An Approach to Analyze Tradeoffs for Aerospace System Design and Operation

There are important tradeoffs that need to be considered for the design and operation of aerospace systems. In addition to tradeoffs, there may also be multiple stakeholders of interest to the system and each may have different preferences as to the balance amongst the tradeoffs under consideration. A tradeoff hyperspace is created when there are three or more tradeoff dimensions and this increases the challenge associated with resolving the hyperspace in order to determine the best design and operation of a system. The corresponding objectives of this research are to develop a framework to analyze tradeoff hyperspaces and to account for the preferences of multiple stakeholders in this framework.This work was supported by the National Aeronautics and Space Administration (NASA) under grant NRA- #NNX10AN92A (NASA Ames). The authors are grateful to Dr. Neil Y. Chen and Dr. Banavar Sridhar in the Aviation Systems Division at NASA Ames for their valuable guidance and feedback in managing this project

FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development. Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method: The family of experiments was carried out by 92 participants: Computer Science Master s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results: The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions: The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.The authors would like to thank all the participants in the experiments for their selfless involvement in this research. This research is supported by the MULTIPLE Project (MICINN TIN2009-13838) and the ValI+D Program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015). Validating a model-driven software architecture evaluation and improvement method: A family of experiments. Information and Software Technology. 57:405-429. https://doi.org/10.1016/j.infsof.2014.05.018S4054295

Towards Process Support for Migrating Applications to Cloud Computing

Cloud computing is an active area of research for industry and academia. There are a large number of organizations providing cloud computing infrastructure and services. In order to utilize these infrastructure resources and services, existing applications need to be migrated to clouds. However, a successful migration effort needs well-defined process support. It does not only help to identify and address challenges associated with migration but also provides a strategy to evaluate different platforms in relation to application and domain specific requirements. This paper present a process framework for supporting migration to cloud computing based on our experiences from migrating an Open Source System (OSS), Hackystat, to two different cloud computing platforms. We explained the process by performing a comparative analysis of our efforts to migrate Hackystate to Amazon Web Services and Google App Engine. We also report the potential challenges, suitable solutions, and lesson learned to support the presented process framework. We expect that the reported experiences can serve guidelines for those who intend to migrate software applications to cloud computing.Muhammad Aufeef Chauhan, Muhammad Ali Baba

Iso-energy-efficiency: An approach to power-constrained parallel computation

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making