Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)最新文献
D. Spiga, M. Antonacci, T. Boccali, D. Ciangottini, A. Costantini, G. Donvito, C. Duma, M. Duranti, V. Formato, L. Gaido, D. Salomoni, M. Tracolli, D. Michelotto
Dynamic On Demand Analysis Service (DODAS) is a Platform as a Service tool built combining several solutions and products developed by the INDIGO-DataCloud H2020 project. DODAS allows to instantiate on-demand container-based clusters. Both HTCondor batch system and platform for the Big Data analysis based on Spark, Hadoop etc, can be deployed on any cloud-based infrastructures with almost zero effort. DODAS acts as cloud enabler designed for scientists seeking to easily exploit distributed and heterogeneous clouds to process data. Aiming to reduce the learning curve as well as the operational cost of managing community specific services running on distributed cloud, DODAS completely automates the process of provisioning, creating, managing and accessing a pool of heterogeneous computing and storage resources. DODAS was selected as one of the Thematic Services that will provide multi-disciplinary solutions in the EOSC-hub project, an integration and management system of the European Open Science Cloud starting in January 2018. The main goals of this contribution are to provide a comprehensive overview of the overall technical implementation of DODAS, as well as to illustrate two distinct real examples of usage: the integration within the CMS Workload Management System and the extension of the AMS computing model.
{"title":"DODAS: How to effectively exploit heterogeneous clouds for scientific computations","authors":"D. Spiga, M. Antonacci, T. Boccali, D. Ciangottini, A. Costantini, G. Donvito, C. Duma, M. Duranti, V. Formato, L. Gaido, D. Salomoni, M. Tracolli, D. Michelotto","doi":"10.22323/1.327.0024","DOIUrl":"https://doi.org/10.22323/1.327.0024","url":null,"abstract":"Dynamic On Demand Analysis Service (DODAS) is a Platform as a Service tool built combining several solutions and products developed by the INDIGO-DataCloud H2020 project. DODAS allows to instantiate on-demand container-based clusters. Both HTCondor batch system and platform for the Big Data analysis based on Spark, Hadoop etc, can be deployed on any cloud-based infrastructures with almost zero effort. DODAS acts as cloud enabler designed for scientists seeking to easily exploit distributed and heterogeneous clouds to process data. Aiming to reduce the learning curve as well as the operational cost of managing community specific services running on distributed cloud, DODAS completely automates the process of provisioning, creating, managing and accessing a pool of heterogeneous computing and storage resources. DODAS was selected as one of the Thematic Services that will provide multi-disciplinary solutions in the EOSC-hub project, an integration and management system of the European Open Science Cloud starting in January 2018. The main goals of this contribution are to provide a comprehensive overview of the overall technical implementation of DODAS, as well as to illustrate two distinct real examples of usage: the integration within the CMS Workload Management System and the extension of the AMS computing model.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130452062","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 mass of adolescents has decided their occupations/jobs/majors out of proper and professional advice from school services. For instance, adolescents do not have adequate information about occupations/jobs, what occupations can be reached by which majors, and what kind of education and training are needed for particular jobs. On the other hand, major choices of adolescents are influenced by a society and their family. They receive occupational information in common jobs from the environment. But they are a lack of information in professional occupations. Furthermore, the choice of major has become increasingly complex due to the existence of multiple human skills, which mean each person has their ability at the certain area and can be applied to multiple jobs/occupations. For those reasons, students need an automatic counselling system according to their values. To do this, occupation recommendation system is implemented with a variety of IT and soft skills. The main goal of this research is to build an occupation recommendation system (ORS) by using data mining and natural language processing (NLP) methods on open educational resource (OER) and skill dataset, in order to help adolescents. The system can provide different variety of academic programs, related online courses (e.g., MOOCs), required skills, ability, knowledge, and job tasks, and jobs currently announced as well as relevant occupational descriptions. The system can assist adolescents in major selection and career planning. Furthermore, the system incorporates a set of searching results, which are recommended using similarity measurements and hybridization recommendation techniques. These methods serve as a base for recommending occupations that meet interests and competencies of adolescents.
