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Space Telescopes and Instrumentation 2022: Ultraviolet to Gamma Ray (2022), Montreal, Jul 17-22, 2022.--Proceedings of SPIE - The International Society for Optical Engineering vol. 12181 Article number 121811W.-- Full list of authors: Zhang, Shuang-Nan; Santangelo, Andrea; Xu, Yupeng; Feroci, Marco; Hernanz, Margarita; Lu, Fangjun; Chen, Yong; Feng, Hua; Nandra, Kirpal; Jiang, Weichun; Svoboda, Jiri; Brandt, Soren; Schanne, Stephane; in't Zand, Jean; Michalska, Malgorzata; Bozzo, Enrico; Kalemci, Emrah; Agudo, Ivan; Ahangarianabhari, Mahdi; Aitink-Kroes, Gabby; Ambrosi, Giovanni; Ambrosino, Filippo; An, Zhenghua; Perez Torres, Miguel Angel; Antonelli, Matias; Argan, Andrea; Babinec, Viktor; Baldini, Luca; Barbera, Marco; van Baren, Coen; Baudin, David; Bayer, Joerg; Bellazzini, Ronaldo; Bellutti, Pierluigi; Bertucci, Bruna; Bertuccio, Giuseppe; Bi, Xingzi; Boezio, Mirko; Bonvicini, Valter; Bonvicini, Walter; Bordas, Pol; Borghese, Alice; Borghi, Giacomo; Bouyjou, Florent; Bozkurt, Ayhan; Brez, Alessandro; Brienza, Daniele; Cadoux, Franck; Campana, Riccardo; Cao, Jiewei; Cao, Xuelei; Casares, Jorge; Cavazzuti, Elisabetta; Ceraudo, Francesco; Chen, Tianxiang; Chen, Wen; Chen, Can; Chen, Yupeng; Chen, Xin; Chen, Yehai; Chenevez, Jerome; Cheng, Yaodong; Cirrincione, Daniela; Civitani, Marta; Cong, Min; Coti Zelati, Francesco; Cui, Weiwei; Cui, Tao; Cui, Wei; Dai, Boyu; Dauser, Thomas; De Angelis, Nicolas; De Marco, Barbara; De Rosa, Alessandra; Del Monte, Ettore; Di Cosimo, Sergio; Diebold, Sebastian; Dilillo, Giuseppe; Ding, Fei; Dohnal, Roman; Dong, Zefang; Donnarumma, Immacolata; Dovciak, Michal; Du, Yuanyuan; Ducci, Lorenzo; Evangelista, Yuri; Fan, Qingmei; Favre, Yannick; Ferres, Patricia; Fiandrini, Emanuele; Ficorella, Francesco; Fuschino, Fabio; Luis Galvez, Jose; Gao, Na; Gao, Min; Ge, Yuqiang; Ge, Mingyu; Gevin, Olivier; Grassi, Marco; Gu, Yudong; Gu, Quanying; Guan, Ju; Guedel, Manuel; Han, Xingbo; Han, Dawei; He, Huilin; He, Junwang; Hedderman, Paul; den Herder, Jan-Willem; Hong, Bin; Hormaetxe, Ander; Hou, Dongjie; Hu, Zexun; Hu, Hao; Hu, Qingbao; Hu, Yu; Huang, Yue; Huang, Jiangjiang; Huang, Qiushi; Huo, Jia; Hynek, Richard; Iwasawa, Kazumi; Izzo, Lucca; Ji, Long; Jia, Shumei; Jiang, Bowen; Jiang, Wei; Jiang, Jiechen; Jiang, Xiaowei; Jiao, Yang; Jin, Ge; Jin, Fan; Jose, Jordi; Karas, Vladimir; Kennedy, Thomas; Kirsch, Christian; Kole, Merlin; Komarek, Martin; Kreykenbohm, Ingo; Kuiper, Lucien; Kuvvetli, Irfan; Labanti, Claudio; Latronico, Luca; Laubert, Phillip; Li, Tao; Li, Longhui; Li, Hong; Li, Duo; Li, Wei; Li, Maoshun; Li, Gang; Li, Xiaobo; Li, Chengkui; Li, Bing; Li, Haibo; Liang, Hong; Liao, Qiuyan; Liao, Jinyuan; Limousin, Olivier; Lin, Yanjian; Linares, Manuel; Liu, Rui; Liu, Yichen; Liu, Zhihao; Liu, Hongwei; Liu, Xiaojing; Liu, Xiaohua; Lo Cicero, Ugo; Loehring, Jens; Lombardi, Giovanni; Lorenz, Maximilian; Lu, Bing; Lucchesi, Leonardo; Luo, Tao; Luo, Laidan; Ma, Jia; Ma, Xiang; Macera, Daniele; Malcovati, Piero; Manfreda, Alberto; Maria Paredes, Josep; Marinucci, Andrea; Martindale, Adrian; Meidinger, Norbert; Mele, Filippo; Mendes, Vasco; Meng, Bin; Merkl, Martin; Meuris, Aline; Miniutti, Giovanni; Minuti, Massimo; Morbidini, Alfredo; Morgante, Gianluca; Muleri, Fabio; Munini, Riccardo; Mussolin, Lorenzo; Negri, Barbara; Nie, Jianyin; Novak, Petr; Nowosielski, Witold; Nuti, Alessio; Onat, Ahmet; Orleanski, Piotr; Orsini, Leonardo; Ottensamer, Roland; Ou, Ge; Pacciani, Luigi; Paltani, Stephane; Pan, Teng; Pan, Kai; Pareschi, Giovanni; Patruno, Alessandro; Pepponi, Giancarlo; Perinati, Emanuele; Piazzolla, Raffaele; Picciotto, Antonino; Piemonte, Claudio; Pinchera, Michele; Pliego, Samuel; Poutanen, Juri; Qi, Liqiang; Qi, Fazhi; Qiang, Pengfei; Qiao, Fangjian; Qiao, Zheng; Qiu, Xiangbiao; Qiu, Chengbo; Qu, Jinlu; Rachevski, Alexandre; Rashevskaia, Irina; Rea, Nanda; Ribo, Marc; de