant penetration of distributed energy resources