{"title":"Skill-based Occupation Recommendation System","authors":"A. Ochirbat, T. Shih","doi":"10.22323/1.327.0008","DOIUrl":"https://doi.org/10.22323/1.327.0008","url":null,"abstract":"A mass of adolescents has decided their occupations/jobs/majors out of proper and professional advice from school services. For instance, adolescents do not have adequate information about occupations/jobs, what occupations can be reached by which majors, and what kind of education and training are needed for particular jobs. On the other hand, major choices of adolescents are influenced by a society and their family. They receive occupational information in common jobs from the environment. But they are a lack of information in professional occupations. Furthermore, the choice of major has become increasingly complex due to the existence of multiple human skills, which mean each person has their ability at the certain area and can be applied to multiple jobs/occupations. For those reasons, students need an automatic counselling system according to their values. To do this, occupation recommendation system is implemented with a variety of IT and soft skills. The main goal of this research is to build an occupation recommendation system (ORS) by using data mining and natural language processing (NLP) methods on open educational resource (OER) and skill dataset, in order to help adolescents. The system can provide different variety of academic programs, related online courses (e.g., MOOCs), required skills, ability, knowledge, and job tasks, and jobs currently announced as well as relevant occupational descriptions. The system can assist adolescents in major selection and career planning. Furthermore, the system incorporates a set of searching results, which are recommended using similarity measurements and hybridization recommendation techniques. These methods serve as a base for recommending occupations that meet interests and competencies of adolescents.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125914298","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}
It is widely argued that optical communication and networking technologies will play a significant role in future data centers. Although the optical technologies have made a significant advancements over the last few years towards providing a very high data transmission rate as well as increased flexibility and efficiency, an additional effort is needed to investigate suitable architectures and technologies for optical network within (intra) and outside (inter) data centers. This paper presents a brief overview on optical networks for data centers. Furthermore, the paper provides a qualitative categorization of the proposed schemes based on the type of optical switches. In the end, future research direction and opportunities of optical interconnect for data centers are discussed.
{"title":"Optical Interconnects for Cloud Computing Data Centers: Recent Advances and Future Challenges","authors":"Muhammad Imran, Saqib Haleem","doi":"10.22323/1.327.0017","DOIUrl":"https://doi.org/10.22323/1.327.0017","url":null,"abstract":"It is widely argued that optical communication and networking technologies will play a significant role in future data centers. Although the optical technologies have made a significant advancements over the last few years towards providing a very high data transmission rate as well as increased flexibility and efficiency, an additional effort is needed to investigate suitable architectures and technologies for optical network within (intra) and outside (inter) data centers. This paper presents a brief overview on optical networks for data centers. Furthermore, the paper provides a qualitative categorization of the proposed schemes based on the type of optical switches. In the end, future research direction and opportunities of optical interconnect for data centers are discussed.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122531725","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}
Xiaowei Jiang, Jiaheng Zou, Jingyan Shi, R. Du, Qingbao Hu, Zhenyu Sun, Hongnan Tan
In a batch system, the job queue is in charge of a set of jobs. Job health is the most important issue concerned by users and administrators. The job state can be queuing, running, completed, error or held, etc, that can reflect the job health. Generally jobs can move from one state to another. However, if a job keeps in a state for too long time, there might be problems, such as worker node failure and network blocking. In a large-scale computing cluster, problems cannot be avoided. That means a number of jobs will be blocked in one state, and cannot be completed in an expected time. This will delay the progress of the computing task. For that situation, this paper studies on the unhealthy job state's reason, problem handling and job queue stability. We aim to improve the job health, and then we can improve job success rate and speed up users' task progress. Unhealthy reasons can be found from job attributes, queue information and logs, which can be analyzed in detail to acquire better solutions. Depending on who do the recovery, all the solutions are grouped into two categories. The first category is recovered by administrator. Most problems are automatically solved through integrating with the monitor system. When problem is solved, the corresponding job will be rescheduled in time, without involving users. The second category is automatically informing users to dispose unhealthy jobs by themselves. In accordance with the results of unhealthy analysis, the helpful suggestion might be recommended to users for quick recovery. Based on the foregoing methods, a job queue health system is designed and implemented at IHEP. We define a series of standards to pick out unhealthy jobs. Various factors relevant with unhealthy jobs are collected and analyzed in association. In case that unhealthy jobs could be recovered at admin side, automatic recovery functions are carried out to automatically recover the unhealthy jobs. In case that unhealthy jobs must be recovered at user side, alarms are sent to users via emails, WeChat, etc. The running status of job queue health system indicates that it's able to improve the job queue health in most situations.