la Rie, Rob; Rodriguez, Jerome; Rodriguez Gil, Pablo; Sala, Gloria; Schanz, Thomas; Serafinelli, Roberto; Sgro, Carmelo; Shen, Zhengxiang; Sheng, Lizhi; Shi, Jingyan; Sironi, Giorgia; Skup, Konrad; Song, Liming; Song, Zeyu; Song, Jiangbo; Spandre, Gloria; Spiga, Daniele; Su, Yongquan; Sun, Liang; Sungur, Muberra; Sveda, Libor; Tagliaferri, Gianpiero; Tan, Ying; Tao, Lian; Tcherniak, Denis; Tenzer, Chris; Todaro, Michela; Tolos, Laura; Torok, Gabriel; Torres, Diego F.; Trois, Alessio; Turhan, Onur; Uttley, Phil; Vacchi, Andrea; Virgilli, Enrico; Vogel, Channah; Walton, Dave; Wang, Jian; Wang, Xianqi; Wang, Bo; Wang, Langping; Wang, Xiaofeng; Wang, Dianlong; Wang, Yusa; Wang, Juan; Wang, Hao; Wang, Ruijie; Wang, Ping; Wang, Lingjun; Wang, Wenshuai; Wang, Tian; Wang, Haoyu; Wang, Zhanshan; Watts, Anna L.; Wen, Xiangyang; Wilms, Joern; Winter, Berend; Wu, Xin; Wu, Chao; Wu, Zhenyu; Wu, Qiong; Wu, Kaiji; Xiong, Hao; Xiong, Shaolin; Xu, Wei; Xu, Zhao; Xu, Jingjing; Xu, Xiongwei; Xue, Jiadai; Yan, Yongqing; Yan, Xingtao; Yang, Xianghui; Yang, Yanji; Yang, Xiongtao; Yang, Sheng; Yang, Jiawei; Yang, Yingquan; Yi, Shuxu; Yu, Yonggao; Yu, Ke; Zampa, Ganluigi; Zampa, Nicola; Zane, Silvia; Zdziarski, Andrzej A.; Zhang, Long; Zhang, Shu; Zhang, Wenda; Zhang, Xiaoli; Zhang, Zhen; Zhang, Jiawei; Zhang, Fan; Zhang, Ziliang; Zhang, Dali; Zhang, Honglin; Zhang, Aimei; Zhang, Tong; Zhang, Hongmei; Zhang, Juan; Zhang, Liang; Zhang, Yueting; Zhao, Li; Zhao, Baosheng; Zhao, Zijian; Zhao, Xiaofan; Zhao, Haisheng; Zhao, Xiaoyun; Zheng, Shijie; Zhou, Yupeng; Zhou, Xiaohong; Zhu, Jiahuan; Zhu, Yuxuan; Zhu, Cheng; Zhu, Zhencai; Zorzi, Nicola; Zwart, Frans, The enhanced X-ray Timing and Polarimetry mission (eXTP) is a flagship observatory for X-ray timing, spectroscopy and polarimetry developed by an International Consortium. Thanks to its very large collecting area, good spectral resolution and unprecedented polarimetry capabilities, eXTP will explore the properties of matter and the propagation of light in the most extreme conditions found in the Universe. eXTP will, in addition, be a powerful X-ray observatory. The mission will continuously monitor the X-ray sky, and will enable multiwavelength and multi-messenger studies. The mission is currently in phase B, which will be completed in the middle of 2022. © 2022 SPIE. All rights reserved., The Chinese team acknowledges support by the Chinese Academy of Sciences through the Strategic Priority Research Program, Grant No. XDA15020100. The Italian authors acknowledge funding support by the Italian Space Agency (under agreement n. 2020-3-HH.0 and ASI-INAF n.2017-14-H.O) and INFN (project XRO -X-ray Observatories). The Spanish authors acknowledge funding support from the MICIN/AEI grant PID2019-108709GB-I00. The German team acknowledges support from the Bundesministerium fur Wirtschaft und Technologie through the Deutsches Zentrum fur Luft-und Raumfahrt e.V. (DLR) under the grant number FKZ 50 OO 1902. The Polish team acknowledges FNP grant POIR.04.04.00-00-5C65/17. The Czech team acknowledges support from the GACR project 21-06825X. The Turkish team acknowledges support from the Turkish Space Agency., With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709

