2018 Ninth International Green and Sustainable Computing Conference (IGSC)最新文献_第6页

Practices of Energy Consumption for Sustainable Software Engineering 可持续软件工程的能源消耗实践

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752151

Ana Carolina Moises, A. Malucelli, S. Reinehr

{"title":"Practices of Energy Consumption for Sustainable Software Engineering","authors":"Ana Carolina Moises, A. Malucelli, S. Reinehr","doi":"10.1109/IGCC.2018.8752151","DOIUrl":"https://doi.org/10.1109/IGCC.2018.8752151","url":null,"abstract":"Sustainable Software Engineering, also known as “Green IN Software”, focuses on the production of sustainable software. The traditional software engineering process causes negative influences on the environment, economy and to society. For instance, the energy consumption during the software processing is considered as a first order impact because it directly leads to high costs on energy bills and consequently on the environment. Moreover, the optimization of a process implementation and software development can lead to second order impacts, also referred to as indirect impacts. Finally, the third other impact considers the user’s behaviors and consciousness regarding the concept of sustainability. In order to mitigate these negative impacts, the purpose of this research is to identify, via systematic literature review, the practices of sustainable software engineering reported by the academy applied in the industry. Through the systematic literature review, it was possible to discover 170 practices in which 70 were related to practices of energy consumption that could be adopted during the software development. Our results indicate that those practices emerged from the grounded theory, which are part of SWEBOK areas and are applicable in the industry.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124420630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Generator Event Detection from Synchrophasor Data Using a Two-Step Time-Series Machine Learning Algorithm 使用两步时间序列机器学习算法从同步相量数据中检测生成器事件

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752121

Jorge Berrios, S. Wallace, Xinghui Zhao, E. C. Sanchez, R. Bass

{"title":"Generator Event Detection from Synchrophasor Data Using a Two-Step Time-Series Machine Learning Algorithm","authors":"Jorge Berrios, S. Wallace, Xinghui Zhao, E. C. Sanchez, R. Bass","doi":"10.1109/IGCC.2018.8752121","DOIUrl":"https://doi.org/10.1109/IGCC.2018.8752121","url":null,"abstract":"The electrical faults of generators on an electrical grid, i.e., generator events (GE), must be detected efficiently when they occur, as these events can propagate to the rest of the grid in a cascading manner, leading to outages and wide-area blackouts. Many reasons exist that give rise to these faults, but at its most fundamental, they constitute an inability of a generator to match the grid usage requirements. In this paper, we present an efficient algorithm to accurately identify the occurrence of generator events within an electrical grid, using the monitoring data obtained from phasor measurement units (PMUs). Specifically, we have developed a machine learning algorithm that takes as input PMU data, and subsequently flags instances where a GE had taken place. The detection is performed in near real-time with the help of a standard off-the-shelf processing unit. Furthermore, we set out to create electrical fault maps that demarcate the progression of the event as it takes place. Experimental results show that our algorithm can accurately and efficiently identify the occurrence of a GE. In addition, we are also able to report a fault network map, which provides a powerful tool for troubleshooting.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116056221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Near Data Filtering for Distributed Database Systems 分布式数据库系统的近数据过滤

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752112

Zimeng Zhou, Xuan Sun, Jinghuan Yu, Sarana Nutanong, C. Xue

{"title":"Near Data Filtering for Distributed Database Systems","authors":"Zimeng Zhou, Xuan Sun, Jinghuan Yu, Sarana Nutanong, C. Xue","doi":"10.1109/IGCC.2018.8752112","DOIUrl":"https://doi.org/10.1109/IGCC.2018.8752112","url":null,"abstract":"Over the past decade, data movement costs dominate the execution time of data-intensive applications for distributed systems and they are expected to be even more important in the future. Near data processing is a straightforward solution to reduce data movement which brings compute resources closer to the data source. This paper explores near data processing in a generic distributed system to improve the performance by reducing data movement. An efficient near data filtering solution is designed and implemented by introducing a filter layer which performs tuple-level near data filtering. In order to reduce idle time of processing nodes and improve data transmission throughput the proposed solution is extended to support block-level near data filtering by creating index for each data block. Furthermore, to answer the question when and how to perform near data filtering this paper proposes an adaptive near data filtering solution to balance the computation and data transmission throughput. Experimental results show that the proposed solutions are superior to the best existing method for most cases. The adaptive near data filtering solution achieves an average speedup factor of 4:59 for queries with low selectivity.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127302325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Data Driven SAR Reconstruction on Hybrid Multicore systems 混合多核系统的动态数据驱动SAR重构