{"title":"Studies on Job Queue Health and Problem Recovery","authors":"Xiaowei Jiang, Jiaheng Zou, Jingyan Shi, R. Du, Qingbao Hu, Zhenyu Sun, Hongnan Tan","doi":"10.22323/1.327.0018","DOIUrl":"https://doi.org/10.22323/1.327.0018","url":null,"abstract":"In a batch system, the job queue is in charge of a set of jobs. Job health is the most important issue concerned by users and administrators. The job state can be queuing, running, completed, error or held, etc, that can reflect the job health. Generally jobs can move from one state to another. However, if a job keeps in a state for too long time, there might be problems, such as worker node failure and network blocking. In a large-scale computing cluster, problems cannot be avoided. That means a number of jobs will be blocked in one state, and cannot be completed in an expected time. This will delay the progress of the computing task. For that situation, this paper studies on the unhealthy job state's reason, problem handling and job queue stability. We aim to improve the job health, and then we can improve job success rate and speed up users' task progress. Unhealthy reasons can be found from job attributes, queue information and logs, which can be analyzed in detail to acquire better solutions. Depending on who do the recovery, all the solutions are grouped into two categories. The first category is recovered by administrator. Most problems are automatically solved through integrating with the monitor system. When problem is solved, the corresponding job will be rescheduled in time, without involving users. The second category is automatically informing users to dispose unhealthy jobs by themselves. In accordance with the results of unhealthy analysis, the helpful suggestion might be recommended to users for quick recovery. Based on the foregoing methods, a job queue health system is designed and implemented at IHEP. We define a series of standards to pick out unhealthy jobs. Various factors relevant with unhealthy jobs are collected and analyzed in association. In case that unhealthy jobs could be recovered at admin side, automatic recovery functions are carried out to automatically recover the unhealthy jobs. In case that unhealthy jobs must be recovered at user side, alarms are sent to users via emails, WeChat, etc. The running status of job queue health system indicates that it's able to improve the job queue health in most situations.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125392776","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}
Pablo Panero, L. Valsan, Vincent Brillault, Ioan Cristian Schuszter
Computer security threats have always been a major concern and continue to increase in frequency and complexity. The nature and techniques of the attacks evolve rapidly over time, making their detection more difficult. Therefore the means and tools used to deal with them need to evolve at the same pace if not faster. In this paper the implementation of an Intrusion Detection System (IDS) both at the Network (NIDS) and Host (HIDS) level, used at CERN, is presented. The system is currently processing in real time approximately one TB of data per day, with the final goal of coping with at least 5 TB / day. In order to accomplish this goal at first an infrastructure to collect data from sources such as system logs, web server logs and the NIDS logs has been developed making use of technologies such as Apache Flume and Apache Kafka. Once the data is collected it needs to be processed in search of malicious activity: the data is consumed by Apache Spark jobs which compare in real time this data with known signatures of malicious activities. These are known as Indicators of Compromise (IoC). They are published by many security experts and centralized in a local Malware Information Sharing Platform (MISP) instance. Nonetheless, detecting an intrusion is not enough. There is a need to understand what happened and why. In order to gain knowledge on the context of the detected intrusion the data is also enriched in real time when it is passing through the pipeline. For example, DNS resolution and IP geolocation are applied to it. A system generic enough to process any kind of data in JSON format is enriching the data in order to get additional context of what is happening and finally looking for indicators of compromise to detect possible intrusions, making use of the latest technologies in the Big Data ecosystem.
