{"title":"AMPRO-HPCC: A Machine-Learning Tool for Predicting Resources on Slurm HPC Clusters.","authors":"Mohammed Tanash, Daniel Andresen, William Hsu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Determining resource allocations (memory and time) for submitted jobs in High Performance Computing (HPC) systems is a challenging process even for computer scientists. HPC users are highly encouraged to overestimate resource allocation for their submitted jobs, so their jobs will not be killed due to insufficient resources. Overestimating resource allocations occurs because of the wide variety of HPC applications and environment configuration options, and the lack of knowledge of the complex structure of HPC systems. This causes a waste of HPC resources, a decreased utilization of HPC systems, and increased waiting and turnaround time for submitted jobs. In this paper, we introduce our first ever implemented fully-offline, fully-automated, stand-alone, and open-source Machine Learning (ML) tool to help users predict memory and time requirements for their submitted jobs on the cluster. Our tool involves implementing six ML discriminative models from the scikit-learn and Microsoft LightGBM applied on the historical data (sacct data) from Simple Linux Utility for Resource Management (Slurm). We have tested our tool using historical data (saact data) using HPC resources of Kansas State University (Beocat), which covers the years from January 2019 - March 2021, and contains around 17.6 million jobs. Our results show that our tool achieves high predictive accuracy <i>R</i> <sup>2</sup> (0.72 using LightGBM for predicting the memory and 0.74 using Random Forest for predicting the time), helps dramatically reduce computational average waiting-time and turnaround time for the submitted jobs, and increases utilization of the HPC resources. Hence, our tool decreases the power consumption of the HPC resources.</p>","PeriodicalId":72112,"journal":{"name":"ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences","volume":"2021 ","pages":"20-27"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906793/pdf/nihms-1831252.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10760547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Facilitating large data management in research contexts.","authors":"Daniel Andresen, Gerrick Teague","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Research data management is becoming increasingly complex as the amount of data, metadata and code increases. Often, researchers must obtain multidisciplinary skills to acquire, transfer, share, and compute large datasets. In this paper we present the results of an investigation into providing a familiar web-based experience for researchers to manage their data and code, leveraging popular, well-funded tools and services. We show how researchers can save time and avoid mistakes, and we provide a detailed discussion of our system architecture and implementation, and summarize the new capabilities, and time savings which can be achieved.</p>","PeriodicalId":72112,"journal":{"name":"ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences","volume":" ","pages":"36-43"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446462/pdf/nihms-1831850.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33448466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Marchese, Lam-Son Lê, Bob Dao, M. Toulouse, N. Thoai
{"title":"Message from the Program Chairs and Industry Panel Chairs","authors":"M. Marchese, Lam-Son Lê, Bob Dao, M. Toulouse, N. Thoai","doi":"10.1109/ACOMP50827.2020.00005","DOIUrl":"https://doi.org/10.1109/ACOMP50827.2020.00005","url":null,"abstract":"","PeriodicalId":72112,"journal":{"name":"ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences","volume":"63 1","pages":"ix"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74407752","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}
{"title":"Low-Complexity Encryption Algorithm Considering Energy Balance on Wireless Sensor Networks","authors":"P. N. Huu, Q. Minh, Hieu Nguyen Trong","doi":"10.1109/ACOMP.2019.00025","DOIUrl":"https://doi.org/10.1109/ACOMP.2019.00025","url":null,"abstract":"This paper proposes an effective key-generation scheme applying to data encryption standard (DES) algorithm for wireless sensor networks (WSNs). In the scheme, data encryption is divided into several tasks for multiple nodes along a path from a source node to the base station. We perform simulations to compare distribution and centralization models. The results show that the distributed model obtains more balances in energy consumption compared to the centralization model. The proposed key management method also improves the security level of data by increasing the number of keys with a simple algorithm in WSNs.","PeriodicalId":72112,"journal":{"name":"ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences","volume":"49 1","pages":"112-118"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74012966","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}
A. An, P. Diem, L. Lan, Tran Van Toi, Lam Quoc Huy Le Nguyen Binh
{"title":"Building a Product Origins Tracking System Based on Blockchain and PoA Consensus Protocol","authors":"A. An, P. Diem, L. Lan, Tran Van Toi, Lam Quoc Huy Le Nguyen Binh","doi":"10.1109/ACOMP.2019.00012","DOIUrl":"https://doi.org/10.1109/ACOMP.2019.00012","url":null,"abstract":"In recent years, the traceability of product origins is strongly concerned, particularly for food products as they directly influence human health. Therefore, there have been some efforts to develop product origins tracking systems. In this paper, we propose an approach to building a supply chain management system based on the blockchain technology for agriculture product origins tracking. The supply chain model is borrowed from Walmart's and it is implemented based on the Ethereum framework using the PoA (Proof of Authority) consensus algorithm. Our experiment shows that the proposed system not only fulfills the requirements of a product origins tracking but also takes the advantages of the blockchain technology such as the immutability and security of data, the low cost in making the transactions, and so on.","PeriodicalId":72112,"journal":{"name":"ADVCOMP ... the ... International Conference on Advanced Engineering Computing and Applications in Sciences","volume":"87 1","pages":"27-33"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75423339","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}