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752129

A. Wijayasiri, S. Ranka, S. Sahni

{"title":"Dynamic Data Driven SAR Reconstruction on Hybrid Multicore systems","authors":"A. Wijayasiri, S. Ranka, S. Sahni","doi":"10.1109/IGCC.2018.8752129","DOIUrl":"https://doi.org/10.1109/IGCC.2018.8752129","url":null,"abstract":"The reconstruction of nxn-pixel Synthetic Aperture Radar imagery using a Backprojection algorithm is compute intensive and incurs O(n2 · m) cost, where m is the number of pulses. As part of this research, we develop dynamic data driven multiresolution algorithms that speed up SAR backprojection on GPUs, hybrid multicore and many-core processors. Further, we performed experiments to observe improvements on a variety of architectures.The challenges in improving performance of this spatially variant reconstruction process on any architecture is load balancing, which circumvents asymmetric work assignment. On GPUs, fine tuned algorithms were developed as part of our research for improving execution time. Further, communication between processors was overlapped with computation to reduce overall execution time.We also developed parallel algorithms and software for constructing multi-resolution SAR images on hybrid multicore processors (HMPs). In particular, several load balancing algorithms were developed for optimizing performance and energy consumption on HMPs. We also developed a systematic approach for deriving the performance-energy trade-offs on HMPs while exploiting dynamic voltage and frequency scaling (DVFS) features of CPU cores and GPUs. This approach helps the user to select the right system configuration, that is, the number of processing elements of each type (cores/GPUs/etc.) and the respective clock frequencies, depending on whether performance or energy optimization is critical to the user.We evaluated performance and energy consumption of our algorithms on an Intel Knights Landing (KNL) processor as a representative of a many-core architecture. We also compared performance and energy consumption of KNL, Ivy Bridge and Tesla K40m.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Evaluation of Threshold Modelling for Smart Buildings’ Performance Testing 智能建筑性能测试阈值模型的比较评价

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752125

Elena Markoska, S. Lazarova-Molnar

{"title":"Comparative Evaluation of Threshold Modelling for Smart Buildings’ Performance Testing","authors":"Elena Markoska, S. Lazarova-Molnar","doi":"10.1109/IGCC.2018.8752125","DOIUrl":"https://doi.org/10.1109/IGCC.2018.8752125","url":null,"abstract":"With buildings consuming ca. 40% of the world’s total energy consumption, greater importance is given to their performance and ensuring that they behave as originally intended. The key to timely detection of underperformance is continuous real time measurement of a building’s behavior. To this end, performance testing has been developed as a practice that compares the observed behavior and the expected behavior of a building. Representation of the observed behavior is obtained by applying specific calculations to meters’ and sensors’ readings. The expected behavior can be calculated in different ways, depending on the necessity for historical data, or knowledge regarding the physical relationships between the building components. We study and compare these approaches based on the difficulty to develop and use, accuracy in predicting the expected behavior, as well as their ability to be integrated and run in real-time. The models are additionally compared to the country’s regulations for building energy consumption. The models for simulating the energy consumption of a building are trained and calibrated based on data from a case study smart building located in Denmark. The results show the superiority of the black box model, based on the higher accuracy of the forecasted performance, the lower effort of model generation and simulation, as well as applicability to a variety of buildings.","PeriodicalId":388554,"journal":{"name":"2018 Ninth International Green and Sustainable Computing Conference (IGSC)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124244279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

IGSC 2018 Special Session on Power-efficient, Optimal and Robust Multi-core Compute Platforms IGSC 2018节能、优化和稳健的多核计算平台特别会议

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/igcc.2018.8752158

引用次数: 0

IGSC 2018 PhD Workshop on IGSC Work-in-Progress IGSC 2018博士研讨会:IGSC正在进行的工作

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/igcc.2018.8752139

引用次数: 0

Fog Infrastructure and Service Management 雾基础设施和服务管理

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/IGCC.2018.8752143

Shehenaz Shaik, Sanjeev Baskiyar

引用次数: 1

IGSC 2018 Main Conference Papers IGSC 2018主要会议论文

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/igcc.2018.8752149

引用次数: 0

IGSC 2018 PhD Workshop on Towards Quantum Computing for Sustainable Computing IGSC 2018可持续计算量子计算博士研讨会

2018 Ninth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2018-10-01 DOI: 10.1109/igcc.2018.8752148

引用次数: 0