{"title":"Building a large scale Intrusion Detection System using Big Data technologies","authors":"Pablo Panero, L. Valsan, Vincent Brillault, Ioan Cristian Schuszter","doi":"10.22323/1.327.0014","DOIUrl":"https://doi.org/10.22323/1.327.0014","url":null,"abstract":"Computer security threats have always been a major concern and continue to increase in frequency and complexity. The nature and techniques of the attacks evolve rapidly over time, making their detection more difficult. Therefore the means and tools used to deal with them need to evolve at the same pace if not faster. \u0000In this paper the implementation of an Intrusion Detection System (IDS) both at the Network (NIDS) and Host (HIDS) level, used at CERN, is presented. The system is currently processing in real time approximately one TB of data per day, with the final goal of coping with at least 5 TB / day. In order to accomplish this goal at first an infrastructure to collect data from sources such as system logs, web server logs and the NIDS logs has been developed making use of technologies such as Apache Flume and Apache Kafka. Once the data is collected it needs to be processed in search of malicious activity: the data is consumed by Apache Spark jobs which compare in real time this data with known signatures of malicious activities. These are known as Indicators of Compromise (IoC). They are published by many security experts and centralized in a local Malware Information Sharing Platform (MISP) instance. \u0000Nonetheless, detecting an intrusion is not enough. There is a need to understand what happened and why. In order to gain knowledge on the context of the detected intrusion the data is also enriched in real time when it is passing through the pipeline. For example, DNS resolution and IP geolocation are applied to it. A system generic enough to process any kind of data in JSON format is enriching the data in order to get additional context of what is happening and finally looking for indicators of compromise to detect possible intrusions, making use of the latest technologies in the Big Data ecosystem.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134532670","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}
M. Mariotti, L. Storchi, D. Spiga, G. Vitillaro, M. Tracolli, D. Ciangottini, Manuel Ciangottini, V. Formato, M. Duranti, M. Mergé, P. D'Angeli, R. Primavera, Antonio Guerra, L. Fanò, B. Bertucci
Harvesting dispersed computational resources is nowadays an important and strategic topic especially in an environment, like the computational science one, where computing needs constantly increase. On the other hand managing dispersed resources might not be neither an easy task not costly effective. We successfully explored the use of OpenStack middleware to achieve this objective, our man goal is not only the resource harvesting but also to provide a modern paradigm of computing and data usage access. In the present work we will illustrate a real example on how to build a geographically distributed cloud to share and manage computing and storage resources, owned by heterogeneous cooperating entities
{"title":"Harvesting dispersed computational resources with Openstack: a Cloud infrastructure for the Computational Science community","authors":"M. Mariotti, L. Storchi, D. Spiga, G. Vitillaro, M. Tracolli, D. Ciangottini, Manuel Ciangottini, V. Formato, M. Duranti, M. Mergé, P. D'Angeli, R. Primavera, Antonio Guerra, L. Fanò, B. Bertucci","doi":"10.22323/1.327.0009","DOIUrl":"https://doi.org/10.22323/1.327.0009","url":null,"abstract":"Harvesting dispersed computational resources is nowadays an important and strategic topic especially in an environment, like the computational science one, where computing needs constantly increase. On the other hand managing dispersed resources might not be neither an easy task not costly effective. We successfully explored the use of OpenStack middleware to achieve this objective, our man goal is not only the resource harvesting but also to provide a modern paradigm of computing and data usage access. In the present work we will illustrate a real example on how to build a geographically distributed cloud to share and manage computing and storage resources, owned by heterogeneous cooperating entities","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854431","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}
T. Kishimoto, T. Mashimo, N. Matsui, Tomoaki Nakamura, H. Sakamoto
The Tokyo Tier-2 center, which is located in the International Center for Elementary Particle Physics at the University of Tokyo, is providing computer resources for the ATLAS experiment in the Worldwide LHC Computing Grid. Logs produced by the Grid services provide useful information to determine whether the services are working properly. Therefore, a new real-time monitoring system based on log analysis has been constructed using the ELK stack. This paper reports the configuration of the new monitoring system at the Tokyo Tier-2 center, and discusses improvements in terms of stability and flexibility of the site operation by introducing the new monitoring system.