In this paper we will describe the LHAASO distributed computing system based on virtualization and cloud computing technologies. Particularly, we discuss the key points of integrating distributed resources. A solution of integrating cross-domain resources is proposed, which adopt the Openstack+HTCondor to make the distributed resources work as a whole resource pool. A flexible resource scheduling strategy and a job scheduling policy are presented to realize the resource expansion on demand and the efficient job scheduling to remote sites transparently, so as to improve the overall resource utilization. We will also introduce the deployment of the computing system located in Daocheng, the LHAASO observation base using cloud-based architecture, which greatly helps to reduce the operation and maintenance cost as well as to make sure the system availability and stability. Finally, we will show running status of the system.

As a data-intensive computing application, high-energy physics requires to process and store massive data at the PB or EB level. It requires high performance data access and large volume of data storage as well. Some enterprises and research organizations are beginning to use tiered storage architectures, using tapes, disks or solid drives at the same time to reduce hardware purchase costs and power consumption. Tiered storage requires data management software to migrate less active data to lower cost storage devices. Thus an automated data migration strategy is very necessary. Data access requests are driven by the behavior of users or programs. There must be associations between different files that are accessed consecutively. This paper proposes a method to predict the heat of data access and use data heat trend as the basis criteria for data migration. This paper proposes a deep learning algorithm model to predict the evolution trend of data access heat. This paper discussed the implementation of some initial parts of the system. Then some preliminary experiments are conducted with these parts.

With the development of the new generation of High Energy Physics (HEP) experiments, huge amounts of data are being generated. Efficient parallel algorithms/frameworks and High IO throughput are key to meet the scalability and performance requirements of HEP offline data analysis. Though Hadoop has gained a lot of attention from scientific community for its scalability and parallel computing framework for large data sets, it’s still difficult to make HEP data processing tasks run directly on Hadoop. In this paper we investigate the application of Hadoop to make HEP jobs run on it transparently. Particularly, we discuss a new mechanism to support HEP software to random access data in HDFS. Because HDFS is streaming data stored only supporting sequential write and append. It cannot satisfy HEP jobs to random access data. This new feature allows the Map/Reduce tasks to random read/write on the local file system on data nodes instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop diverse MapReduce model for HEP jobs such as Corsika simulation, ARGO detector simulation and Medea++ reconstruction. And we develop a toolkit for users to submit/query/remove jobs. In addition, we provide cluster monitoring and account system to benefit to the system availability. This work has been in production for HEP experiment to gain about 40,000 CPU hours per month since September, 2016.