{"title":"Construction of real-time monitoring system for Grid services based on log analysis at the Tokyo Tier-2 center","authors":"T. Kishimoto, T. Mashimo, N. Matsui, Tomoaki Nakamura, H. Sakamoto","doi":"10.22323/1.327.0019","DOIUrl":"https://doi.org/10.22323/1.327.0019","url":null,"abstract":"The Tokyo Tier-2 center, which is located in the International Center for Elementary Particle Physics at the University of Tokyo, is providing computer resources for the ATLAS experiment in the Worldwide LHC Computing Grid. Logs produced by the Grid services provide useful information to determine whether the services are working properly. Therefore, a new real-time monitoring system based on log analysis has been constructed using the ELK stack. This paper reports the configuration of the new monitoring system at the Tokyo Tier-2 center, and discusses improvements in terms of stability and flexibility of the site operation by introducing the new monitoring system.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127021569","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}
D. Bonacorsi, V. Kuznetsov, L. Giommi, T. Diotalevi, J. Vlimant, D. Abercrombie, C. Contreras, A. Repecka, Ž. Matonis, K. Kančys
Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase.
机器和深度学习技术被用于大型强子对撞机CMS操作的各个领域，如数据采集、监测、处理和物理分析。回顾几个选定的用例，重点关注CMS软件和计算，展示了该领域的进展，重点介绍了最新的发展，以及对LHC Run III和高亮度LHC阶段未来应用的展望。
{"title":"Progress on Machine and Deep Learning applications in CMS Computing","authors":"D. Bonacorsi, V. Kuznetsov, L. Giommi, T. Diotalevi, J. Vlimant, D. Abercrombie, C. Contreras, A. Repecka, Ž. Matonis, K. Kančys","doi":"10.22323/1.327.0022","DOIUrl":"https://doi.org/10.22323/1.327.0022","url":null,"abstract":"Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116829516","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}
D. Crooks, L. Valsan, Kashif Mohammad, M. Cărăbaş, S. McKee, J. Trinder
The modern security landscape affecting Grid and Cloud sites is evolving to include possible threats from a range of avenues, including social engineering as well as more direct approaches. An effective strategy to defend against these risks must include cooperation between security teams in different contexts. It is essential that sites have the ability to share threat intelligence data with confidence, as well as being able to act on this data in a timely and effective manner. As reported at ISGC 2017, the Worldwide LHC Computing Grid (WLCG) Security Operations Centres Working Group (WG) has been working with sites across the WLCG to develop a model for a Security Operations Centre reference design. This work includes not only the technical aspect of developing a security stack appropriate for sites of different sizes and topologies, but also the more social aspect of sharing data between groups of different kinds. In particular, since many Grid and Cloud sites operate as part of larger University or other Facility networks, collaboration between Grid and Campus / Facility security teams is an important aspect of maintaining overall security. We discuss recent work on sharing threat intelligence, particularly involving the WLCG MISP instance hosted at CERN. In addition, we examine strategies for the use of this intelligence, as well as considering recent progress in the deployment and integration of the Bro Intrusion Detection System (IDS) at contributing sites. An important part of this work is a report on the first WLCG SOC WG Workshop / Hackathon, a Workshop planned at time of writing for December 2017. This Workshop provides an opportunity to assist participating sites in the deployment of these security tools as well as giving attendees the opportunity to share experiences and consider site policies as a result. This Workshop is hoped to play a substantial role in shaping the future goals of the working group, as well as shaping future workshops.
{"title":"Harnessing the Power of Threat Intelligence in Grids and Clouds: WLCG SOC Working Group","authors":"D. Crooks, L. Valsan, Kashif Mohammad, M. Cărăbaş, S. McKee, J. Trinder","doi":"10.22323/1.327.0012","DOIUrl":"https://doi.org/10.22323/1.327.0012","url":null,"abstract":"The modern security landscape affecting Grid and Cloud sites is evolving to include possible threats from a range of avenues, including social engineering as well as more direct approaches. An effective strategy to defend against these risks must include cooperation between security teams in different contexts. It is essential that sites have the ability to share threat intelligence data with confidence, as well as being able to act on this data in a timely and effective manner. \u0000 \u0000As reported at ISGC 2017, the Worldwide LHC Computing Grid (WLCG) Security Operations Centres Working Group (WG) has been working with sites across the WLCG to develop a model for a Security Operations Centre reference design. This work includes not only the technical aspect of developing a security stack appropriate for sites of different sizes and topologies, but also the more social aspect of sharing data between groups of different kinds. In particular, since many Grid and Cloud sites operate as part of larger University or other Facility networks, collaboration between Grid and Campus / Facility security teams is an important aspect of maintaining overall security. \u0000 \u0000We discuss recent work on sharing threat intelligence, particularly involving the WLCG MISP instance hosted at CERN. In addition, we examine strategies for the use of this intelligence, as well as considering recent progress in the deployment and integration of the Bro Intrusion Detection System (IDS) at contributing sites. \u0000 \u0000An important part of this work is a report on the first WLCG SOC WG Workshop / Hackathon, a Workshop planned at time of writing for December 2017. This Workshop provides an opportunity to assist participating sites in the deployment of these security tools as well as giving attendees the opportunity to share experiences and consider site policies as a result. This Workshop is hoped to play a substantial role in shaping the future goals of the working group, as well as shaping future workshops.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134458617","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}
It has been over a decade since the HEP community initially started to explore the possibility of using the massively available Volunteer Computing resource for its computation. The first project LHC@home was only trying to run a platform portable FORTRAN program for the SixTrack application in the BOINC traditional way. With the development and advancement of a few key technologies such as virtualization and the BOINC middleware which is commonly used to harness the volunteer computers, it not only became possible to run the platform heavily dependent HEP software on the heterogeneous volunteer computers, but also yielded very good performance from the utilization. With the technology advancements and the potential of harvesting a large amount of free computing resource to fill the gap between the increasing computing requirements and the flat available resources, more and more HEP experiments endeavor to integrate the Volunteer Computing resource into their Grid Computing systems based on which the workflows were designed. Resource integration and credential are the two common challenges for this endeavor. In order to address this, each experiment comes out with their own solutions, among which some are lightweight and put into production very soon while the others require heavier adaptation and implementation of the gateway services due to the complexity of their Grid Computing platforms and workflow design. Among all the efforts, the ATLAS experiment is the most successful example by harnessing several tens of millions of CPU hours from its Volunteer Computing project ATLAS@home each year. In this paper, we will retrospect the key phases of exploring Volunteer Computing in HEP, and compare and discuss the different solutions that experiments coming out to harness and integrate the Volunteer Computing resource, finally based on the production experience and successful outcomes, we envision the future challenges in order to sustain, expand and more efficiently utilize the Volunteer Computing resource. Furthermore, we envision common efforts to be put together in order to address all these current and future challenges and to achieve a full exploitation of Volunteer Computing resource for the whole HEP computing community.
{"title":"Explore the massive Volunteer Computing resources for HEP computation","authors":"Wenjing Wu, D. Cameron","doi":"10.22323/1.327.0027","DOIUrl":"https://doi.org/10.22323/1.327.0027","url":null,"abstract":"It has been over a decade since the HEP community initially started to explore the possibility of using the massively available Volunteer Computing resource for its computation. The first project LHC@home was only trying to run a platform portable FORTRAN program for the SixTrack application in the BOINC traditional way. With the development and advancement of a few key technologies such as virtualization and the BOINC middleware which is commonly used to harness the volunteer computers, it not only became possible to run the platform heavily dependent HEP software on the heterogeneous volunteer computers, but also yielded very good performance from the utilization. With the technology advancements and the potential of harvesting a large amount of free computing resource to fill the gap between the increasing computing requirements and the flat available resources, more and more HEP experiments endeavor to integrate the Volunteer Computing resource into their Grid Computing systems based on which the workflows were designed. Resource integration and credential are the two common challenges for this endeavor. In order to address this, each experiment comes out with their own solutions, among which some are lightweight and put into production very soon while the others require heavier adaptation and implementation of the gateway services due to the complexity of their Grid Computing platforms and workflow design. Among all the efforts, the ATLAS experiment is the most successful example by harnessing several tens of millions of CPU hours from its Volunteer Computing project ATLAS@home each year. In this paper, we will retrospect the key phases of exploring Volunteer Computing in HEP, and compare and discuss the different solutions that experiments coming out to harness and integrate the Volunteer Computing resource, finally based on the production experience and successful outcomes, we envision the future challenges in order to sustain, expand and more efficiently utilize the Volunteer Computing resource. Furthermore, we envision common efforts to be put together in order to address all these current and future challenges and to achieve a full exploitation of Volunteer Computing resource for the whole HEP computing community.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134037825","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}
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